From f239c6d6969d5964b50b5a44fa71e19ad7924d9e Mon Sep 17 00:00:00 2001 From: Lucian Smith Date: Wed, 24 May 2023 13:43:20 -0700 Subject: [PATCH 01/16] Use latest KiSAO terms. Also, sort the list so that future diffs are easier to figure out how they changed, and note where the CSV file came from in the python script. --- src/kisao/KISAO.csv | 382 +++++++++------ src/kisao/transform_kisao.py | 12 +- src/sedml/kisaomap.cpp | 915 ++++++++++++++++++----------------- 3 files changed, 717 insertions(+), 592 deletions(-) diff --git a/src/kisao/KISAO.csv b/src/kisao/KISAO.csv index fb8da4cf..8450ba7e 100644 --- a/src/kisao/KISAO.csv +++ b/src/kisao/KISAO.csv @@ -1,38 +1,41 @@ Class ID,Preferred Label,Synonyms,Definitions,Obsolete,CUI,Semantic Types,Parents,has characteristic,has parameter,has Runge-Kutta method order,has type,http://data.bioontology.org/metadata/prefixIRI,http://protege.stanford.edu/plugins/owl/protege#defaultLanguage,http://purl.org/dc/terms/created,http://purl.org/dc/terms/creator,http://purl.org/dc/terms/rights,http://www.biomodels.net/kisao/KISAO#isImplementedIn,http://www.biomodels.net/kisao/KISAO#isOrganizational,http://www.w3.org/2004/02/skos/core#altLabel,http://www.w3.org/2004/02/skos/core#definition,is characteristic of,is generalization of,is hybrid of,is parameter of,is similar to,is used by,uses http://www.biomodels.net/kisao/KISAO#KISAO_0000654,amount rate,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000834,,,,,kisao:KISAO_0000654,,2021-06-04,JRK,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000386,scaled preconditioned generalized minimal residual method,SPGMR,"A scaled preconditioned version of 'generalized minimal residual algorithm' [http://identifiers.org/biomodels.kisao/KISAO_0000353]. For linear system Ax = b a preconditioner matrix P that approximates A is sought, for which linear system Px = b can be solved easily. Preconditioning is applied on the left only. Scaling is done using diagonal matrix D whose diagonal elements are weights w^i = rtol|y^i| +atol^i, where rtol is 'relative tolerance' [http://identifiers.org/biomodels.kisao/KISAO_0000209] and atol is 'absolute tolerance' [http://identifiers.org/biomodels.kisao/KISAO_0000211].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000353,,,,,kisao:KISAO_0000386,,2011-07-18,AZ,,,,SPGMR,"A scaled preconditioned version of 'generalized minimal residual algorithm' [http://identifiers.org/biomodels.kisao/KISAO_0000353]. For linear system Ax = b a preconditioner matrix P that approximates A is sought, for which linear system Px = b can be solved easily. Preconditioning is applied on the left only. Scaling is done using diagonal matrix D whose diagonal elements are weights w^i = rtol|y^i| +atol^i, where rtol is 'relative tolerance' [http://identifiers.org/biomodels.kisao/KISAO_0000209] and atol is 'absolute tolerance' [http://identifiers.org/biomodels.kisao/KISAO_0000211].",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000810,Reduced eigenvalue matrix,,"The reduced eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000810,,06-03-2021,LPS,,,,,"The reduced eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000621,stochastic simulation leaping method,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000621,,2021-04-27,JRK,,,true,,,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000808,Reduced stoichiometry matrix,,The reduced stoichiometry matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000808,,06-03-2021,LPS,,,,,The reduced stoichiometry matrix. The dimensions are species by reactions.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000852,count ignoring NaN,,"The number of non-zero elements of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000852,,2021-10-09,MK,,,,,"The number of non-zero elements of a set of values, ignoring Nan entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000404,symmetricity of matrix,,"In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000370,,,,,kisao:KISAO_0000404,,2011-07-19,AZ,,,,,"In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000539,minimum factor to change step size by,fac1|dfactor,Minimum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000539,,2020-10-29,JRK,,Odespy|https://identifiers.org/biosimulators/gillespy2|JModelica|SciPy,,fac1|dfactor,Minimum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000506,genetic algorithm,GA,"The genetic algorithm (GA) is a computational technique that mimics evolution and is based on reproduction and selection. A GA is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the GA, each individual is paired with one other at random for reproduction. Two offspring are produced by combining their genomes and allowing for ""cross-over"", i.e., the two new individuals have genomes that are formed from a combination of the genomes of their parents. Also each new gene might have mutated, i.e. the parameter value might have changed slightly. At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000520,,,,,kisao:KISAO_0000506,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,GA,"The genetic algorithm (GA) is a computational technique that mimics evolution and is based on reproduction and selection. A GA is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the GA, each individual is paired with one other at random for reproduction. Two offspring are produced by combining their genomes and allowing for ""cross-over"", i.e., the two new individuals have genomes that are formed from a combination of the genomes of their parents. Also each new gene might have mutated, i.e. the parameter value might have changed slightly. At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000539,minimum factor to change step size by,fac1|dfactor,Minimum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000539,,2020-10-29,JRK,,Odespy|JModelica|http://identifiers.org/biosimulators/gillespy2|SciPy,,fac1|dfactor,Minimum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000506,genetic algorithm,GA,"The genetic algorithm (GA) is a computational technique that mimics evolution and is based on reproduction and selection. A GA is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the GA, each individual is paired with one other at random for reproduction. Two offspring are produced by combining their genomes and allowing for ""cross-over"", i.e., the two new individuals have genomes that are formed from a combination of the genomes of their parents. Also each new gene might have mutated, i.e. the parameter value might have changed slightly. At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000520,,,,,kisao:KISAO_0000506,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,GA,"The genetic algorithm (GA) is a computational technique that mimics evolution and is based on reproduction and selection. A GA is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the GA, each individual is paired with one other at random for reproduction. Two offspring are produced by combining their genomes and allowing for ""cross-over"", i.e., the two new individuals have genomes that are formed from a combination of the genomes of their parents. Also each new gene might have mutated, i.e. the parameter value might have changed slightly. At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000219,maximum Adams order,Adams max order|maximum non-stiff order,This parameter is a positive integer value specifying the maximal order the non-stiff Adams integration method [http://identifiers.org/biomodels.kisao/KISAO_0000289] shall attempt before switching to the stiff BDF method [http://identifiers.org/biomodels.kisao/KISAO_0000288].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000484,,,,,kisao:KISAO_0000219,,,,,,,Adams max order|maximum non-stiff order,This parameter is a positive integer value specifying the maximal order the non-stiff Adams integration method [http://identifiers.org/biomodels.kisao/KISAO_0000289] shall attempt before switching to the stiff BDF method [http://identifiers.org/biomodels.kisao/KISAO_0000288].,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000667,memory size,,"Maximum number of points to store in memory, such as in the second order backward implicit product Euler scheme.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000667,,2021-08-07,JRK,,http://identifiers.org/biosimulators/xpp,,,"Maximum number of points to store in memory, such as in the second order backward implicit product Euler scheme.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000099,type of system behaviour,,"A characteristic describing the rules the algorithm uses to simulate the temporal evolution of a system, specifically whether or not the final state is uniquely determined from a precise initial state.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000099,,,AZ,,,true,,"A characteristic describing the rules the algorithm uses to simulate the temporal evolution of a system, specifically whether or not the final state is uniquely determined from a precise initial state.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000693,biochemical system,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000690,,,,,kisao:KISAO_0000693,,2022-03-29,EN|JRK|WL,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000543,stability limit detection flag,,Flag to activate stability limit detection.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243|http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000543,,2020-10-29,JRK,,SUNDIALS,,,Flag to activate stability limit detection.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000510,truncated Newton,,The Truncated Newton method is a sophisticated variant of the Newton optimization method. The Newton optimization method searches for the minimum of a nonlinear function by following descent directions determined from the function's first and second partial derivatives. The Truncated Newton method does an incomplete (truncated) solution of a system of linear equations to calculate the Newton direction. This means that the actual direction chosen for the descent is between the steepest descent direction and the true Newton direction.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000510,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,,The Truncated Newton method is a sophisticated variant of the Newton optimization method. The Newton optimization method searches for the minimum of a nonlinear function by following descent directions determined from the function's first and second partial derivatives. The Truncated Newton method does an incomplete (truncated) solution of a system of linear equations to calculate the Newton direction. This means that the actual direction chosen for the descent is between the steepest descent direction and the true Newton direction.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000510,truncated Newton,,The Truncated Newton method is a sophisticated variant of the Newton optimization method. The Newton optimization method searches for the minimum of a nonlinear function by following descent directions determined from the function's first and second partial derivatives. The Truncated Newton method does an incomplete (truncated) solution of a system of linear equations to calculate the Newton direction. This means that the actual direction chosen for the descent is between the steepest descent direction and the true Newton direction.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000510,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,,The Truncated Newton method is a sophisticated variant of the Newton optimization method. The Newton optimization method searches for the minimum of a nonlinear function by following descent directions determined from the function's first and second partial derivatives. The Truncated Newton method does an incomplete (truncated) solution of a system of linear equations to calculate the Newton direction. This means that the actual direction chosen for the descent is between the steepest descent direction and the true Newton direction.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000358,biconjugate gradient method,BiCG|BCG|Bi-CG,The biconjugate gradient method provides a generalization of conjugate gradient method [http://identifiers.org/biomodels.kisao/KISAO_0000357] to non-symmetric matrices.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000354,,,,,kisao:KISAO_0000358,,2011-06-10,AZ,,,,BiCG|BCG|Bi-CG,The biconjugate gradient method provides a generalization of conjugate gradient method [http://identifiers.org/biomodels.kisao/KISAO_0000357] to non-symmetric matrices.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000325,minimum fast/discrete reaction occurrences number,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which describes the minimum number of fast/discrete reaction occurrences before their effects cause convergence to a quasi-steady-state distribution.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000325,,2011-06-02,AZ,,,,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which describes the minimum number of fast/discrete reaction occurrences before their effects cause convergence to a quasi-steady-state distribution.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000421,type of validation,,Parameter of 'partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000416] describing how validation is performed. Possible values include cross-validation and test set validation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000421,,2012-01-18,AZ,,,,,Parameter of 'partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000416] describing how validation is performed. Possible values include cross-validation and test set validation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000824,aggregation function,,"A function that aggregates a set of results, reducing its dimension(s). Examples include functions that compute minima or maxima of sets of values.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000633,,,,,kisao:KISAO_0000824,,06-03-2021,LPS,,,true,,"A function that aggregates a set of results, reducing its dimension(s). Examples include functions that compute minima or maxima of sets of values.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000556,relative quadrature tolerance,,Relative error tolerance of the adjoint solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000556,,2020-10-29,JRK,,https://identifiers.org/biosimulators/amici|SUNDIALS,,,Relative error tolerance of the adjoint solution.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000589,ACB flux sampling method,Artificial centering boundary flux sampling method,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000588,,,,,kisao:KISAO_0000589,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cobratoolbox,,Artificial centering boundary flux sampling method,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000556,relative quadrature tolerance,,Relative error tolerance of the adjoint solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000556,,2020-10-29,JRK,,http://identifiers.org/biosimulators/amici|SUNDIALS,,,Relative error tolerance of the adjoint solution.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000589,ACB flux sampling method,artificial centering boundary flux sampling method|Artificial centering boundary flux sampling method,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000588,,,,,kisao:KISAO_0000589,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cobratoolbox,,artificial centering boundary flux sampling method|Artificial centering boundary flux sampling method,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000523,cooling factor,,"Rate by which the temperature is reduced from one cycle to the next, given by the formula: Tnew=Told*""Cooling Factor"". The simulated annealing algorithm works best if the temperature is reduced at a slow rate, so this value should be close to 1.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000521,,,,,kisao:KISAO_0000523,,2019-01-18,AZ,,,,,"Rate by which the temperature is reduced from one cycle to the next, given by the formula: Tnew=Told*""Cooling Factor"". The simulated annealing algorithm works best if the temperature is reduced at a slow rate, so this value should be close to 1.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000453,ordered updating policy,,An updating policy that chooses a transition in a definite way.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000451,,,,,kisao:KISAO_0000453,,2013-01-28,AZ,,,,,An updating policy that chooses a transition in a definite way.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000671,use stiff method,,Specifies whether the integrator attempts to solve stiff equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000671,,2021-08-07,JRK,,http://identifiers.org/biosimulators/tellurium,,,Specifies whether the integrator attempts to solve stiff equations.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000428,matrix for clusterization,,A matrix to do the clustering in 'hierarchical cluster-based partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000417].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000429,,,,,kisao:KISAO_0000428,,2012-01-18,AZ,,,,,A matrix to do the clustering in 'hierarchical cluster-based partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000417].,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000497,KLU,"""Clark Kent"" LU factorization algorithm","KLU is a software package and an algorithm for solving sparse unsymmetric linear systems of equations that arise in circuit simulation applications. It relies on a permutation to Block Triangular Form (BTF), several methods for finding a fill-reducing ordering (variants of approximate minimum degree and nested dissection), and Gilbert/Peierls’ sparse left-looking LU factorization algorithm to factorize each block. The package is written in C and includes a MATLAB interface.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000497,,2017-01-09,AZ,,,,"""Clark Kent"" LU factorization algorithm","KLU is a software package and an algorithm for solving sparse unsymmetric linear systems of equations that arise in circuit simulation applications. It relies on a permutation to Block Triangular Form (BTF), several methods for finding a fill-reducing ordering (variants of approximate minimum degree and nested dissection), and Gilbert/Peierls’ sparse left-looking LU factorization algorithm to factorize each block. The package is written in C and includes a MATLAB interface.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000338,h-version of the finite element method,h-method|h-FEM,"Classical form of the 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337], in which polynomials of fixed degree p are used and the mesh is refined to increase accuracy. Can be considered as a special case of the h-p version [http://identifiers.org/biomodels.kisao/KISAO_0000340].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000337,,,,,kisao:KISAO_0000338,,2011-06-07,AZ,,,,h-method|h-FEM,"Classical form of the 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337], in which polynomials of fixed degree p are used and the mesh is refined to increase accuracy. Can be considered as a special case of the h-p version [http://identifiers.org/biomodels.kisao/KISAO_0000340].",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000639,flux,,A rate through an volume such as of a reaction of a constraint-based models.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000639,,2021-06-04,JRK,,,,,A rate through an volume such as of a reaction of a constraint-based models.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000108,progression with fixed time step,,"Algorithm, possessing this characteristic, uses time steps of constant length to update the state of a system during the whole simulation.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000100,,,,,kisao:KISAO_0000108,,2008-07-08,NLN,,,,,"Algorithm, possessing this characteristic, uses time steps of constant length to update the state of a system during the whole simulation.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000606,Hierarchical Stochastic Simulation Algorithm,hSSA,"Fast, memory-efficient method for stochastic simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000241,,,,,kisao:KISAO_0000606,,2021-01-25,JRK,,https://identifiers.org/biosimulators/ibiosim,,hSSA,"Fast, memory-efficient method for stochastic simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000560,LSODA/LSODAR hybrid method,,Automatically use LSODA or LSODAR as apropriate for the given problem. Use LSODA if the problem has no roots. Use LSODAR if the problem has roots.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000560,,2020-10-29,JRK,,https://identifiers.org/biosimulators/copasi,,,Automatically use LSODA or LSODAR as apropriate for the given problem. Use LSODA if the problem has no roots. Use LSODAR if the problem has roots.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000606,hierarchical stochastic simulation algorithm,hSSA,"Fast, memory-efficient method for stochastic simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000241,,,,,kisao:KISAO_0000606,,2021-01-25,JRK,,http://identifiers.org/biosimulators/ibiosim,,hSSA,"Fast, memory-efficient method for stochastic simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000560,LSODA/LSODAR hybrid method,,Automatically use LSODA or LSODAR as apropriate for the given problem. Use LSODA if the problem has no roots. Use LSODAR if the problem has roots.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000560,,2020-10-29,JRK,,http://identifiers.org/biosimulators/copasi,,,Automatically use LSODA or LSODAR as apropriate for the given problem. Use LSODA if the problem has no roots. Use LSODAR if the problem has roots.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000837,particle number,,"The extensive quantity particle number, or, the molar amount of the entity multiplied by Avogadro's number.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000837,,06-03-2021,LPS,,,,,"The extensive quantity particle number, or, the molar amount of the entity multiplied by Avogadro's number.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000039,tau-leaping method,tauL,"Approximate acceleration procedure of the Stochastic Simulation Algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] that divides the time into subintervals and ''leaps'' from one to another, firing all the reaction events in each subinterval.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000621,,,,,kisao:KISAO_0000039,,2008-07-08,NLN,,ByoDyn|Cain|SmartCell,,tauL,"Approximate acceleration procedure of the Stochastic Simulation Algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] that divides the time into subintervals and ''leaps'' from one to another, firing all the reaction events in each subinterval.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000039,tau-leaping method,tauL,"Approximate acceleration procedure of the stochastic simulation algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] that divides the time into subintervals and ''leaps'' from one to another, firing all the reaction events in each subinterval.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000621,,,,,kisao:KISAO_0000039,,2008-07-08,NLN,,ByoDyn|Cain|SmartCell,,tauL,"Approximate acceleration procedure of the stochastic simulation algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] that divides the time into subintervals and ''leaps'' from one to another, firing all the reaction events in each subinterval.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000236,exact solution,,"Algorithm, possessing this characteristic, provides an exact solution to the initial problem.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000235,,,,,kisao:KISAO_0000236,,,,,,,,"Algorithm, possessing this characteristic, provides an exact solution to the initial problem.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000592,dynamic rFBA,regulatory flux balance analysis|dynamic regulatory flux balance analysis|rFBA,Method for predicting the dynamics of metabolic fluxes under patterns of the regulation of gene expression,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000595,,,,,kisao:KISAO_0000592,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cobratoolbox,,regulatory flux balance analysis|dynamic regulatory flux balance analysis|rFBA,Method for predicting the dynamics of metabolic fluxes under patterns of the regulation of gene expression,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000592,dynamic rFBA,regulatory flux balance analysis|dynamic regulatory flux balance analysis|rFBA,Method for predicting the dynamics of metabolic fluxes under patterns of the regulation of gene expression,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000595,,,,,kisao:KISAO_0000592,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cobratoolbox,,regulatory flux balance analysis|dynamic regulatory flux balance analysis|rFBA,Method for predicting the dynamics of metabolic fluxes under patterns of the regulation of gene expression,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000432,IDA-like method,,"Solves real differential-algebraic systems in N-space, in the general form F(t,y,y')=0, y(t0)=y0, y'(t0)=y'0. At each step, a Newton iteration [http://identifiers.org/biomodels.kisao/KISAO_0000408] leads to linear systems Jx=b, which are solved by one of five methods - two direct (dense or band; serial version only) and three Krylov [http://identifiers.org/biomodels.kisao/KISAO_0000354] (GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000353], BiCGStab [http://identifiers.org/biomodels.kisao/KISAO_0000392], or TFQMR [http://identifiers.org/biomodels.kisao/KISAO_0000396]).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000432,,2012-05-24,AZ,,,true,,"Solves real differential-algebraic systems in N-space, in the general form F(t,y,y')=0, y(t0)=y0, y'(t0)=y'0. At each step, a Newton iteration [http://identifiers.org/biomodels.kisao/KISAO_0000408] leads to linear systems Jx=b, which are solved by one of five methods - two direct (dense or band; serial version only) and three Krylov [http://identifiers.org/biomodels.kisao/KISAO_0000354] (GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000353], BiCGStab [http://identifiers.org/biomodels.kisao/KISAO_0000392], or TFQMR [http://identifiers.org/biomodels.kisao/KISAO_0000396]).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000203,particle number lower limit,,This parameter of 'Pahle hybrid method' [http://identifiers.org/biomodels.kisao/KISAO_0000231] is a double value specifying the lower limit for particle numbers. Species with a particle number below this value are considered as having a low particle number. The 'particle number lower limit' cannot be higher than the 'particle number upper limit' [http://identifiers.org/biomodels.kisao/KISAO_0000204].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000203,,,,,,,,This parameter of 'Pahle hybrid method' [http://identifiers.org/biomodels.kisao/KISAO_0000231] is a double value specifying the lower limit for particle numbers. Species with a particle number below this value are considered as having a low particle number. The 'particle number lower limit' cannot be higher than the 'particle number upper limit' [http://identifiers.org/biomodels.kisao/KISAO_0000204].,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000517,number of generations,,The parameter is a positive integer value to determine the number of generations the evolutionary algorithm shall evolve the population.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000518,,,,,kisao:KISAO_0000517,,2019-01-18,AZ,,,,,The parameter is a positive integer value to determine the number of generations the evolutionary algorithm shall evolve the population.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000665,maximum number of iterations for root finding,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000665,,2021-08-07,JRK,,http://identifiers.org/biosimulators/xpp,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000319,Monte Carlo method,MC,Monte Carlo methods (or Monte Carlo experiments) are a class of computational algorithms that rely on repeated random sampling to compute their results.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000319,,2011-05-26,AZ,,,true,MC,Monte Carlo methods (or Monte Carlo experiments) are a class of computational algorithms that rely on repeated random sampling to compute their results.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000632,functional iteration root-finding method,,"Iterative method for finding the root of a function f(x) given by @@ -46,58 +49,67 @@ This method only involves evaluations of f. This method is suitable for non-stif http://www.biomodels.net/kisao/KISAO#KISAO_0000064,Runge-Kutta based method,modified Euler method,"A method of numerically integrating ordinary differential equations, which uses a sampling of slopes through an interval and takes a weighted average to determine the right end point. This averaging gives a very accurate approximation.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000377,,,,,kisao:KISAO_0000064,,2007-11-12,dk,,ByoDyn,true,modified Euler method,"A method of numerically integrating ordinary differential equations, which uses a sampling of slopes through an interval and takes a weighted average to determine the right end point. This averaging gives a very accurate approximation.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000097,modelling and simulation algorithm characteristic,modeling and simulation algorithm characteristic,"Simulation algorithm property, which can, for example, describe the model, such as the type of variables (discrete or continuous), and information on the treatment of spatial descriptions, or can be a numerical characteristic, such as the system's behaviour (deterministic or stochastic) as well as the progression mechanism (fixed or adaptive time steps).",false,,,http://www.w3.org/2002/07/owl#Thing,,,,,kisao:KISAO_0000097,,,AZ,,,true,modeling and simulation algorithm characteristic,"Simulation algorithm property, which can, for example, describe the model, such as the type of variables (discrete or continuous), and information on the treatment of spatial descriptions, or can be a numerical characteristic, such as the system's behaviour (deterministic or stochastic) as well as the progression mechanism (fixed or adaptive time steps).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000415,maximum number of steps,maximum steps,The limit on number of internal steps before an output point.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000415,,2012-01-18,AZ,,,,maximum steps,The limit on number of internal steps before an output point.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000448,logical model simulation method,,Qualitative (logical) models specify the evolution rules of their components. In each state a number of transitions are enabled. A 'logical model simulation method' guides the choice of the transitions processed at each step.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000448,,2013-01-28,AZ,,,,,Qualitative (logical) models specify the evolution rules of their components. In each state a number of transitions are enabled. A 'logical model simulation method' guides the choice of the transitions processed at each step.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000448,logical model simulation method,,Qualitative (logical) models specify the evolution rules of their components. In each state a number of transitions are enabled. A 'logical model simulation method' guides the choice of the transitions processed at each step.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000448,,2013-01-28,AZ,,,true,,Qualitative (logical) models specify the evolution rules of their components. In each state a number of transitions are enabled. A 'logical model simulation method' guides the choice of the transitions processed at each step.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000521,simulated annealing parameter,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000521,,2019-01-18,AZ,,,true,,,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000554,parsimonius flux balance analysis (minimum number of active fluxes),parsimonious FBA|parsimonious flux balance analysis|pFBA,A technique for selecting a parsimonious flux distribution which has a minimal number of active fluxes.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000620,,,,,kisao:KISAO_0000554,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cbmpy,,parsimonious FBA|parsimonious flux balance analysis|pFBA,A technique for selecting a parsimonious flux distribution which has a minimal number of active fluxes.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000528,parsimonious enzyme usage flux balance analysis (minimum sum of absolute fluxes),parsimonious FBA|parsimonious flux balance analysis|pFBA,Method for determining the smallest flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000620,,,,,kisao:KISAO_0000528,,2020-08-11,AZ,,https://identifiers.org/biosimulators/cobrapy|https://identifiers.org/biosimulators/cbmpy,,parsimonious FBA|parsimonious flux balance analysis|pFBA,Method for determining the smallest flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000587,IMEX,Implicit-Explicit Runge-Kutta method,Method for solving stiff and imaginary ODE problems,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000587,,2020-10-29,JRK,,https://identifiers.org/biosimulators/biouml,,Implicit-Explicit Runge-Kutta method,Method for solving stiff and imaginary ODE problems,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000599,Hybrid Gibson - Euler-Maruyama Method,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000599,,2021-01-13,MLB|JRK,,https://identifiers.org/biosimulators/vcell,,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000554,parsimonius flux balance analysis (minimum number of active fluxes),parsimonious FBA|parsimonious flux balance analysis|pFBA,A technique for selecting a parsimonious flux distribution which has a minimal number of active fluxes.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000620,,,,,kisao:KISAO_0000554,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cbmpy,,parsimonious FBA|parsimonious flux balance analysis|pFBA,A technique for selecting a parsimonious flux distribution which has a minimal number of active fluxes.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000528,parsimonious enzyme usage flux balance analysis (minimum sum of absolute fluxes),parsimonious FBA|parsimonious flux balance analysis|pFBA,Method for determining the smallest flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000620,,,,,kisao:KISAO_0000528,,2020-08-11,AZ,,http://identifiers.org/biosimulators/cbmpy|http://identifiers.org/biosimulators/cobrapy,,parsimonious FBA|parsimonious flux balance analysis|pFBA,Method for determining the smallest flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000587,IMEX,Implicit-Explicit Runge-Kutta method|implicit-explicit Runge-Kutta method,Method for solving stiff and imaginary ODE problems,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000587,,2020-10-29,JRK,,http://identifiers.org/biosimulators/biouml,,Implicit-Explicit Runge-Kutta method|implicit-explicit Runge-Kutta method,Method for solving stiff and imaginary ODE problems,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000802,control coefficient (scaled),,A scaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000802,,06-03-2021,LPS,,,,,A scaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000599,hybrid Gibson - Euler-Maruyama method,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000599,,2021-01-13,MLB|JRK,,http://identifiers.org/biosimulators/vcell,,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000201,modelling and simulation algorithm parameter,modeling and simulation algorithm parameter,Parameter that can be used in the simulation experiment settings.,false,,,http://www.w3.org/2002/07/owl#Thing,,,,,kisao:KISAO_0000201,,,AZ,,,true,modeling and simulation algorithm parameter,Parameter that can be used in the simulation experiment settings.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000234,LSODKR,"Livermore solver for ordinary differential equations, with preconditioned Krylov iteration methods for the Newton correction linear systems, and with root finding.","LSODKR is an initial value ODE solver for stiff and nonstiff systems. It is a variant of the LSODPK [http://identifiers.org/biomodels.kisao/KISAO_0000093] and LSODE [http://identifiers.org/biomodels.kisao/KISAO_0000071] solvers, intended mainly for large stiff systems. The main differences between LSODKR and LSODE [http://identifiers.org/biomodels.kisao/KISAO_0000071] are the following: a) for stiff systems, LSODKR uses a corrector iteration composed of Newton iteration and one of four preconditioned Krylov subspace iteration methods. The user must supply routines for the preconditioning operations, b) within the corrector iteration, LSODKR does automatic switching between functional (fixpoint) iteration and modified Newton iteration, c) LSODKR includes the ability to find roots of given functions of the solution during the integration.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000234,,,,,,,"Livermore solver for ordinary differential equations, with preconditioned Krylov iteration methods for the Newton correction linear systems, and with root finding.","LSODKR is an initial value ODE solver for stiff and nonstiff systems. It is a variant of the LSODPK [http://identifiers.org/biomodels.kisao/KISAO_0000093] and LSODE [http://identifiers.org/biomodels.kisao/KISAO_0000071] solvers, intended mainly for large stiff systems. The main differences between LSODKR and LSODE [http://identifiers.org/biomodels.kisao/KISAO_0000071] are the following: a) for stiff systems, LSODKR uses a corrector iteration composed of Newton iteration and one of four preconditioned Krylov subspace iteration methods. The user must supply routines for the preconditioning operations, b) within the corrector iteration, LSODKR does automatic switching between functional (fixpoint) iteration and modified Newton iteration, c) LSODKR includes the ability to find roots of given functions of the solution during the integration.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000369,partial differential equation discretization method,,"A method which solves partial differential equations by discretizing them, i.e. approximating them by equations that involve a finite number of unknowns.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000369,,2011-06-27,AZ,,,true,,"A method which solves partial differential equations by discretizing them, i.e. approximating them by equations that involve a finite number of unknowns.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000106,continuous variable,,"Algorithm, possessing this characteristic, allows the values of a system's variables to change by continuous (non-integral) amounts.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000098,,,,,kisao:KISAO_0000106,,2008-07-08,NLN,,,,,"Algorithm, possessing this characteristic, allows the values of a system's variables to change by continuous (non-integral) amounts.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000604,MSR Tolerance,Multiple slow reactions tolerance,Maximum allowed effect of executing multiple slow reactions per numerical integration of the SDEs.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000604,,2021-01-13,MLB|JRK,,,,Multiple slow reactions tolerance,Maximum allowed effect of executing multiple slow reactions per numerical integration of the SDEs.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000604,MSR tolerance,multiple slow reactions tolerance|Multiple slow reactions tolerance,Maximum allowed effect of executing multiple slow reactions per numerical integration of the SDEs.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000604,,2021-01-13,MLB|JRK,,,,multiple slow reactions tolerance|Multiple slow reactions tolerance,Maximum allowed effect of executing multiple slow reactions per numerical integration of the SDEs.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000336,D-leaping method,,"We propose a novel, accelerated algorithm for the approximate stochastic simulation of biochemical systems with delays. The present work extends existing accelerated algorithms by distributing, in a time adaptive fashion, the delayed reactions so as to minimize the computational effort while preserving their accuracy.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000336,,2011-06-03,AZ,,,,,"We propose a novel, accelerated algorithm for the approximate stochastic simulation of biochemical systems with delays. The present work extends existing accelerated algorithms by distributing, in a time adaptive fashion, the delayed reactions so as to minimize the computational effort while preserving their accuracy.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000835,Concentration control coefficient matrix (scaled),,The scaled concentration control coefficient matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:KISAO_0000835,,06-03-2021,LPS,,,,,The scaled concentration control coefficient matrix. The dimensions are species by reactions.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000567,force physical correctness,,Indicates whether to force physical correctness.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000567,,2020-10-29,JRK,,https://identifiers.org/biosimulators/copasi,,,Indicates whether to force physical correctness.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000835,concentration control coefficient matrix (scaled),,The scaled concentration control coefficient matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000835,,06-03-2021,LPS,,,,,The scaled concentration control coefficient matrix. The dimensions are species by reactions.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000567,force physical correctness,,Indicates whether to force physical correctness.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000567,,2020-10-29,JRK,,http://identifiers.org/biosimulators/copasi,,,Indicates whether to force physical correctness.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000340,h-p version of the finite element method,hp-FEM|hp-method,In h-p version of 'finite difference method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] the two approaches of mesh refinement and degree enchacement are combined.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000337,,,,,kisao:KISAO_0000340,,2011-06-07,AZ,,,,hp-FEM|hp-method,In h-p version of 'finite difference method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] the two approaches of mesh refinement and degree enchacement are combined.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000017,multi-state agent-based simulation method,Morton-Firth,"The agent-based simulation method instantiates each molecule as an individual software object. The interactions between those objects are determined by interaction probabilities according to experimental data. The probability is depended on the state the molecule is in at that specific time (molecules have multiple-state). Additionally, ''pseudo-molecules'' are introduced to the system in order to simulate unimolecular reactions. For simulation, continuous time is broken down into discrete, independent ''slices''. During each time slice one molecule is selected randomly, a second molecule or pseudo-molecule is selected afterwards (leading to either a unimolecular or a bimolecular reaction). The reaction will only take place if a produced random number exceeds the reaction probability calculated beforehand. In that case, the system is updated after that reaction.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000363,,,,,kisao:KISAO_0000017,,,,,StochSim,,Morton-Firth,"The agent-based simulation method instantiates each molecule as an individual software object. The interactions between those objects are determined by interaction probabilities according to experimental data. The probability is depended on the state the molecule is in at that specific time (molecules have multiple-state). Additionally, ''pseudo-molecules'' are introduced to the system in order to simulate unimolecular reactions. For simulation, continuous time is broken down into discrete, independent ''slices''. During each time slice one molecule is selected randomly, a second molecule or pseudo-molecule is selected afterwards (leading to either a unimolecular or a bimolecular reaction). The reaction will only take place if a produced random number exceeds the reaction probability calculated beforehand. In that case, the system is updated after that reaction.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000373,differential-algebraic equation problem,DAE,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000405,,,,,kisao:KISAO_0000373,,,AZ,,,,DAE,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000617,IDA-CVODE hybrid method,,"Meta algorithm which chooses between IDA and CVODE depending on the problem to be solved. -CVODE is used for ordinary differential equation (ODE) systems. IDA is used for differential-algebraic equation (DAE) systems.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000617,,2021-01-30,JRK,,https://identifiers.org/biosimulators/vcell,,,"Meta algorithm which chooses between IDA and CVODE depending on the problem to be solved. +CVODE is used for ordinary differential equation (ODE) systems. IDA is used for differential-algebraic equation (DAE) systems.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000617,,2021-01-30,JRK,,http://identifiers.org/biosimulators/vcell,,,"Meta algorithm which chooses between IDA and CVODE depending on the problem to be solved. CVODE is used for ordinary differential equation (ODE) systems. IDA is used for differential-algebraic equation (DAE) systems.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000475,integration method,,the integration method used by the solver,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000475,,2015-09-10,AZ,,https://identifiers.org/biosimulators/opencor,,,the integration method used by the solver,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000475,integration method,,the integration method used by the solver,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000475,,2015-09-10,AZ,,http://identifiers.org/biosimulators/opencor,,,the integration method used by the solver,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000643,upper bound,,An upper bound on an estimate of a quantity.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000641,,,,,kisao:KISAO_0000643,,2021-06-04,JRK,,,,,An upper bound on an estimate of a quantity.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000571,absolute tolerance adjustment factor,,How much to adjust the absolute tolerance.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000597,,,,,kisao:KISAO_0000571,,2020-10-29,JRK,,https://identifiers.org/biosimulators/pysces,,,How much to adjust the absolute tolerance.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000571,absolute tolerance adjustment factor,,How much to adjust the absolute tolerance.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000597,,,,,kisao:KISAO_0000571,,2020-10-29,JRK,,http://identifiers.org/biosimulators/pysces,,,How much to adjust the absolute tolerance.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000075,Gillespie multi-particle method,particle-based spatial stochastic method|Gillespie's multi-particle method|GMP,Combination of the multiparticle method for diffusion [http://identifiers.org/biomodels.kisao/KISAO_0000334] and the SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000075,,,,,,,particle-based spatial stochastic method|Gillespie's multi-particle method|GMP,Combination of the multiparticle method for diffusion [http://identifiers.org/biomodels.kisao/KISAO_0000334] and the SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029].,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000829,minimum,,The minimum value of a set of values.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000829,,06-03-2021,LPS,,,,,The minimum value of a set of values.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000829,minimum ignoring NaN,,"The minimum value of a set of values, ignoring NaN entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000829,,2021-06-03,"LPS, MK",,,,,"The minimum value of a set of values, ignoring NaN entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000021,StochSim nearest-neighbour algorithm,,"The nearest-neighbour algorithm allows for the representation of spatial information, by adding a two-dimensional lattice in the form of a probabilistic cellular automata. That way, nearest neighbour interactions do additionally influence reactions taking place in the systems. Reactions between entities are calculated using the agent-based simulation algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000017].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000363,,,,,kisao:KISAO_0000021,,,,,Stochsim 1.2 and more recent versions,,,"The nearest-neighbour algorithm allows for the representation of spatial information, by adding a two-dimensional lattice in the form of a probabilistic cellular automata. That way, nearest neighbour interactions do additionally influence reactions taking place in the systems. Reactions between entities are calculated using the agent-based simulation algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000017].",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000424,mean-centring of variables,,A boolean parameter of the 'hierarchical cluster-based partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000417] specifying whether the variables were mean-centred prior to the regression analysis.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000430,,,,,kisao:KISAO_0000424,,2012-01-18,AZ,,,,,A boolean parameter of the 'hierarchical cluster-based partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000417] specifying whether the variables were mean-centred prior to the regression analysis.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000676,degree of linearity,,The degree of linearity of a system.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000676,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,The degree of linearity of a system.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000469,maximal timestep,,"Key parameter of the 'maximal timestep method' [http://www.biomodels.net/kisao/KISAO#KISAO_0000468]. If Gillespie [http://www.biomodels.net/kisao/KISAO#KISAO_0000027] waiting time is longer than maximal timestep, slow reaction is not fired and tau-leap [http://www.biomodels.net/kisao/KISAO#KISAO_0000039] step is executed for fast reactions. Otherwise, slow reaction is fired and tau-leap [http://www.biomodels.net/kisao/KISAO#KISAO_0000039] is executed with shorter time step.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000469,,2014-04-25,AZ,,,,,"Key parameter of the 'maximal timestep method' [http://www.biomodels.net/kisao/KISAO#KISAO_0000468]. If Gillespie [http://www.biomodels.net/kisao/KISAO#KISAO_0000027] waiting time is longer than maximal timestep, slow reaction is not fired and tau-leap [http://www.biomodels.net/kisao/KISAO#KISAO_0000039] step is executed for fast reactions. Otherwise, slow reaction is fired and tau-leap [http://www.biomodels.net/kisao/KISAO#KISAO_0000039] is executed with shorter time step.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000301,Heun method,Heun's method,The method is named after Karl L. W. M. Heun and is a numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It can be seen as extension of the Euler method [http://identifiers.org/biomodels.kisao/KISAO_0000261] into two-stage second-order Runge-Kutta method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000301,,2011-05-10,AZ,,,,Heun's method,The method is named after Karl L. W. M. Heun and is a numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It can be seen as extension of the Euler method [http://identifiers.org/biomodels.kisao/KISAO_0000261] into two-stage second-order Runge-Kutta method.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000430,variables preprocessing parameter,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000430,,,AZ,,,true,,,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000565,absolute tolerance for root finding,,Absolute error tolerance for root finding.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000211,,,,,kisao:KISAO_0000565,,2020-10-29,JRK,,https://identifiers.org/biosimulators/copasi,,,Absolute error tolerance for root finding.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000565,absolute tolerance for root finding,,Absolute error tolerance for root finding.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000211,,,,,kisao:KISAO_0000565,,2020-10-29,JRK,,http://identifiers.org/biosimulators/copasi,,,Absolute error tolerance for root finding.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000800,systems property,,A systems-level property of an entire model or simulation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000800,,06-03-2021,LPS,,,true,,A systems-level property of an entire model or simulation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000532,loopless,,Whether to return only loopless flux solutions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000532,,,,,,,,Whether to return only loopless flux solutions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000031,Euler backward method,implicit Euler method|implicit Gaussian first order Runge-Kutta,"The Euler backward method is an implicit one-step method for the numerical integration of ODES with a given initial value. The next state of a system is calculated by solving an equation that considers both, the current state of the system and the later one.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000261,,,,,kisao:KISAO_0000031,,2007-11-02,dk,,GSL,,implicit Euler method|implicit Gaussian first order Runge-Kutta,"The Euler backward method is an implicit one-step method for the numerical integration of ODES with a given initial value. The next state of a system is calculated by solving an equation that considers both, the current state of the system and the later one.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000302,embedded Runge-Kutta method,embedded RK,An embedded Runge-Kutta method is a method in which two Runge-Kutta estimates are obtained using the same auxiliary functions ki but with a different linear combination of these functions so that one estimate has an order one greater than the other.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000302,,2011-05-10,AZ,,,true,embedded RK,An embedded Runge-Kutta method is a method in which two Runge-Kutta estimates are obtained using the same auxiliary functions ki but with a different linear combination of these functions so that one estimate has an order one greater than the other.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000809,reduced Jacobian matrix,,The reduced Jacobian matrix. The dimensions are species by species.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000809,,06-03-2021,LPS,,,,,The reduced Jacobian matrix. The dimensions are species by species.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000680,duration,,Length of time to simulate.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000680,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Length of time to simulate.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000437,flux balance analysis,FBA,Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000437,,2012-11-29,AZ,,,,FBA,Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000597,tolerance,,Numeric value specifying the desired tolerance the user wants to achieve. A smaller value means that the prediction is calculated more accurately.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000597,,2020-10-29,JRK,,,,,Numeric value specifying the desired tolerance the user wants to achieve. A smaller value means that the prediction is calculated more accurately.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000104,stochastic system behaviour,,"Algorithm, possessing this characteristic, simulates the temporal evolution of a system using probabilistic rules, so that between two simulations, the same precise initial state may result in a different final state.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000099,,,,,kisao:KISAO_0000104,,2008-07-08,NLN,,,,,"Algorithm, possessing this characteristic, simulates the temporal evolution of a system using probabilistic rules, so that between two simulations, the same precise initial state may result in a different final state.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000334,multiparticle lattice gas automata,multiparticle lattice gas cellular automata,"An algorithm which allows for an arbitrary number of particles, while keeping the benefits of the cellular automata approach [http://identifiers.org/biomodels.kisao/KISAO_0000315].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000068,,,,,kisao:KISAO_0000334,,2011-06-03,AZ,,,,multiparticle lattice gas cellular automata,"An algorithm which allows for an arbitrary number of particles, while keeping the benefits of the cellular automata approach [http://identifiers.org/biomodels.kisao/KISAO_0000315].",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000846,product ignoring NaN,,"The product of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000846,,2021-10-09,MK,,,,,"The product of a set of values, ignoring Nan entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000278,Metropolis Monte Carlo algorithm,Metropolis-Hastings algorithm|Metropolis algorithm,"A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration [http://identifiers.org/biomodels.kisao/KISAO_0000051] over configuration space.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000319,,,,,kisao:KISAO_0000278,,2011-05-09,AZ,,CompuCell3D,,Metropolis-Hastings algorithm|Metropolis algorithm,"A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration [http://identifiers.org/biomodels.kisao/KISAO_0000051] over configuration space.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000813,Eigenvalue matrix,full eigenvalue matrix,"The (full) eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000813,,06-03-2021,LPS,,,,full eigenvalue matrix,"The (full) eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000015,Gillespie first reaction algorithm,Gillespie's first reaction method,"Stochastic simulation algorithm using the reaction probability density function (next-reaction density function), giving the probability that the next reaction will happen in a given time interval. To choose the next reaction to fire, the algorithm calculates a tentative reaction time for each reaction and then select the smallest.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000241,,,,,kisao:KISAO_0000015,,2007-11-09,NLN,,Cain,,Gillespie's first reaction method,"Stochastic simulation algorithm using the reaction probability density function (next-reaction density function), giving the probability that the next reaction will happen in a given time interval. To choose the next reaction to fire, the algorithm calculates a tentative reaction time for each reaction and then select the smallest.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000615,fully-implicit regular grid finite volume method with a variable time step,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000285,,,,,kisao:KISAO_0000615,,2021-01-30,JRK,,https://identifiers.org/biosimulators/vcell,,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000615,fully-implicit regular grid finite volume method with a variable time step,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000285,,,,,kisao:KISAO_0000615,,2021-01-30,JRK,,http://identifiers.org/biosimulators/vcell,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000347,meshless geometry handling,,"Most meshless methods require a scattered set of nodal points in the domain of interest. In these methods, there may be no fixed connectivities between the nodes, unlike the finite element or finite difference methods. This feature has significant implications in modeling some physical phenomena that are characterized by a continuous change in the geometry of the domain under analysis. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000311,,,,,kisao:KISAO_0000347,,,AZ,,,,,"Most meshless methods require a scattered set of nodal points in the domain of interest. In these methods, there may be no fixed connectivities between the nodes, unlike the finite element or finite difference methods. This feature has significant implications in modeling some physical phenomena that are characterized by a continuous change in the geometry of the domain under analysis. ",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000648,step,,Iteration such as along a pseudo timecourse of a logical simulation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000648,,2021-06-04,JRK,,,,,Iteration such as along a pseudo timecourse of a logical simulation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000473,Bayesian inference algorithm,,A bayesian inference algorithm calculates a posterior probability distribution from a prior probability distribution and some additional evidence in the form of a likelyhood function.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000473,,2015-04-23,AZ,,,,,A bayesian inference algorithm calculates a posterior probability distribution from a prior probability distribution and some additional evidence in the form of a likelyhood function.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000081,estimated midpoint tau-leaping method,explicit tau-leaping method with estimated-mid point technique,"Estimated-Midpoint tau-Leap Method: For the selected leaping time tau which satisfies the Leap Condition, compute the expected state change lambda' = tau sumj( aj(x)vj ) during [t, t + tau). Then, with x' =x + [lambda'/2], generate for each j = 1,...,M a sample value kj of the Poisson random variable P(aj(x'), tau). Compute the actual state change, lambda = sumj( kjvj ), and effect the leap by replacing t by t + tau and x by x + lambda.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000081,,,,,,,explicit tau-leaping method with estimated-mid point technique,"Estimated-Midpoint tau-Leap Method: For the selected leaping time tau which satisfies the Leap Condition, compute the expected state change lambda' = tau sumj( aj(x)vj ) during [t, t + tau). Then, with x' =x + [lambda'/2], generate for each j = 1,...,M a sample value kj of the Poisson random variable P(aj(x'), tau). Compute the actual state change, lambda = sumj( kjvj ), and effect the leap by replacing t by t + tau and x by x + lambda.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000850,cumulative product ignoring NaN,,"The cumulative product of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000850,,2021-10-09,MK,,,,,"The cumulative product of a set of values, ignoring Nan entries.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000687,Max-min Driving Force method,MDF,"For a given metabolic network model, the MDF method determines plausible metabolite concentrations and thermodynamic forces. Flux directions, equilibrium constants (or equivalently, standard Gibbs free energies of reactions) and admissible metabolite concentration ranges are given as input data. The method applies the principle that low thermodynamic forces must be avoided, and maximizes the minimum thermodynamic force across the entire network.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000685,,,,,kisao:KISAO_0000687,,2022-03-27,EN|JRK|WL,,https://gitlab.com/equilibrator/metabolic-pathway-analysis,,MDF,"For a given metabolic network model, the MDF method determines plausible metabolite concentrations and thermodynamic forces. Flux directions, equilibrium constants (or equivalently, standard Gibbs free energies of reactions) and admissible metabolite concentration ranges are given as input data. The method applies the principle that low thermodynamic forces must be avoided, and maximizes the minimum thermodynamic force across the entire network.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000223,number of history bins,,"The 'number of history bins' is only enabled for models that contain delayed or multistep reactions for specifying the granularity with which the delayed reaction solver should retain the history of species values, for species that participate in delayed reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000223,,,,,,,,"The 'number of history bins' is only enabled for models that contain delayed or multistep reactions for specifying the granularity with which the delayed reaction solver should retain the history of species values, for species that participate in delayed reactions.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000282,KINSOL,Newton-Krylov solver for nonlinear algebraic systems|FKINSOL|NKSOL,"KINSOL solves algebraic systems in real N-space, written as F(u)=0, F:RN->RN, given an initial guess u0. The basic method is either a modified or an inexact Newton iteration [http://identifiers.org/biomodels.kisao/KISAO_0000408]. The linear systems that arise are solved with either a direct (dense or banded) solver (serial version only), or one of the Krylov iterative solvers [http://identifiers.org/biomodels.kisao/KISAO_0000354]. In the Krylov case, the user can (optionally) supply a right preconditioner.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000282,,2011-05-09,AZ,,SUNDIALS,,Newton-Krylov solver for nonlinear algebraic systems|FKINSOL|NKSOL,"KINSOL solves algebraic systems in real N-space, written as F(u)=0, F:RN->RN, given an initial guess u0. The basic method is either a modified or an inexact Newton iteration [http://identifiers.org/biomodels.kisao/KISAO_0000408]. The linear systems that arise are solved with either a direct (dense or banded) solver (serial version only), or one of the Krylov iterative solvers [http://identifiers.org/biomodels.kisao/KISAO_0000354]. In the Krylov case, the user can (optionally) supply a right preconditioner.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000504,random search,,"Random search is an optimization method that attempts to find the optimum by testing the objective function's value on a series of combinations of random values of the adjustable parameters. The random values are generated complying with any boundaries selected by the user, furthermore, any combinations of parameter values that do not fulfill constraints on the variables are excluded. This means that the method is capable of handling bounds on the adjustable parameters and fulfilling constraints. -For infinite number of iterations this method is guaranteed to find the global optimum of the objective function. In general one is interested in processing a very large number of iterations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000504,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,,"Random search is an optimization method that attempts to find the optimum by testing the objective function's value on a series of combinations of random values of the adjustable parameters. The random values are generated complying with any boundaries selected by the user, furthermore, any combinations of parameter values that do not fulfill constraints on the variables are excluded. This means that the method is capable of handling bounds on the adjustable parameters and fulfilling constraints. +For infinite number of iterations this method is guaranteed to find the global optimum of the objective function. In general one is interested in processing a very large number of iterations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000504,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,,"Random search is an optimization method that attempts to find the optimum by testing the objective function's value on a series of combinations of random values of the adjustable parameters. The random values are generated complying with any boundaries selected by the user, furthermore, any combinations of parameter values that do not fulfill constraints on the variables are excluded. This means that the method is capable of handling bounds on the adjustable parameters and fulfilling constraints. For infinite number of iterations this method is guaranteed to find the global optimum of the objective function. In general one is interested in processing a very large number of iterations.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000028,slow-scale stochastic simulation algorithm,slow-scale stochastic SSA|ssSSA,Attempt to overcome the problem of stiff systems by developing an ''approximate theory that allows one to stochastically advance the system in time by simulating the firings of only the slow reaction events''.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000028,,,,,,,slow-scale stochastic SSA|ssSSA,Attempt to overcome the problem of stiff systems by developing an ''approximate theory that allows one to stochastically advance the system in time by simulating the firings of only the slow reaction events''.,,,,,,, @@ -109,21 +121,27 @@ http://www.biomodels.net/kisao/KISAO#KISAO_0000256,virtual box side length,,The http://www.biomodels.net/kisao/KISAO#KISAO_0000435,embedded Runge-Kutta 5(4) method,RK5(4),An embedded Runge-Kutta integrator of order 5(4).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000435,,2012-09-26,AZ,,,true,RK5(4),An embedded Runge-Kutta integrator of order 5(4).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000309,Crank-Nicolson method,,"In numerical analysis, the Crank-Nicolson method is a finite difference method [http://identifiers.org/biomodels.kisao/KISAO_0000307] used for numerically solving the heat equation and similar partial differential equations. It is a second-order method in time, implicit in time, and is numerically stable. The method was developed by John Crank and Phyllis Nicolson in the mid 20th century.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000307,,,,,kisao:KISAO_0000309,,2011-05-11,AZ,,,,,"In numerical analysis, the Crank-Nicolson method is a finite difference method [http://identifiers.org/biomodels.kisao/KISAO_0000307] used for numerically solving the heat equation and similar partial differential equations. It is a second-order method in time, implicit in time, and is numerically stable. The method was developed by John Crank and Phyllis Nicolson in the mid 20th century.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000239,explicit method type,,"Explicit methods calculate the state of a system at a later time from the state of the system at the current time. Mathematically, if Y(t) is the current system state and Y((t+delta t) is the state at the later time (delta t is a small time step), then, for an explicit method Y(t+delta t) = F(Y(t)), to find Y(t+delta t).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000238,,,,,kisao:KISAO_0000239,,,AZ,,,,,"Explicit methods calculate the state of a system at a later time from the state of the system at the current time. Mathematically, if Y(t) is the current system state and Y((t+delta t) is the state at the later time (delta t is a small time step), then, for an explicit method Y(t+delta t) = F(Y(t)), to find Y(t+delta t).",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000537,explicit Runge-Kutta method of order 3(2),RK23,"RK23 uses the Bogacki-Shampine pair of formulas [1]. The error is controlled assuming accuracy of the second-order method, but steps are taken using the third-order accurate formula (local extrapolation is done). A cubic Hermite polynomial is used for the dense output.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000537,,2020-10-29,JRK,,Odespy|https://identifiers.org/biosimulators/gillespy2|MATLAB|deSolve|Octave|SciPy,,RK23,"RK23 uses the Bogacki-Shampine pair of formulas [1]. The error is controlled assuming accuracy of the second-order method, but steps are taken using the third-order accurate formula (local extrapolation is done). A cubic Hermite polynomial is used for the dense output.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000086,Fehlberg method,RKF45|Runge-Kutta-Fehlberg method,"The method was developed by the German mathematician Erwin Fehlberg and is based on the class of Runge-Kutta methods. The Runge-Kutta-Fehlberg method uses an O(h4) method together with an O(h5) method that uses all of the points of the O(h4) method, and hence is often referred to as an RKF45 method. Similar schemes with different orders have since been developed. By performing one extra calculation that would be required for an RK5 method, the error in the solution can be estimated and controlled and an appropriate step size can be determined automatically, making this method efficient for ordinary problems of automated numerical integration of ordinary differential equations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000435,,,,,kisao:KISAO_0000086,,,,,https://identifiers.org/biosimulators/jsim|https://identifiers.org/biosimulators/ibiosim|GSL|https://identifiers.org/biosimulators/vcell|https://identifiers.org/biosimulators/ecell4,,RKF45|Runge-Kutta-Fehlberg method,"The method was developed by the German mathematician Erwin Fehlberg and is based on the class of Runge-Kutta methods. The Runge-Kutta-Fehlberg method uses an O(h4) method together with an O(h5) method that uses all of the points of the O(h4) method, and hence is often referred to as an RKF45 method. Similar schemes with different orders have since been developed. By performing one extra calculation that would be required for an RK5 method, the error in the solution can be estimated and controlled and an appropriate step size can be determined automatically, making this method efficient for ordinary problems of automated numerical integration of ordinary differential equations.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000563,Pahle hybrid Gibson-Bruck Next Reaction method/RK-45 method,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with RK-45. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000231,,,,,kisao:KISAO_0000563,,2020-10-29,JRK,,https://identifiers.org/biosimulators/copasi,,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with RK-45. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000537,explicit Runge-Kutta method of order 3(2),RK23,"RK23 uses the Bogacki-Shampine pair of formulas [1]. The error is controlled assuming accuracy of the second-order method, but steps are taken using the third-order accurate formula (local extrapolation is done). A cubic Hermite polynomial is used for the dense output.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000537,,2020-10-29,JRK,,Odespy|MATLAB|http://identifiers.org/biosimulators/gillespy2|deSolve|Octave|SciPy,,RK23,"RK23 uses the Bogacki-Shampine pair of formulas [1]. The error is controlled assuming accuracy of the second-order method, but steps are taken using the third-order accurate formula (local extrapolation is done). A cubic Hermite polynomial is used for the dense output.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000086,Fehlberg method,RKF45|Runge-Kutta-Fehlberg method,"The method was developed by the German mathematician Erwin Fehlberg and is based on the class of Runge-Kutta methods. The Runge-Kutta-Fehlberg method uses an O(h4) method together with an O(h5) method that uses all of the points of the O(h4) method, and hence is often referred to as an RKF45 method. Similar schemes with different orders have since been developed. By performing one extra calculation that would be required for an RK5 method, the error in the solution can be estimated and controlled and an appropriate step size can be determined automatically, making this method efficient for ordinary problems of automated numerical integration of ordinary differential equations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000435,,,,,kisao:KISAO_0000086,,,,,GSL|http://identifiers.org/biosimulators/ibiosim|http://identifiers.org/biosimulators/jsim|http://identifiers.org/biosimulators/ecell4|http://identifiers.org/biosimulators/vcell,,RKF45|Runge-Kutta-Fehlberg method,"The method was developed by the German mathematician Erwin Fehlberg and is based on the class of Runge-Kutta methods. The Runge-Kutta-Fehlberg method uses an O(h4) method together with an O(h5) method that uses all of the points of the O(h4) method, and hence is often referred to as an RKF45 method. Similar schemes with different orders have since been developed. By performing one extra calculation that would be required for an RK5 method, the error in the solution can be estimated and controlled and an appropriate step size can be determined automatically, making this method efficient for ordinary problems of automated numerical integration of ordinary differential equations.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000563,Pahle hybrid Gibson-Bruck Next Reaction method/RK-45 method,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with RK-45. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000231,,,,,kisao:KISAO_0000563,,2020-10-29,JRK,,http://identifiers.org/biosimulators/copasi,,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with RK-45. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000807,elasticity coefficient (scaled),,A scaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000807,,06-03-2021,LPS,,,,,A scaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000691,metabolic system,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000690,,,,,kisao:KISAO_0000691,,2022-03-29,EN|JRK|WL,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000378,implicit midpoint rule,implicit Gaussian second order Runge-Kutta method,The implicit midpoint rule is a second-order case of the more general implicit s-stage Runge-Kutta methods [http://identifiers.org/biomodels.kisao/KISAO_0000064 and (http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000240)].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000378,,2011-06-30,AZ,,GSL,,implicit Gaussian second order Runge-Kutta method,The implicit midpoint rule is a second-order case of the more general implicit s-stage Runge-Kutta methods [http://identifiers.org/biomodels.kisao/KISAO_0000064 and (http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000240)].,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000345,h-p cloud method,h-p clouds|method of clouds,"A meshless method, which uses a partition of unity to construct the family of h-p cloud functions. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000345,,2011-06-09,AZ,,,,h-p clouds|method of clouds,"A meshless method, which uses a partition of unity to construct the family of h-p cloud functions. ",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000576,Quadratic MOMA,MOMA|Quadratic Minimization of Metabolic Adjustment|Minimization of Metabolic Adjustment,"Minimization Of Metabolic Adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA employs quadratic programming to identify the closest point (in terms of its Euclidean distance) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem +http://www.biomodels.net/kisao/KISAO#KISAO_0000844,sum ignoring NaN,,"The sum of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000844,,2021-10-09,MK,,,,,"The sum of a set of values, ignoring Nan entries.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000576,quadratic MOMA,MOMA|Quadratic Minimization of Metabolic Adjustment|quadratic minimization of metabolic adjustment|minimization of metabolic adjustment,"Minimization of metabolic adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA employs quadratic programming to identify the closest point (in terms of its Euclidean distance) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem -Min sum((fluxAi - fluxBi)^2) + sum(fluxAi)^(fluxMinimizationWeight) + sum(fluxBi)^(fluxMinimizationWeight)",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000593,,,,,kisao:KISAO_0000576,,2020-10-29,JRK,,https://identifiers.org/biosimulators/raven|https://identifiers.org/biosimulators/cobratoolbox|https://identifiers.org/biosimulators/optflux,,MOMA|Quadratic Minimization of Metabolic Adjustment|Minimization of Metabolic Adjustment,"Minimization Of Metabolic Adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA employs quadratic programming to identify the closest point (in terms of its Euclidean distance) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem +Min sum((fluxAi - fluxBi)^2) + sum(fluxAi)^(fluxMinimizationWeight) + sum(fluxBi)^(fluxMinimizationWeight)",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000593,,,,,kisao:KISAO_0000576,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cobratoolbox|http://identifiers.org/biosimulators/raven|http://identifiers.org/biosimulators/optflux,,MOMA|Quadratic Minimization of Metabolic Adjustment|quadratic minimization of metabolic adjustment|minimization of metabolic adjustment,"Minimization of metabolic adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA employs quadratic programming to identify the closest point (in terms of its Euclidean distance) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem Min sum((fluxAi - fluxBi)^2) + sum(fluxAi)^(fluxMinimizationWeight) + sum(fluxBi)^(fluxMinimizationWeight)",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000811,stoichiometry matrix,full stochiometry matrix,The (full) stoichiometry matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000811,,06-03-2021,LPS,,,,full stochiometry matrix,The (full) stoichiometry matrix. The dimensions are species by reactions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000243,method switching control parameter,,Parameters describing threshold conditions for algorithms that switch between different methods.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000243,,,AZ,,,true,,Parameters describing threshold conditions for algorithms that switch between different methods.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000659,Naldi MDD logical model stable state search method,Naldi Multi-valued Decision Diagram stable state search method,Efficient method for determining the stable states of a regulatory graph using a Multi-valued Decision Diagram (MDD) representation of the logical functions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000660,,,,,kisao:KISAO_0000659,,2021-07-08,JRK,,,,Naldi Multi-valued Decision Diagram stable state search method,Efficient method for determining the stable states of a regulatory graph using a Multi-valued Decision Diagram (MDD) representation of the logical functions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000626,band direct solver,banded,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000624,,,,,kisao:KISAO_0000626,,2021-06-01,JRK,,CVODE,,banded,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000451,type of updating policy,,A rule to guide the choice of a unique transition at each step used by an 'asynchronous logical model simulation method' [http://identifiers.org/biomodels.kisao/KISAO_0000450].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000451,,2013-01-28,AZ,,,true,,A rule to guide the choice of a unique transition at each step used by an 'asynchronous logical model simulation method' [http://identifiers.org/biomodels.kisao/KISAO_0000450].,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000484,maximum order,,"Maximum order of method. For example, in Roadrunner it can be used for two parameters that one can set for deterministic runs: 'maximum_bdf_order' and 'maximum_adams_order'.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000594,,,,,kisao:KISAO_0000484,,,AZ,,Roadrunner,,,"Maximum order of method. For example, in Roadrunner it can be used for two parameters that one can set for deterministic runs: 'maximum_bdf_order' and 'maximum_adams_order'.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000058,Greens function reaction dynamics,GFRD|Green's function reaction dynamics,Method that simulates biochemical networks on particle level. It considers both changes in time and space by ''exploiting both the exact solution of the Smoluchowski Equation to set up an event-driven algorithm'' which allows for large jumps in time when the considered particles are far away from each other [in space] and thus cannot react. GFRD combines the propagation of particles in space with the reactions taking place between them in one simulation step.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000056,,,,,kisao:KISAO_0000058,,,,,,,GFRD|Green's function reaction dynamics,Method that simulates biochemical networks on particle level. It considers both changes in time and space by ''exploiting both the exact solution of the Smoluchowski Equation to set up an event-driven algorithm'' which allows for large jumps in time when the considered particles are far away from each other [in space] and thus cannot react. GFRD combines the propagation of particles in space with the reactions taking place between them in one simulation step.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000857,median,,The median of a set of values. If the values contain NaN the median is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000857,,2021-10-09,MK,,,,,The median of a set of values. If the values contain NaN the median is NaN.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000289,Adams method,,"Adams' methods are multi-step methods used for the numerical integration of initial value problems in Ordinary Differential Equations (ODE's). Adams' algorithm consists of two parts: firstly, a starting procedure which provides y1, ... , yk-1 ( approximations to the exact solution at the points x0 + h, ... , x0 + (k - 1)h ) and, secondly, a multistep formula to obtain an approximation to the exact solution y(x0 + kh). This is then applied recursively, based on the numerical approximation of k successive steps, to compute y(x0 + (k + 1)h).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000281,,,,,kisao:KISAO_0000289,,2011-05-10,AZ,,ByoDyn,,,"Adams' methods are multi-step methods used for the numerical integration of initial value problems in Ordinary Differential Equations (ODE's). Adams' algorithm consists of two parts: firstly, a starting procedure which provides y1, ... , yk-1 ( approximations to the exact solution at the points x0 + h, ... , x0 + (k - 1)h ) and, secondly, a multistep formula to obtain an approximation to the exact solution y(x0 + kh). This is then applied recursively, based on the numerical approximation of k successive steps, to compute y(x0 + (k + 1)h).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000382,modified midpoint method,Gragg's method|Gragg's modified midpoint method,"The modified midpoint method is globally a second order method for approximating the solution of the initial value problem y' = f(x, y), y(x0) = y0, which advances a vector of dependent variables y(x) from a point x to a point x + H by a sequence of n substeps each of size h, h = H/n. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000382,,2011-07-01,AZ,,,,Gragg's method|Gragg's modified midpoint method,"The modified midpoint method is globally a second order method for approximating the solution of the initial value problem y' = f(x, y), y(x0) = y0, which advances a vector of dependent variables y(x) from a point x to a point x + H by a sequence of n substeps each of size h, h = H/n. @@ -132,27 +150,28 @@ http://www.biomodels.net/kisao/KISAO#KISAO_0000630,root-finding method,,Method f http://www.biomodels.net/kisao/KISAO#KISAO_0000362,implicit-state Doob-Gillespie algorithm,,"The algorithm uses a representation of the system together with a super-approximation of its ‘event horizon’ (all events that may happen next), and a specific correction scheme to obtain exact timings. Being completely local and not based on any kind of enumeration, this algorithm has a per event time cost which is independent of (i) the size of the set of generable species (which can even be infinite), and (ii) independent of the size of the system (ie, the number of agent instances). The algorithm can be refined, using concepts derived from the classical notion of causality, so that in addition to the above one also has that the even cost is depending (iii) only logarithmically on the size of the model (ie, the number of rules). ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000017,,,,,kisao:KISAO_0000362,,2011-06-13,AZ,,,,,"The algorithm uses a representation of the system together with a super-approximation of its ‘event horizon’ (all events that may happen next), and a specific correction scheme to obtain exact timings. Being completely local and not based on any kind of enumeration, this algorithm has a per event time cost which is independent of (i) the size of the set of generable species (which can even be infinite), and (ii) independent of the size of the system (ie, the number of agent instances). The algorithm can be refined, using concepts derived from the classical notion of causality, so that in addition to the above one also has that the even cost is depending (iii) only logarithmically on the size of the model (ie, the number of rules). ",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000590,ACHR flux sampling method,Artificial Centering Hit-and-Run flux sampling method,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000588,,,,,kisao:KISAO_0000590,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cobrapy|https://identifiers.org/biosimulators/cobratoolbox,,Artificial Centering Hit-and-Run flux sampling method,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000590,ACHR flux sampling method,artificial centering hit-and-run flux sampling method,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000588,,,,,kisao:KISAO_0000590,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cobratoolbox|http://identifiers.org/biosimulators/cobrapy,,artificial centering hit-and-run flux sampling method,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000548,quadratic programming,QP,Process of solving a quadratic optimization problem.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000549,,,,,kisao:KISAO_0000548,,2020-10-29,JRK,,CPLEX|CVXOPT|Mosek|MATLAB|OptLang|XPRESS|Gurobi|ConvOpt,,QP,Process of solving a quadratic optimization problem.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000515,Levenberg-Marquardt,,"Levenberg-Marquardt is a gradient descent method. It is a hybrid between the steepest descent and the Newton methods. -Levenberg first suggested an improvement to the Newton method in order to make it more robust, i.e. to overcome the problem of non-convergence. His suggestion was to add a factor to the diagonal elements of the Hessian matrix of second derivatives when not close to the minimum (this can be judged by how positive definite the matrix is). The effect when this factor is large compared to the elements of Hessian is that the method then becomes the steepest descent method. Later Marquardt suggested that the factor should be multiplicative rather than additive and also defined a heuristic to make this factor increase or decrease. The method known as Levenberg-Marquardt is thus an adaptive method that effectively changes between the steepest descent to the Newton method.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000515,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,,"Levenberg-Marquardt is a gradient descent method. It is a hybrid between the steepest descent and the Newton methods. +Levenberg first suggested an improvement to the Newton method in order to make it more robust, i.e. to overcome the problem of non-convergence. His suggestion was to add a factor to the diagonal elements of the Hessian matrix of second derivatives when not close to the minimum (this can be judged by how positive definite the matrix is). The effect when this factor is large compared to the elements of Hessian is that the method then becomes the steepest descent method. Later Marquardt suggested that the factor should be multiplicative rather than additive and also defined a heuristic to make this factor increase or decrease. The method known as Levenberg-Marquardt is thus an adaptive method that effectively changes between the steepest descent to the Newton method.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000515,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,,"Levenberg-Marquardt is a gradient descent method. It is a hybrid between the steepest descent and the Newton methods. Levenberg first suggested an improvement to the Newton method in order to make it more robust, i.e. to overcome the problem of non-convergence. His suggestion was to add a factor to the diagonal elements of the Hessian matrix of second derivatives when not close to the minimum (this can be judged by how positive definite the matrix is). The effect when this factor is large compared to the elements of Hessian is that the method then becomes the steepest descent method. Later Marquardt suggested that the factor should be multiplicative rather than additive and also defined a heuristic to make this factor increase or decrease. The method known as Levenberg-Marquardt is thus an adaptive method that effectively changes between the steepest descent to the Newton method.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000260,virtual box size,,Target size of virtual boxes for 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000260,,,,,,,,Target size of virtual boxes for 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057].,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000807,Elasticity coefficient (scaled),,A scaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000807,,06-03-2021,LPS,,,,,A scaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000663,BDD logical model trap space identification method,Binary Decision Diagram logical model trap space identification method,Method for determining the trap spaces of a regulatory graph using a Binary Decision Diagram (BDD).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000661,,,,,kisao:KISAO_0000663,,2021-07-08,JRK,,,,Binary Decision Diagram logical model trap space identification method,Method for determining the trap spaces of a regulatory graph using a Binary Decision Diagram (BDD).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000395,improved biconjugate gradient method,,"An 'improved biconjugate gradient method' branch contains algorithms which can be viewed as improvements over some of drawbacks of BCG [http://identifiers.org/biomodels.kisao/KISAO_0000358], such as (1) the need for matrix-vector multiplications with A^T (which can be inconvenient as well as doubling the number of matrix-vector multiplications compared to CG [http://identifiers.org/biomodels.kisao/KISAO_0000357] for each increase in the degree of the underlying Krylov subspace), (2) the possibility of breakdowns and (3) erratic convergence behavior. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000354,,,,,kisao:KISAO_0000395,,2011-07-19,AZ,,,true,,"An 'improved biconjugate gradient method' branch contains algorithms which can be viewed as improvements over some of drawbacks of BCG [http://identifiers.org/biomodels.kisao/KISAO_0000358], such as (1) the need for matrix-vector multiplications with A^T (which can be inconvenient as well as doubling the number of matrix-vector multiplications compared to CG [http://identifiers.org/biomodels.kisao/KISAO_0000357] for each increase in the degree of the underlying Krylov subspace), (2) the possibility of breakdowns and (3) erratic convergence behavior. ",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000574,species transition probabilities,,Probability of each species to be chosen for the next state transition.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000574,,2020-10-29,JRK,,https://identifiers.org/biosimulators/boolnet,,,Probability of each species to be chosen for the next state transition.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000574,species transition probabilities,,Probability of each species to be chosen for the next state transition.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000574,,2020-10-29,JRK,,http://identifiers.org/biosimulators/boolnet,,,Probability of each species to be chosen for the next state transition.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000040,Poisson tau-leaping method,explicit tau-leaping|explicit tau-leaping method with basic pre-leap check|poisson tau-leaping|explicit tau-leaping method with basic preleap check,Explicit tau-leaping method with basic pre leap check.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000040,,,,,ByoDyn,,explicit tau-leaping|explicit tau-leaping method with basic pre-leap check|poisson tau-leaping|explicit tau-leaping method with basic preleap check,Explicit tau-leaping method with basic pre leap check.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000541,Beta parameter for stabilized step size control,beta,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243|http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000541,,2020-10-29,JRK,,Odespy|https://identifiers.org/biosimulators/gillespy2|JModelica|SciPy,,beta,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000541,beta parameter for stabilized step size control,beta,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243|http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000541,,2020-10-29,JRK,,Odespy|JModelica|http://identifiers.org/biosimulators/gillespy2|SciPy,,beta,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000095,sub-volume stochastic reaction-diffusion algorithm,,Stochastic method using a combination of discretisation of compartment volumes into voxels and Gillespie-like algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000241] to simulate the evolution of the system.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000095,,2008-07-08,NLN,,,true,,Stochastic method using a combination of discretisation of compartment volumes into voxels and Gillespie-like algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000241] to simulate the evolution of the system.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000317,E-Cell multi-algorithm simulation method,,A modular meta-algorithm with a discrete event scheduler that can incorporate any type of time-driven simulation algorithm. It was shown that this meta-algorithm can efficiently drive simulation models with different simulation algorithms with little intrusive modification to the algorithms themselves. Only a few additional methods to handle communications between computational modules are required.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000317,,2011-05-23,AZ,,https://identifiers.org/biosimulators/ecell4,,,A modular meta-algorithm with a discrete event scheduler that can incorporate any type of time-driven simulation algorithm. It was shown that this meta-algorithm can efficiently drive simulation models with different simulation algorithms with little intrusive modification to the algorithms themselves. Only a few additional methods to handle communications between computational modules are required.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000818,L0 matrix,,The L0 matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:KISAO_0000818,,06-03-2021,LPS,,,,,The L0 matrix of a model.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000317,E-Cell multi-algorithm simulation method,,A modular meta-algorithm with a discrete event scheduler that can incorporate any type of time-driven simulation algorithm. It was shown that this meta-algorithm can efficiently drive simulation models with different simulation algorithms with little intrusive modification to the algorithms themselves. Only a few additional methods to handle communications between computational modules are required.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000317,,2011-05-23,AZ,,http://identifiers.org/biosimulators/ecell4,,,A modular meta-algorithm with a discrete event scheduler that can incorporate any type of time-driven simulation algorithm. It was shown that this meta-algorithm can efficiently drive simulation models with different simulation algorithms with little intrusive modification to the algorithms themselves. Only a few additional methods to handle communications between computational modules are required.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000818,L0 matrix,,The L0 matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000818,,06-03-2021,LPS,,,,,The L0 matrix of a model.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000413,exact Newton method,direct Newton method,"Any of the finite dimensional Newton-type methods [http://identifiers.org/biomodels.kisao/KISAO_0000408] requires the numerical solution of the linear equations F'(x[k])deltax[k]=-F(x[k]). Whenever direct elimination methods are applicable, we speak of exact Newton methods.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000413,,2012-01-18,AZ,,,,direct Newton method,"Any of the finite dimensional Newton-type methods [http://identifiers.org/biomodels.kisao/KISAO_0000408] requires the numerical solution of the linear equations F'(x[k])deltax[k]=-F(x[k]). Whenever direct elimination methods are applicable, we speak of exact Newton methods.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000343,generalized finite element method,partition of unity method|PUM|GFEM,"The GFEM is a generalization of the classical 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] — in its h [http://identifiers.org/biomodels.kisao/KISAO_0000338], p [http://identifiers.org/biomodels.kisao/KISAO_0000339], and h-p versions [http://identifiers.org/biomodels.kisao/KISAO_0000340]— as well as of the various forms of meshless methods used in engineering.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000343,,2011-06-07,AZ,,,,partition of unity method|PUM|GFEM,"The GFEM is a generalization of the classical 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] — in its h [http://identifiers.org/biomodels.kisao/KISAO_0000338], p [http://identifiers.org/biomodels.kisao/KISAO_0000339], and h-p versions [http://identifiers.org/biomodels.kisao/KISAO_0000340]— as well as of the various forms of meshless methods used in engineering.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000478,preconditioner,,"the preconditioner, if any, used by the solver when using a GMRES, BiCGStab or TFQMR linear solver during a Newton iteration.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000478,,2015-09-10,AZ,,https://identifiers.org/biosimulators/opencor,,,"the preconditioner, if any, used by the solver when using a GMRES, BiCGStab or TFQMR linear solver during a Newton iteration.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000478,preconditioner,,"the preconditioner, if any, used by the solver when using a GMRES, BiCGStab or TFQMR linear solver during a Newton iteration.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000478,,2015-09-10,AZ,,http://identifiers.org/biosimulators/opencor,,,"the preconditioner, if any, used by the solver when using a GMRES, BiCGStab or TFQMR linear solver during a Newton iteration.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000310,method of lines,NUMOL|NMOL|MOL,The method of lines is a general technique for solving partial differential equations (PDEs) by typically using finite difference relationships for the spatial derivatives and ordinary differential equations for the time derivative.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000310,,2011-05-11,AZ,,,,NUMOL|NMOL|MOL,The method of lines is a general technique for solving partial differential equations (PDEs) by typically using finite difference relationships for the spatial derivatives and ordinary differential equations for the time derivative.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000822,extensive property,,"An extensive variable such as an amount, particle number, or mass.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000822,,06-03-2021,LPS,,,,,"An extensive variable such as an amount, particle number, or mass.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000855,length,,The number of elements of a set of values.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000855,,2021-10-09,MK,,,,,The number of elements of a set of values.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000287,Milstein method,,The Milstein method is a technique for the approximate numerical solution of a stochastic differential equation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000281,,,,,kisao:KISAO_0000287,,2011-05-10,AZ,,,,,The Milstein method is a technique for the approximate numerical solution of a stochastic differential equation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000254,Brownian diffusion accuracy,,"Accuracy code of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057], which sets which neighbouring boxes are checked for potential bi-molecular reactions. Consider the reaction A + B -> C and suppose that A and B are within a binding radius of each other. This reaction will always be performed if A and B are in the same virtual box. If accuracy is set to at least 3, then it will also occur if A and B are in nearest-neighbour virtual boxes. If it is at least 7, then the reaction will happen if they are in nearest-neighbour boxes that are separated by periodic boundary conditions. And if it is 9 or 10, then all edge and corner boxes are checked for reactions, which means that no potential reactions are overlooked.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000254,,,,,Smoldyn,,,"Accuracy code of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057], which sets which neighbouring boxes are checked for potential bi-molecular reactions. Consider the reaction A + B -> C and suppose that A and B are within a binding radius of each other. This reaction will always be performed if A and B are in the same virtual box. If accuracy is set to at least 3, then it will also occur if A and B are in nearest-neighbour virtual boxes. If it is at least 7, then the reaction will happen if they are in nearest-neighbour boxes that are separated by periodic boundary conditions. And if it is 9 or 10, then all edge and corner boxes are checked for reactions, which means that no potential reactions are overlooked.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000389,quasi-minimal residual method,QMR,"The QMR algorithm is a robust iterative solver for general nonsingular non-Hermitian linear systems. The method uses a robust implementation of the look-ahead Lanczos algorithm to generate basis vectors for the Krylov subspaces Kn(r0, A). The QMR iterates are characterized by a quasi-minimal residual property over Kn(r0, A).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000393,,,,,kisao:KISAO_0000389,,2011-07-18,AZ,,,,QMR,"The QMR algorithm is a robust iterative solver for general nonsingular non-Hermitian linear systems. The method uses a robust implementation of the look-ahead Lanczos algorithm to generate basis vectors for the Krylov subspaces Kn(r0, A). The QMR iterates are characterized by a quasi-minimal residual property over Kn(r0, A).",,,,,,, @@ -182,179 +201,198 @@ http://www.biomodels.net/kisao/KISAO#KISAO_0000637,derived property,,An output o http://www.biomodels.net/kisao/KISAO#KISAO_0000090,LSODI,"Livermore solver for ordinary differential equations, implicit version","LSODI solves systems given in linearly implicit form, including differential-algebraic systems.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000090,,,,,,,"Livermore solver for ordinary differential equations, implicit version","LSODI solves systems given in linearly implicit form, including differential-algebraic systems.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000232,LSOIBT,"Livermore solver for ordinary differential equations given in implicit form, with block-tridiagonal Jacobian treatment",LSOIBT solves linearly implicit systems in which the matrices involved are all assumed to be block-tridiagonal. Linear systems are solved by the LU method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000232,,,,,,,"Livermore solver for ordinary differential equations given in implicit form, with block-tridiagonal Jacobian treatment",LSOIBT solves linearly implicit systems in which the matrices involved are all assumed to be block-tridiagonal. Linear systems are solved by the LU method.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000367,partitioned Runge-Kutta method,symplectic partitioned Runge-Kutta method|PRK|SPRK,"If a Hamiltonian system possesses a natural partitioning, it is possible to integrate its certain components using one Runge-Kutta method and other components using a different Runge-Kutta method. The overall s-stage scheme is called a partitioned Runge-Kutta method.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000367,,2011-06-16,AZ,,,,symplectic partitioned Runge-Kutta method|PRK|SPRK,"If a Hamiltonian system possesses a natural partitioning, it is possible to integrate its certain components using one Runge-Kutta method and other components using a different Runge-Kutta method. The overall s-stage scheme is called a partitioned Runge-Kutta method.",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000806,Elasticity matrix (scaled),,The scaled elasticity matrix. The dimensions are reactions by species.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000806,,06-03-2021,LPS,,,,,The scaled elasticity matrix. The dimensions are reactions by species.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000674,presimulate,,Whether a model should be presimulated prior to analysis.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000674,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Whether a model should be presimulated prior to analysis.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000641,bound,,An upper or lower bound on an estimate of a quantity.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000641,,2021-06-04,JRK,,,true,,An upper or lower bound on an estimate of a quantity.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000526,flux variability analysis,FVA,Method for determining the minimum and maximum flux of each reaction that satisfies the flux constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000526,,2020-08-11,AZ,,https://identifiers.org/biosimulators/cobrapy,,FVA,Method for determining the minimum and maximum flux of each reaction that satisfies the flux constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000559,initial step size,,Initial time step size.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000559,,2020-10-29,JRK,,https://identifiers.org/biosimulators/tellurium|https://identifiers.org/biosimulators/copasi|libRoadRunner|https://identifiers.org/biosimulators/gillespy2|SUNDIALS|SciPy,,,Initial time step size.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000526,flux variability analysis,FVA,Method for determining the minimum and maximum flux of each reaction that satisfies the flux constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000526,,2020-08-11,AZ,,http://identifiers.org/biosimulators/cobrapy,,FVA,Method for determining the minimum and maximum flux of each reaction that satisfies the flux constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000559,initial step size,,Initial time step size.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000559,,2020-10-29,JRK,,http://identifiers.org/biosimulators/tellurium|libRoadRunner|http://identifiers.org/biosimulators/gillespy2|SUNDIALS|http://identifiers.org/biosimulators/copasi|SciPy,,,Initial time step size.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000321,Cash-Karp method,,"An family of explicit Runge-Kutta formulas, which are very efficient for problems with smooth solution as well as problems having rapidly varying solutions. Each member of this family consists of a fifty-order formula that contains embedded formulas of all orders 1 through 4. By computing solutions at several different orders, it is possible to detect sharp fronts or discontinuities before all the function evaluations defining the full Runge-Kutta step have been computed.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000435,,,,,kisao:KISAO_0000321,,2011-05-26,AZ,,Cain|GSL,,,"An family of explicit Runge-Kutta formulas, which are very efficient for problems with smooth solution as well as problems having rapidly varying solutions. Each member of this family consists of a fifty-order formula that contains embedded formulas of all orders 1 through 4. By computing solutions at several different orders, it is possible to detect sharp fronts or discontinuities before all the function evaluations defining the full Runge-Kutta step have been computed.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000051,Bortz-Kalos-Lebowitz algorithm,KMC|kinetic Monte Carlo method|BKL|DMC|dynamic Monte Carlo|kinetic Monte Carlo|dynamic Monte Carlo method|n-fold way,The Bortz-Kalos-Lebowitz (or: kinetic Monte-Carlo-) method is a stochastic method for the simulation of time evolution of processes using (pseudo-)random numbers.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000051,,,,,,,KMC|kinetic Monte Carlo method|BKL|DMC|dynamic Monte Carlo|kinetic Monte Carlo|dynamic Monte Carlo method|n-fold way,The Bortz-Kalos-Lebowitz (or: kinetic Monte-Carlo-) method is a stochastic method for the simulation of time evolution of processes using (pseudo-)random numbers.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000585,TOMS731,,Moving-grid interface for systems of one-dimensional time-dependent partial differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000585,,2020-10-29,JRK,,https://identifiers.org/biosimulators/jsim,,,Moving-grid interface for systems of one-dimensional time-dependent partial differential equations.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000853,count,,The number of non-zero elements of a set of values. If the values contain NaN the count is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000853,,2021-10-09,MK,,,,,The number of non-zero elements of a set of values. If the values contain NaN the count is NaN.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000585,TOMS731,,Moving-grid interface for systems of one-dimensional time-dependent partial differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000585,,2020-10-29,JRK,,http://identifiers.org/biosimulators/jsim,,,Moving-grid interface for systems of one-dimensional time-dependent partial differential equations.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000328,minimum fast rate,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which controls the minimum rate of the reaction in order for it to be considered fast.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000328,,2011-06-02,AZ,,,,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which controls the minimum rate of the reaction in order for it to be considered fast.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000820,model and simulation property characteristic,,A property of a variable of a model or simulation.,false,,,http://www.w3.org/2002/07/owl#Thing,,,,,kisao:KISAO_0000820,,06-03-2021,LPS,,,true,,A property of a variable of a model or simulation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000354,Krylov subspace projection method,Krylov subspace method,"Krylov subspace method is an iterative linear equation method, which builds up Krylov subspaces and look for good approximations to eigenvectors and invariant subspaces within the Krylov spaces.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000398,,,,,kisao:KISAO_0000354,,2011-06-10,AZ,,,,Krylov subspace method,"Krylov subspace method is an iterative linear equation method, which builds up Krylov subspaces and look for good approximations to eigenvectors and invariant subspaces within the Krylov spaces.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000602,Minimum species threshold for continuous approximation,Epsilon,Minimum number of molecules of both reactant and product species required for approximation as a continuous Markov process.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000602,,2021-01-13,MLB|JRK,,,,Epsilon,Minimum number of molecules of both reactant and product species required for approximation as a continuous Markov process.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000602,minimum species threshold for continuous approximation,epsilon|Epsilon,Minimum number of molecules of both reactant and product species required for approximation as a continuous Markov process.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000602,,2021-01-13,MLB|JRK,,,,epsilon|Epsilon,Minimum number of molecules of both reactant and product species required for approximation as a continuous Markov process.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000230,minimum reactions per leap,threshold,"'minimum reactions per leap' parameter is used in hybrid methods, which adaptively switch between the tau-leaping algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000039] to the SSA Direct Method [http://identifiers.org/biomodels.kisao/KISAO_0000029] when the number of reactions in a single tau-leaping leap step is less than the threshold.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000230,,,,,,,threshold,"'minimum reactions per leap' parameter is used in hybrid methods, which adaptively switch between the tau-leaping algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000039] to the SSA Direct Method [http://identifiers.org/biomodels.kisao/KISAO_0000029] when the number of reactions in a single tau-leaping leap step is less than the threshold.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000595,rFBA,regulatory flux balance analysis,Method for predicting metabolic fluxes under patterns of the regulation of gene expression,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000622|http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000595,,2020-10-29,JRK,,,true,regulatory flux balance analysis,Method for predicting metabolic fluxes under patterns of the regulation of gene expression,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000332,ER-leap initial leap,L,"L (initial step) is a parameter of 'exact R-leaping method' [http://identifiers.org/biomodels.kisao/KISAO_0000331]. ''We will assume that the reaction event to be bounded occurs within a run of L events in the SSA algorithm[http://identifiers.org/biomodels.kisao/KISAO_0000029], in order to execute L reactions at once in the manner of the R-leap algorithm[http://identifiers.org/biomodels.kisao/KISAO_0000230]''.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000332,,2011-06-03,AZ,,,,L,"L (initial step) is a parameter of 'exact R-leaping method' [http://identifiers.org/biomodels.kisao/KISAO_0000331]. ''We will assume that the reaction event to be bounded occurs within a run of L events in the SSA algorithm[http://identifiers.org/biomodels.kisao/KISAO_0000029], in order to execute L reactions at once in the manner of the R-leap algorithm[http://identifiers.org/biomodels.kisao/KISAO_0000230]''.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000668,"Numerical Recipes in C ""stiff\"" Rosenbrock method",stiff,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000033,,,,,kisao:KISAO_0000668,,2021-08-08,JRK,,http://identifiers.org/biosimulators/xpp,,stiff,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000635,unscaled property,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000635,,2021-06-04,JRK,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000493,synchronous updating policy,,An updating policy where all enabled transitions occur simultaneously. Thus a state will have at most one successor.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000451,,,,,kisao:KISAO_0000493,,,,,,,,An updating policy where all enabled transitions occur simultaneously. Thus a state will have at most one successor.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000276,Gill method,Runge-Kutta-Gill method|Gill's method,"Gill's fourth order method is a Runge-Kutta method for approximating the solution of the initial value problem y'(x) = f(x,y); y(x0) = y0 which evaluates the integrand,f(x,y), four times per step. This method is a fourth order procedure for which Richardson extrapolation can be used.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000276,,2011-05-09,AZ,,,,Runge-Kutta-Gill method|Gill's method,"Gill's fourth order method is a Runge-Kutta method for approximating the solution of the initial value problem y'(x) = f(x,y); y(x0) = y0 which evaluates the integrand,f(x,y), four times per step. This method is a fourth order procedure for which Richardson extrapolation can be used.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000524,partitioned leaping method,,"Multiscale simulation approach for modeling stochasticity in chemical reaction networks. The approach seamlessly integrates exact-stochastic and ""leaping"" methodologies into a single partitioned leaping algorithmic framework. The technique correctly accounts for stochastic noise at significantly reduced computational cost, requires the definition of only three modelindependent parameters and is particularly well-suited for simulating systems containing widely disparate species populations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000524,,2020-05-29,AZ,,https://identifiers.org/biosimulators/bionetgen,,,"Multiscale simulation approach for modeling stochasticity in chemical reaction networks. The approach seamlessly integrates exact-stochastic and ""leaping"" methodologies into a single partitioned leaping algorithmic framework. The technique correctly accounts for stochastic noise at significantly reduced computational cost, requires the definition of only three modelindependent parameters and is particularly well-suited for simulating systems containing widely disparate species populations.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000524,partitioned leaping method,,"Multiscale simulation approach for modeling stochasticity in chemical reaction networks. The approach seamlessly integrates exact-stochastic and ""leaping"" methodologies into a single partitioned leaping algorithmic framework. The technique correctly accounts for stochastic noise at significantly reduced computational cost, requires the definition of only three modelindependent parameters and is particularly well-suited for simulating systems containing widely disparate species populations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000524,,2020-05-29,AZ,,http://identifiers.org/biosimulators/bionetgen,,,"Multiscale simulation approach for modeling stochasticity in chemical reaction networks. The approach seamlessly integrates exact-stochastic and ""leaping"" methodologies into a single partitioned leaping algorithmic framework. The technique correctly accounts for stochastic noise at significantly reduced computational cost, requires the definition of only three modelindependent parameters and is particularly well-suited for simulating systems containing widely disparate species populations.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000045,implicit tau-leaping method,,"Contrary to the explicit tau-leaping [http://identifiers.org/biomodels.kisao/KISAO_0000039 and http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000239] , the implicit tau-leaping allows for much larger time-steps when simulating stiff systems.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000045,,2007-10-12,dk,,,,,"Contrary to the explicit tau-leaping [http://identifiers.org/biomodels.kisao/KISAO_0000039 and http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000239] , the implicit tau-leaping allows for much larger time-steps when simulating stiff systems.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000429,clusterization parameter,,Parameter used by algorithms performing clusterization.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000429,,,AZ,,,true,,Parameter used by algorithms performing clusterization.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000672,"Numerical Recipes in C ""quality-controlled Runge-Kutta\"" method",rkqs,Cash-Karp method with step size adjustment.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000321,,,,,kisao:KISAO_0000672,,2021-08-18,JRK,,http://identifiers.org/biosimulators/xpp,,rkqs,Cash-Karp method with step size adjustment.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000371,stochastic differential equation problem,SDE problem,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000405,,,,,kisao:KISAO_0000371,,,AZ,,,,SDE problem,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000805,elasticity coefficient (unscaled),,An unscaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000805,,06-03-2021,LPS,,,,,An unscaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000307,finite difference method,FDM,"The finite difference method is based on local approximations of the partial derivatives in a Partial Differential Equation, which are derived by low order Taylor series expansions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000307,,2011-05-11,AZ,,,,FDM,"The finite difference method is based on local approximations of the partial derivatives in a Partial Differential Equation, which are derived by low order Taylor series expansions.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000685,biological state optimization method,,A method for computing the optimal state of a biological system accordion to a particular objective.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000685,,2022-03-27,EN|JRK|WL,,,true,,A method for computing the optimal state of a biological system accordion to a particular objective.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000652,concentration rate,,"Rate of a process relative to a volume, such as the rate of a reaction in molar^-1 s^-1.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000834,,,,,kisao:KISAO_0000652,,2021-06-04,JRK,,,,,"Rate of a process relative to a volume, such as the rate of a reaction in molar^-1 s^-1.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000204,particle number upper limit,,This parameter of 'Pahle hybrid method' [http://identifiers.org/biomodels.kisao/KISAO_0000231] is a double value specifying the upper limit for particle numbers. Species with a particle number above this value are considered as having a high particle number. The 'particle number upper limit' cannot be lower than the 'particle number lower limit' [http://identifiers.org/biomodels.kisao/KISAO_0000203].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000204,,,,,,,,This parameter of 'Pahle hybrid method' [http://identifiers.org/biomodels.kisao/KISAO_0000231] is a double value specifying the upper limit for particle numbers. Species with a particle number above this value are considered as having a high particle number. The 'particle number upper limit' cannot be lower than the 'particle number lower limit' [http://identifiers.org/biomodels.kisao/KISAO_0000203].,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000409,ordinary Newton method,,"A 'Newton-type method' [http://identifiers.org/biomodels.kisao/KISAO_0000408] which solves the general nonlinear problem F(x)=0 by applying successive linearization F'(x[k])deltax[k]=-F(x[k]), x[k+1]=x[k]+deltax[k], k=0,1,...",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000409,,2012-01-18,AZ,,,,,"A 'Newton-type method' [http://identifiers.org/biomodels.kisao/KISAO_0000408] which solves the general nonlinear problem F(x)=0 by applying successive linearization F'(x[k])deltax[k]=-F(x[k]), x[k+1]=x[k]+deltax[k], k=0,1,...",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000280,Adams-Moulton method,implicit Adams method,"The (k-1)-step Adams-Moulton method is an implicit linear multistep method that iteratively approximates the solution, y(x) at x = x0+kh, of the initial value problem by yk = yk - 1 + h * ( b0 f(xk,yk) + b1 f(xk - 1,yk - 1) + . . . + bk - 1 f(x1,y1) ), where b1, . . . , bk - 1 are constants.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000289,,,,,kisao:KISAO_0000280,,2011-05-09,AZ,,https://identifiers.org/biosimulators/vcell,,implicit Adams method,"The (k-1)-step Adams-Moulton method is an implicit linear multistep method that iteratively approximates the solution, y(x) at x = x0+kh, of the initial value problem by yk = yk - 1 + h * ( b0 f(xk,yk) + b1 f(xk - 1,yk - 1) + . . . + bk - 1 f(x1,y1) ), where b1, . . . , bk - 1 are constants.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000280,Adams-Moulton method,implicit Adams method,"The (k-1)-step Adams-Moulton method is an implicit linear multistep method that iteratively approximates the solution, y(x) at x = x0+kh, of the initial value problem by yk = yk - 1 + h * ( b0 f(xk,yk) + b1 f(xk - 1,yk - 1) + . . . + bk - 1 f(x1,y1) ), where b1, . . . , bk - 1 are constants.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000289,,,,,kisao:KISAO_0000280,,2011-05-09,AZ,,http://identifiers.org/biosimulators/vcell,,implicit Adams method,"The (k-1)-step Adams-Moulton method is an implicit linear multistep method that iteratively approximates the solution, y(x) at x = x0+kh, of the initial value problem by yk = yk - 1 + h * ( b0 f(xk,yk) + b1 f(xk - 1,yk - 1) + . . . + bk - 1 f(x1,y1) ), where b1, . . . , bk - 1 are constants.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000502,DA-DFBA,direct approach dynamics flux balance analysis|DA,Dynamic Flux Balance Analysis (DFBA) [http://identifiers.org/biomodels.kisao/KISAO_0000499] couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches. The direct approach (DA) includes the LP solver in the right-hand side evaluator for the ordinary differential equations (ODEs) and takes advantage of reliable implicit ODE integrators with adaptive step size for error control.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000499,,,,,kisao:KISAO_0000502,,2017-09-12,AZ,,DFBAlab,,direct approach dynamics flux balance analysis|DA,Dynamic Flux Balance Analysis (DFBA) [http://identifiers.org/biomodels.kisao/KISAO_0000499] couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches. The direct approach (DA) includes the LP solver in the right-hand side evaluator for the ordinary differential equations (ODEs) and takes advantage of reliable implicit ODE integrators with adaptive step size for error control.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000339,p-version of the finite element method,p-method|p-FEM,The p version of 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] uses a fixed mesh but increases the polynomial degree p to increase accuracy. Can be considered as a special case of the h-p version [http://identifiers.org/biomodels.kisao/KISAO_0000340].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000337,,,,,kisao:KISAO_0000339,,2011-06-07,AZ,,,,p-method|p-FEM,The p version of 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] uses a fixed mesh but increases the polynomial degree p to increase accuracy. Can be considered as a special case of the h-p version [http://identifiers.org/biomodels.kisao/KISAO_0000340].,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000084,nonnegative Poisson tau-leaping method,modified poisson tau-leaping,"The explicit tau-leaping procedure attempts to speed up the stochastic simulation of a chemically reacting system by approximating the number of firings of each reaction channel during a chosen time increment Tau as a Poisson random variable. Since the Poisson random variable can have arbitrarily large sample values, there is always the possibility that this procedure will cause one or more reaction channels to fire so many times during Tau that the population of some reactant species will be driven negative. Two recent papers have shown how that unacceptable occurrence can be avoided by replacing the Poisson random variables with binomial random variables, whose values are naturally bounded. This paper describes a modified Poisson tau-leaping procedure that also avoids negative populations, but is easier to implement than the binomial procedure. The new Poisson procedure also introduces a second control parameter, whose value essentially dials the procedure from the original Poisson tau-leaping at one extreme to the exact stochastic simulation algorithm at the other; therefore, the modified Poisson procedure will generally be more accurate than the original Poisson procedure [http://identifiers.org/biomodels.kisao/KISAO_0000040].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000084,,,,,,,modified poisson tau-leaping,"The explicit tau-leaping procedure attempts to speed up the stochastic simulation of a chemically reacting system by approximating the number of firings of each reaction channel during a chosen time increment Tau as a Poisson random variable. Since the Poisson random variable can have arbitrarily large sample values, there is always the possibility that this procedure will cause one or more reaction channels to fire so many times during Tau that the population of some reactant species will be driven negative. Two recent papers have shown how that unacceptable occurrence can be avoided by replacing the Poisson random variables with binomial random variables, whose values are naturally bounded. This paper describes a modified Poisson tau-leaping procedure that also avoids negative populations, but is easier to implement than the binomial procedure. The new Poisson procedure also introduces a second control parameter, whose value essentially dials the procedure from the original Poisson tau-leaping at one extreme to the exact stochastic simulation algorithm at the other; therefore, the modified Poisson procedure will generally be more accurate than the original Poisson procedure [http://identifiers.org/biomodels.kisao/KISAO_0000040].",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000263,NFSim agent-based simulation method,,"A generalization a rule-based version of 'Gillespie's direct method' (SSA) [http://identifiers.org/biomodels.kisao/KISAO_0000029]. The method is guaranteed to produce the same results as the exact SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] by cycling over three primary steps. First, NFsim calculates the probability or propensity for each rule to take effect given the current molecular states. Second, it samples the time to the next reaction event and selects the corresponding reaction rule. Finally, NFsim executes the selected reaction by applying the rule and updating the molecular agents accordingly.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000017,,,,,kisao:KISAO_0000263,,2011-04-07,AZ,,https://identifiers.org/biosimulators/bionetgen,,,"A generalization a rule-based version of 'Gillespie's direct method' (SSA) [http://identifiers.org/biomodels.kisao/KISAO_0000029]. The method is guaranteed to produce the same results as the exact SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] by cycling over three primary steps. First, NFsim calculates the probability or propensity for each rule to take effect given the current molecular states. Second, it samples the time to the next reaction event and selects the corresponding reaction rule. Finally, NFsim executes the selected reaction by applying the rule and updating the molecular agents accordingly.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000561,Pahle hybrid Gibson-Bruck Next Reaction method/Runge-Kutta method,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. A 4th-order Runge-Kutta method is used to numerically integrate the deterministic part of the system. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000231,,,,,kisao:KISAO_0000561,,2020-10-29,JRK,,https://identifiers.org/biosimulators/copasi,,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. A 4th-order Runge-Kutta method is used to numerically integrate the deterministic part of the system. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000263,NFSim agent-based simulation method,,"A generalization a rule-based version of 'Gillespie's direct method' (SSA) [http://identifiers.org/biomodels.kisao/KISAO_0000029]. The method is guaranteed to produce the same results as the exact SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] by cycling over three primary steps. First, NFsim calculates the probability or propensity for each rule to take effect given the current molecular states. Second, it samples the time to the next reaction event and selects the corresponding reaction rule. Finally, NFsim executes the selected reaction by applying the rule and updating the molecular agents accordingly.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000017,,,,,kisao:KISAO_0000263,,2011-04-07,AZ,,http://identifiers.org/biosimulators/bionetgen,,,"A generalization a rule-based version of 'Gillespie's direct method' (SSA) [http://identifiers.org/biomodels.kisao/KISAO_0000029]. The method is guaranteed to produce the same results as the exact SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] by cycling over three primary steps. First, NFsim calculates the probability or propensity for each rule to take effect given the current molecular states. Second, it samples the time to the next reaction event and selects the corresponding reaction rule. Finally, NFsim executes the selected reaction by applying the rule and updating the molecular agents accordingly.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000561,Pahle hybrid Gibson-Bruck Next Reaction method/Runge-Kutta method,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. A 4th-order Runge-Kutta method is used to numerically integrate the deterministic part of the system. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000231,,,,,kisao:KISAO_0000561,,2020-10-29,JRK,,http://identifiers.org/biosimulators/copasi,,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. A 4th-order Runge-Kutta method is used to numerically integrate the deterministic part of the system. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000398,iterative method for solving a system of linear equations,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000624,,,,,kisao:KISAO_0000398,,2011-07-19,AZ,,,true,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000365,NDSolve method,,"The Mathematica computation system function NDSolve is a general numerical differential equation solver. It can handle a wide range of ordinary differential equations as well as some partial differential equations. NDSolve can also solve some differential-algebraic equations, which are typically a mix of differential and algebraic equations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000365,,2011-06-16,AZ,,Mathematica,,,"The Mathematica computation system function NDSolve is a general numerical differential equation solver. It can handle a wide range of ordinary differential equations as well as some partial differential equations. NDSolve can also solve some differential-algebraic equations, which are typically a mix of differential and algebraic equations.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000838,concentration,,"The intensive quantity concentration, or, the amount of the entity with respect to the entity in which it resides.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000838,,06-03-2021,LPS,,,,,"The intensive quantity concentration, or, the amount of the entity with respect to the entity in which it resides.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000102,spatial description,,"Algorithm, possessing this characteristic, takes into account the location of the reacting components.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000102,,2008-07-08,NLN,,,,,"Algorithm, possessing this characteristic, takes into account the location of the reacting components.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000600,Hybrid Adaptive Gibson - Milstein Method,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000600,,2021-01-13,MLB|JRK,,https://identifiers.org/biosimulators/vcell,,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000600,hybrid adaptive Gibson - Milstein method,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000600,,2021-01-13,MLB|JRK,,http://identifiers.org/biosimulators/vcell,,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000467,maximum step size,,"An upper limit, in the units of the bound variable over which a numerical integration is being performed, that an adaptive step size numerical integration algorithm should take.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000467,,2013-07-05,AZ,,,,,"An upper limit, in the units of the bound variable over which a numerical integration is being performed, that an adaptive step size numerical integration algorithm should take.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000609,"Embedded Runge-Kutta Prince-Dormand (8,9) method",RK8PD,An embedded Runge-Kutta integrator of order 8(9).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000609,,2021-01-25,JRK,,https://identifiers.org/biosimulators/ibiosim,,RK8PD,An embedded Runge-Kutta integrator of order 8(9).,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000593,MOMA,Minimization of Metabolic Adjustment,Minimization Of Metabolic Adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA identifies the closest point in the permissible flux space of the knockout to the wild-type flux vector by solving an optimization problem.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000593,,2020-10-29,JRK,,,true,Minimization of Metabolic Adjustment,Minimization Of Metabolic Adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA identifies the closest point in the permissible flux space of the knockout to the wild-type flux vector by solving an optimization problem.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000609,"embedded Runge-Kutta Prince-Dormand (8,9) method",RK8PD,An embedded Runge-Kutta integrator of order 8(9).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000609,,2021-01-25,JRK,,http://identifiers.org/biosimulators/ibiosim,,RK8PD,An embedded Runge-Kutta integrator of order 8(9).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000593,MOMA,Minimization of Metabolic Adjustment|minimization of metabolic adjustment,minimization of metabolic adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA identifies the closest point in the permissible flux space of the knockout to the wild-type flux vector by solving an optimization problem.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000593,,2020-10-29,JRK,,,true,Minimization of Metabolic Adjustment|minimization of metabolic adjustment,minimization of metabolic adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. MOMA identifies the closest point in the permissible flux space of the knockout to the wild-type flux vector by solving an optimization problem.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000237,approximate solution,,"Approximation algorithms are algorithms used to find approximate solutions to optimization problems. Approximation algorithms are often associated with NP-hard problems; since it is unlikely that there can ever be efficient polynomial time exact algorithms solving NP-hard problems, one settles for polynomial time sub-optimal solutions. Unlike heuristics, which usually only find reasonably good solutions reasonably fast, one wants provable solution quality and provable run time bounds. Ideally, the approximation is optimal up to a small constant factor (for instance within 5% of the optimal solution). Approximation algorithms are increasingly being used for problems where exact polynomial-time algorithms are known but are too expensive due to the input size.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000235,,,,,kisao:KISAO_0000237,,,,,,,,"Approximation algorithms are algorithms used to find approximate solutions to optimization problems. Approximation algorithms are often associated with NP-hard problems; since it is unlikely that there can ever be efficient polynomial time exact algorithms solving NP-hard problems, one settles for polynomial time sub-optimal solutions. Unlike heuristics, which usually only find reasonably good solutions reasonably fast, one wants provable solution quality and provable run time bounds. Ideally, the approximation is optimal up to a small constant factor (for instance within 5% of the optimal solution). Approximation algorithms are increasingly being used for problems where exact polynomial-time algorithms are known but are too expensive due to the input size.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000831,model and simulation property,,A variable of a model or simulation.,false,,,http://www.w3.org/2002/07/owl#Thing,,,,,kisao:KISAO_0000831,,06-03-2021,LPS,,,true,,A variable of a model or simulation.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000613,Stochastic simulation algorithm with normally-distributed next reaction times,NMC,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000613,,2021-01-25,JRK,,https://identifiers.org/biosimulators/ibiosim,,NMC,,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000646,propensity,,"Tendency of an event such as of the firing of a reaction in the Stochastic Simulation Algorithm (SSA, KISAO_0000029).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000646,,2021-06-04,JRK,,,,,"Tendency of an event such as of the firing of a reaction in the Stochastic Simulation Algorithm (SSA, KISAO_0000029).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000679,maximum time for approximation,,Maximum amount of time to spend approximating an analysis.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000681,,,,,kisao:KISAO_0000679,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Maximum amount of time to spend approximating an analysis.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000613,stochastic simulation algorithm with normally-distributed next reaction times,NMC,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000613,,2021-01-25,JRK,,http://identifiers.org/biosimulators/ibiosim,,NMC,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000646,propensity,,"Tendency of an event such as of the firing of a reaction in the stochastic simulation algorithm (SSA, KISAO_0000029).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000646,,2021-06-04,JRK,,,,,"Tendency of an event such as of the firing of a reaction in the stochastic simulation algorithm (SSA, KISAO_0000029).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000529,parallelism,,Number of parallel processes to use.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000529,,,,,,,,Number of parallel processes to use.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000241,Gillespie-like method,,Stochastic simulation algorithm using an approach alike the one described in Gillespie's papers of 1976 and 1977.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000319,,,,,kisao:KISAO_0000241,,,,,,true,,Stochastic simulation algorithm using an approach alike the one described in Gillespie's papers of 1976 and 1977.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000274,first-passage Monte Carlo algorithm,asynchronous event-driven diffusion Monte Carlo|AED diffusion kinetic Monte Carlo method|AED DKMC,"We present a novel Monte Carlo algorithm for N diffusing finite particles that react on collisions. Using the theory of first-passage processes and time dependent Green's functions, we break the difficult N-body problem into independent single- and two-body propagations circumventing numerous diffusion hops used in standard Monte Carlo simulations. The new algorithm is exact, extremely efficient, and applicable to many important physical situations in arbitrary integer dimensions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000319,,,,,kisao:KISAO_0000274,,2011-05-05,AZ,,,,asynchronous event-driven diffusion Monte Carlo|AED diffusion kinetic Monte Carlo method|AED DKMC,"We present a novel Monte Carlo algorithm for N diffusing finite particles that react on collisions. Using the theory of first-passage processes and time dependent Green's functions, we break the difficult N-body problem into independent single- and two-body propagations circumventing numerous diffusion hops used in standard Monte Carlo simulations. The new algorithm is exact, extremely efficient, and applicable to many important physical situations in arbitrary integer dimensions.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000471,local optimization algorithm,local optimiation method,"A local optimization algorithm is an optimisation algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000470] that only finds a local optimum of a function. If several optima exist for the function, it usually depends on the starting values for the variables which optimum is found.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000470,,,,,kisao:KISAO_0000471,,2015-04-23,AZ,,,,local optimiation method,"A local optimization algorithm is an optimisation algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000470] that only finds a local optimum of a function. If several optima exist for the function, it usually depends on the starting values for the variables which optimum is found.",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000809,Reduced Jacobian matrix,,The reduced Jacobian matrix. The dimensions are species by species.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000809,,06-03-2021,LPS,,,,,The reduced Jacobian matrix. The dimensions are species by species.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000376,linearity of equation,,"Linear differential equations are of the form Ly = f, where the differential operator L is a linear operator, y is the unknown function, and the right hand side f is a given function of the same nature as y.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000370,,,,,kisao:KISAO_0000376,,2011-07-19,AZ,,,,,"Linear differential equations are of the form Ly = f, where the differential operator L is a linear operator, y is the unknown function, and the right hand side f is a given function of the same nature as y.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000407,steady state method,,A method looking for a steady state of a dynamic system.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000407,,2012-01-17,AZ,,,true,,A method looking for a steady state of a dynamic system.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000407,steady state method,,A method looking for a steady state of a dynamic system.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000407,,2012-01-17,AZ,,,true,,A method looking for a steady state of a dynamic system.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000650,sensitivity,,"The sensitivity of a variable to another variable, such as the derivative a variable with respect to another.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000650,,2021-06-04,JRK,,,true,,"The sensitivity of a variable to another variable, such as the derivative a variable with respect to another.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000509,evolutionary strategy,SRES|evolutionary strategies with stochastic ranking,"Evolutionary Strategies with Stochastic Ranking (SRES) is similar to Evolutionary Programming. However, a parent has multiple offsprings during each generation. Each offspring will contain a recombination of genes with another parent and additional mutations. The algorithm assures that each parameter value will be within its boundaries. But constraints to the solutions may be violated.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000508,,,,,kisao:KISAO_0000509,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,SRES|evolutionary strategies with stochastic ranking,"Evolutionary Strategies with Stochastic Ranking (SRES) is similar to Evolutionary Programming. However, a parent has multiple offsprings during each generation. Each offspring will contain a recombination of genes with another parent and additional mutations. The algorithm assures that each parameter value will be within its boundaries. But constraints to the solutions may be violated.",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000805,Elasticity coefficient (unscaled),,An unscaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000805,,06-03-2021,LPS,,,,,An unscaled elasticity coefficient of any reaction with respect to an independent element (such as a global parameter or boundary species).,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000535,VODE,"Real-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation|DVODE",VODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000535,,2020-10-29,JRK,,Odespy|https://identifiers.org/biosimulators/gillespy2|deSolve|SciPy,,"Real-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation|DVODE",VODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000568,NLEQ1,Numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems|Newton-type method for solveing non-linear (NL) equations (EQ),"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. +http://www.biomodels.net/kisao/KISAO#KISAO_0000683,relative tolerance for approximation,,Relatative tolerance for an alternative approximate solution to an exact solution which could not be found.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000683,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Relatative tolerance for an alternative approximate solution to an exact solution which could not be found.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000509,evolutionary strategy,SRES|evolutionary strategies with stochastic ranking,"Evolutionary Strategies with Stochastic Ranking (SRES) is similar to Evolutionary Programming. However, a parent has multiple offsprings during each generation. Each offspring will contain a recombination of genes with another parent and additional mutations. The algorithm assures that each parameter value will be within its boundaries. But constraints to the solutions may be violated.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000508,,,,,kisao:KISAO_0000509,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,SRES|evolutionary strategies with stochastic ranking,"Evolutionary Strategies with Stochastic Ranking (SRES) is similar to Evolutionary Programming. However, a parent has multiple offsprings during each generation. Each offspring will contain a recombination of genes with another parent and additional mutations. The algorithm assures that each parameter value will be within its boundaries. But constraints to the solutions may be violated.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000535,VODE,"Real-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation|real-valued variable-coefficient ordinary differential equation solver, with fixed-leading-coefficient implementation|DVODE",VODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000535,,2020-10-29,JRK,,Odespy|http://identifiers.org/biosimulators/gillespy2|deSolve|SciPy,,"Real-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation|real-valued variable-coefficient ordinary differential equation solver, with fixed-leading-coefficient implementation|DVODE",VODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000568,NLEQ1,Newton-type method for solveing non-linear (NL) equations (EQ)|numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems,"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. -Global Newton method with error oriented convergence criterion; arbitrary selection of direct linear equation solver.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000568,,2020-10-29,JRK,,https://identifiers.org/biosimulators/tellurium,,Numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems|Newton-type method for solveing non-linear (NL) equations (EQ),"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. +Global Newton method with error oriented convergence criterion; arbitrary selection of direct linear equation solver.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000568,,2020-10-29,JRK,,http://identifiers.org/biosimulators/tellurium,,Newton-type method for solveing non-linear (NL) equations (EQ)|numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems,"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. Global Newton method with error oriented convergence criterion; arbitrary selection of direct linear equation solver.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000089,LSODAR,ordinary differential equation solver for stiff or non-stiff systems with root finding|Livermore solver for ordinary differential equations with automatic method switching and root finding,"LSODAR is a variant of LSODA [http://identifiers.org/biomodels.kisao/KISAO_0000088] with a root finding capability added. Thus it solves problems dy/dt = f with dense or banded Jacobian and automatic method selection, and at the same time, it finds the roots of any of a set of given functions of the form g(t,y). This is often useful for finding stop conditions, or for finding points at which a switch is to be made in the function f.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000089,,2007-10-27,dk,,,,ordinary differential equation solver for stiff or non-stiff systems with root finding|Livermore solver for ordinary differential equations with automatic method switching and root finding,"LSODAR is a variant of LSODA [http://identifiers.org/biomodels.kisao/KISAO_0000088] with a root finding capability added. Thus it solves problems dy/dt = f with dense or banded Jacobian and automatic method selection, and at the same time, it finds the roots of any of a set of given functions of the form g(t,y). This is often useful for finding stop conditions, or for finding points at which a switch is to be made in the function f.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000305,Verner method,Verner's method,The first high order (6(5)) embedded Runge-Kutta formulas that avoid the drawback of giving identically zero error estimates for quadrature problems y' = f(x) were constructed by Verner in 1978.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000305,,2011-05-10,AZ,,,,Verner's method,The first high order (6(5)) embedded Runge-Kutta formulas that avoid the drawback of giving identically zero error estimates for quadrature problems y' = f(x) were constructed by Verner in 1978.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000056,Smoluchowski equation based method,,Method based on the Smoluchowski equation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000056,,2007-10-29,dk,,,true,,Method based on the Smoluchowski equation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000546,convex optimization algorithm,,Optimization of a convex function over a convex set. Convex optimization is subclass of global optimization because conveness gaurantees that each local optimum is a global optimum.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000546,,2020-10-29,JRK,,,,,Optimization of a convex function over a convex set. Convex optimization is subclass of global optimization because conveness gaurantees that each local optimum is a global optimum.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000513,NL2SOL,adaptive nonlinear least-squares algorithm,"The NL2SOL method is based on an adaptive nonlinear least-squares algorithm, devised by Dennis and colleagues. For problems with large number of residuals, this algorithm is known to be more reliable than Gauss-Newton or Levenberg-Marquardt method and more efficient than the secant or variable metric algorithms that are intended for general function minimization.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000513,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,adaptive nonlinear least-squares algorithm,"The NL2SOL method is based on an adaptive nonlinear least-squares algorithm, devised by Dennis and colleagues. For problems with large number of residuals, this algorithm is known to be more reliable than Gauss-Newton or Levenberg-Marquardt method and more efficient than the secant or variable metric algorithms that are intended for general function minimization.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000572,level of superimposed noise,noise level,Standard deviation of the Gaussian noise which is added to each prediction.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000572,,2020-10-29,JRK,,https://identifiers.org/biosimulators/boolnet,,noise level,Standard deviation of the Gaussian noise which is added to each prediction.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000816,Link matrix,,The link matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:KISAO_0000816,,06-03-2021,LPS,,,,,The link matrix of a model.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000579,Linear MOMA,Linear Minimization of Metabolic Adjustment,"Linear Minimization Of Metabolic Adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. Linear MOMA employs linear programming to identify the closest point (in terms of its L1 norm) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem +http://www.biomodels.net/kisao/KISAO#KISAO_0000513,NL2SOL,adaptive nonlinear least-squares algorithm,"The NL2SOL method is based on an adaptive nonlinear least-squares algorithm, devised by Dennis and colleagues. For problems with large number of residuals, this algorithm is known to be more reliable than Gauss-Newton or Levenberg-Marquardt method and more efficient than the secant or variable metric algorithms that are intended for general function minimization.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000513,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,adaptive nonlinear least-squares algorithm,"The NL2SOL method is based on an adaptive nonlinear least-squares algorithm, devised by Dennis and colleagues. For problems with large number of residuals, this algorithm is known to be more reliable than Gauss-Newton or Levenberg-Marquardt method and more efficient than the secant or variable metric algorithms that are intended for general function minimization.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000572,level of superimposed noise,noise level,Standard deviation of the Gaussian noise which is added to each prediction.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000572,,2020-10-29,JRK,,http://identifiers.org/biosimulators/boolnet,,noise level,Standard deviation of the Gaussian noise which is added to each prediction.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000849,cumulative sum,,The cumulative sum of a set of values. If the values contain NaN the cumulative sum is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000849,,2021-10-09,MK,,,,,The cumulative sum of a set of values. If the values contain NaN the cumulative sum is NaN.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000816,link matrix,,The link matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000816,,06-03-2021,LPS,,,,,The link matrix of a model.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000579,linear MOMA,linear minimization of metabolic adjustment|Linear Minimization of Metabolic Adjustment,"Linear minimization of metabolic adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. Linear MOMA employs linear programming to identify the closest point (in terms of its L1 norm) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem -Min sum(|fluxAi - fluxBi|)",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000593,,,,,kisao:KISAO_0000579,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cobratoolbox|https://identifiers.org/biosimulators/optflux,,Linear Minimization of Metabolic Adjustment,"Linear Minimization Of Metabolic Adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. Linear MOMA employs linear programming to identify the closest point (in terms of its L1 norm) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem +Min sum(|fluxAi - fluxBi|)",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000593,,,,,kisao:KISAO_0000579,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cobratoolbox|http://identifiers.org/biosimulators/optflux,,linear minimization of metabolic adjustment|Linear Minimization of Metabolic Adjustment,"Linear minimization of metabolic adjustment (MOMA) is an extension of FBA for the prediction of flux distributions in gene knockouts. Linear MOMA employs linear programming to identify the closest point (in terms of its L1 norm) in the permissible flux space of the knockout to the wild-type flux vector by solving the optimization problem Min sum(|fluxAi - fluxBi|)",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000411,Newton-like method,,"A 'Newton-type method' [http://identifiers.org/biomodels.kisao/KISAO_0000408] which is characterized by the fact that, in finite dimension, the Jacodian matrices are either replaced by some fixed 'close by' Jacobian F'(z) with z not equal to the initial guess x[0], or by some approximation so that: M'(x[0])deltax[k]=-F(x[k]), x[k+1]=x[k]+deltax[k], k=0,1,...",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000411,,2012-01-18,AZ,,,,,"A 'Newton-type method' [http://identifiers.org/biomodels.kisao/KISAO_0000408] which is characterized by the fact that, in finite dimension, the Jacodian matrices are either replaced by some fixed 'close by' Jacobian F'(z) with z not equal to the initial guess x[0], or by some approximation so that: M'(x[0])deltax[k]=-F(x[k]), x[k+1]=x[k]+deltax[k], k=0,1,...",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000248,tau-leaping delta,,Tau-leaping delta specifies how close two symmetric transition rates must be before we classify them as in partial-equilibrium. Only applies to the implicit tau routine [http://identifiers.org/biomodels.kisao/KISAO_0000045].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000248,,,,,,,,Tau-leaping delta specifies how close two symmetric transition rates must be before we classify them as in partial-equilibrium. Only applies to the implicit tau routine [http://identifiers.org/biomodels.kisao/KISAO_0000045].,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000842,standard deviation,,The standard deviation of a set of values. If the values contain NaN the standard deviation is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000842,,2021-10-09,MK,,,,,The standard deviation of a set of values. If the values contain NaN the standard deviation is NaN.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000341,mixed finite element method,,A 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] in which both stress and displacement fields are approximated as primary variables.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000337,,,,,kisao:KISAO_0000341,,2011-06-07,AZ,,,,,A 'finite element method' [http://identifiers.org/biomodels.kisao/KISAO_0000337] in which both stress and displacement fields are approximated as primary variables.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000644,maximum flux,,"Maximum possible flux such as computed by flux variability analysis (FVA, KISAO_0000526).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000639,,,,,kisao:KISAO_0000644,,2021-06-04,JRK,,,,,"Maximum possible flux such as computed by flux variability analysis (FVA, KISAO_0000526).",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000611,Incremental stochastic simulation algorithm,iSSA,Performs local averaging over small time-intervals to compute statistics on typical behavior.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000611,,,JRK,,https://identifers.org/biosimulators/ibiosim,,iSSA,Performs local averaging over small time-intervals to compute statistics on typical behavior.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000611,incremental stochastic simulation algorithm,iSSA,Performs local averaging over small time-intervals to compute statistics on typical behavior.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000611,,,JRK,,https://identifers.org/biosimulators/ibiosim,,iSSA,Performs local averaging over small time-intervals to compute statistics on typical behavior.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000500,SOA-DFBA,SOA|static optimization approach dynamic flux balance analysis,"Dynamic Flux Balance Analysis (DFBA) [http://identifiers.org/biomodels.kisao/KISAO_0000499] couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches. The static optimization approach (SOA) uses the Euler forward method [http://identifiers.org/biomodels.kisao/KISAO_0000030], solving the embedded LPs at each time step. The FBA fluxes are assumed to be constant during the time step.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000499,,,,,kisao:KISAO_0000500,,2017-09-12,AZ,,DFBAlab,,SOA|static optimization approach dynamic flux balance analysis,"Dynamic Flux Balance Analysis (DFBA) [http://identifiers.org/biomodels.kisao/KISAO_0000499] couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches. The static optimization approach (SOA) uses the Euler forward method [http://identifiers.org/biomodels.kisao/KISAO_0000030], solving the embedded LPs at each time step. The FBA fluxes are assumed to be constant during the time step.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000405,type of differential equation,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000370,,,,,kisao:KISAO_0000405,,2011-07-19,AZ,,,true,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000657,sequential logical simulation method,,"Qualitative (logical) models specify the evolution rules of their components. In the case of a sequential updating, nodes are updated sequentially in a pre-determined deterministic order.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000448,,,,,kisao:KISAO_0000657,,2021-07-08,JRK,,,,,"Qualitative (logical) models specify the evolution rules of their components. In the case of a sequential updating, nodes are updated sequentially in a pre-determined deterministic order.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000380,Richardson extrapolation based method,,"A method based on ideas of Richardson extrapolation, which is a process for obtaining increased accuracy in a discretized approximation by extrapolating results from coarse discretizations to an arbitrarily fine one.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000377,,,,,kisao:KISAO_0000380,,2011-07-01,AZ,,,true,,"A method based on ideas of Richardson extrapolation, which is a process for obtaining increased accuracy in a discretized approximation by extrapolating results from coarse discretizations to an arbitrarily fine one.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000285,finite volume method,FVM,"The finite volume method is a method for representing and evaluating partial differential equations in the form of algebraic equations, which attempts to emulate continuous conservation laws of physics.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000285,,2011-05-09,AZ,,https://identifiers.org/biosimulators/vcell,,FVM,"The finite volume method is a method for representing and evaluating partial differential equations in the form of algebraic equations, which attempts to emulate continuous conservation laws of physics.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000507,genetic algorithm SR,genetic algorithm with stochastic ranking,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000506,,,,,kisao:KISAO_0000507,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,genetic algorithm with stochastic ranking,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000285,finite volume method,FVM,"The finite volume method is a method for representing and evaluating partial differential equations in the form of algebraic equations, which attempts to emulate continuous conservation laws of physics.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000285,,2011-05-09,AZ,,http://identifiers.org/biosimulators/vcell,,FVM,"The finite volume method is a method for representing and evaluating partial differential equations in the form of algebraic equations, which attempts to emulate continuous conservation laws of physics.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000507,genetic algorithm SR,genetic algorithm with stochastic ranking,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000506,,,,,kisao:KISAO_0000507,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,genetic algorithm with stochastic ranking,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000000,modelling and simulation algorithm,modeling and simulation algorithm,Algorithm used to instantiate a simulation from a mathematical model.,false,,,http://www.w3.org/2002/07/owl#Thing,,,,,kisao:KISAO_0000000,,2008-05-26,dk,,,true,modeling and simulation algorithm,Algorithm used to instantiate a simulation from a mathematical model.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000252,partitioning control parameter,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000252,,,Parameter describing partitioning of the system.,,,true,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000814,flux control coefficient matrix (unscaled),,The unscaled flux control coefficient matrix. The dimensions are reactions by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000814,,06-03-2021,LPS,,,,,The unscaled flux control coefficient matrix. The dimensions are reactions by reactions.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000694,ODE solver,ordinary differential equation solver,An ODE solver is the general category of packages such as CVODE-like methods [http://identifiers.org/biomodels.kisao/KISAO_0000433] or Livermore solvers [http://identifiers.org/biomodels.kisao/KISAO_0000094] that solve systems of ordinary differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000694,,2023-05-19,lps,,,false,ordinary differential equation solver,An ODE solver is the general category of packages such as CVODE-like methods [http://identifiers.org/biomodels.kisao/KISAO_0000433] or Livermore solvers [http://identifiers.org/biomodels.kisao/KISAO_0000094] that solve systems of ordinary differential equations.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000544,IDAS,implicit differential-algebraic solver with sensitivity analysis,"IDAS solves real differential-algebraic systems in N-space, in the general form F(t,y,y')=0, y(t0)=y0, y'(t0)=y'0 with sensitivity analysis.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000432,,,,,kisao:KISAO_0000544,,2020-10-29,JRK,,SUNDIALS,,implicit differential-algebraic solver with sensitivity analysis,"IDAS solves real differential-algebraic systems in N-space, in the general form F(t,y,y')=0, y(t0)=y0, y'(t0)=y'0 with sensitivity analysis.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000661,logical model trap space identification method,,"Method for determining the trap spaces, or stable motifs or symbolic stable states, of a regulatory graph,",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000658,,,,,kisao:KISAO_0000661,,2021-07-08,JRK,,,true,,"Method for determining the trap spaces, or stable motifs or symbolic stable states, of a regulatory graph,",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000418,N-way partial least squares regression method,N-way PLSR|N-PLS|N-way partial least squares method,"Multivariate regression method that can be used on N-way data. Based on Partial Least Squares Regression (PLSR) [http://identifiers.org/biomodels.kisao/KISAO_0000416], which is a regression method based on estimated latent variables. PLSR is related to Principal Component Analysis (PCA) and Principal Component Regression (PCR).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000423,,,,,kisao:KISAO_0000418,,2012-01-18,AZ,,,,N-way PLSR|N-PLS|N-way partial least squares method,"Multivariate regression method that can be used on N-way data. Based on Partial Least Squares Regression (PLSR) [http://identifiers.org/biomodels.kisao/KISAO_0000416], which is a regression method based on estimated latent variables. PLSR is related to Principal Component Analysis (PCA) and Principal Component Regression (PCR).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000511,steepest descent,,Steepest descent is an optimization method that follows the direction of steepest descent on the hyper-surface of the objective function to find a local minimum. The direction of steepest descent is defined by the negative of the gradient of the objective function.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000511,,2019-01-18,AZ,,,,,Steepest descent is an optimization method that follows the direction of steepest descent on the hyper-surface of the objective function to find a local minimum. The direction of steepest descent is defined by the negative of the gradient of the objective function.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000348,extended finite element method,X-FEM|XFEM,A numerical method to model arbitrary discontinuities in continuous bodies that does not require the mesh to conform to the discontinuities nor significant mesh refinement near singularities. In X-FEM the standard finite element approximation [http://identifiers.org/biomodels.kisao/KISAO_0000337] is enriched and the approximation space is extended by an additional family of functions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000348,,2011-06-09,AZ,,,,X-FEM|XFEM,A numerical method to model arbitrary discontinuities in continuous bodies that does not require the mesh to conform to the discontinuities nor significant mesh refinement near singularities. In X-FEM the standard finite element approximation [http://identifiers.org/biomodels.kisao/KISAO_0000337] is enriched and the approximation space is extended by an additional family of functions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000315,lattice gas automata,LGCA|lattice gas cellular automata|LGA,"Lattice gas automata methods are a series of cellular automata methods used to simulate fluid flows. From the LGCA, it is possible to derive the macroscopic Navier-Stokes equations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000068,,,,,kisao:KISAO_0000315,,2011-05-23,AZ,,,,LGCA|lattice gas cellular automata|LGA,"Lattice gas automata methods are a series of cellular automata methods used to simulate fluid flows. From the LGCA, it is possible to derive the macroscopic Navier-Stokes equations.",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000804,Elasticity matrix (unscaled),,The unscaled elasticity matrix. The dimensions are reactions by species.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000804,,06-03-2021,LPS,,,,,The unscaled elasticity matrix. The dimensions are reactions by species.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000827,standard error,,The standard error of a set of values.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000827,,06-03-2021,LPS,,,,,The standard error of a set of values.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000827,standard error ignoring NaN,,"The standard error of a set of values, ignoring NaN entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000827,,2021-06-03,"LPS, MK",,,,,"The standard error of a set of values, ignoring NaN entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000422,number of N-way partial least squares regression factors,number of factors,Parameter of 'N-way partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000369] describing the number of factors to compute.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000422,,2012-01-18,AZ,,,,number of factors,Parameter of 'N-way partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000369] describing the number of factors to compute.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000029,Gillespie direct algorithm,Doob-Gillespie method|stochastic simulation algorithm|SSA|Gillespie's algorithm|Gillespie's direct method|DM,"Stochastic simulation algorithm using the reaction probability density function (next-reaction density function), giving the probability that the next reaction will happen in a given time interval. To choose the next reaction to fire, the algorithm directly and separately calculates the identity of the reaction and the time it will fire.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000241,,,,,kisao:KISAO_0000029,,2007-11-10,dk,,https://identifiers.org/biosimulators/bionetgen|ByoDyn|Cain|https://identifiers.org/biosimulators/ibiosim|BetaWB,,Doob-Gillespie method|stochastic simulation algorithm|SSA|Gillespie's algorithm|Gillespie's direct method|DM,"Stochastic simulation algorithm using the reaction probability density function (next-reaction density function), giving the probability that the next reaction will happen in a given time interval. To choose the next reaction to fire, the algorithm directly and separately calculates the identity of the reaction and the time it will fire.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000629,Null,None,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000628,,,,,kisao:KISAO_0000629,,2021-06-01,JRK,,,,None,,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000557,absolute steady-state tolerance,,Absolute error tolerance of the steady-state.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000211,,,,,kisao:KISAO_0000557,,2020-10-29,JRK,,https://identifiers.org/biosimulators/amici|SUNDIALS,,,Absolute error tolerance of the steady-state.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000029,Gillespie direct algorithm,Doob-Gillespie method|stochastic simulation algorithm|SSA|Gillespie's algorithm|Gillespie's direct method|DM,"Stochastic simulation algorithm using the reaction probability density function (next-reaction density function), giving the probability that the next reaction will happen in a given time interval. To choose the next reaction to fire, the algorithm directly and separately calculates the identity of the reaction and the time it will fire.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000241,,,,,kisao:KISAO_0000029,,2007-11-10,dk,,ByoDyn|Cain|http://identifiers.org/biosimulators/ibiosim|http://identifiers.org/biosimulators/bionetgen|BetaWB,,Doob-Gillespie method|stochastic simulation algorithm|SSA|Gillespie's algorithm|Gillespie's direct method|DM,"Stochastic simulation algorithm using the reaction probability density function (next-reaction density function), giving the probability that the next reaction will happen in a given time interval. To choose the next reaction to fire, the algorithm directly and separately calculates the identity of the reaction and the time it will fire.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000629,null,none,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000628,,,,,kisao:KISAO_0000629,,2021-06-01,JRK,,,,none,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000557,absolute steady-state tolerance,,Absolute error tolerance of the steady-state.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000211,,,,,kisao:KISAO_0000557,,2020-10-29,JRK,,http://identifiers.org/biosimulators/amici|SUNDIALS,,,Absolute error tolerance of the steady-state.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000487,minimum damping,,The damping factor is a variable for at least some steady state algorithms: roadrunner allows you to set the minimum value for this.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000487,,,,,Roadrunner,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000454,constant updating policy,,An updating policy that chooses a transition in a constant way.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000453,,,,,kisao:KISAO_0000454,,2013-01-28,AZ,,,,,An updating policy that chooses a transition in a constant way.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000082,k-alpha leaping method,,"Alternative to the tau-leaping [http://identifiers.org/biomodels.kisao/KISAO_0000039], where one leaps a fixed number of reaction-events.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000621,,,,,kisao:KISAO_0000082,,,,,,,,"Alternative to the tau-leaping [http://identifiers.org/biomodels.kisao/KISAO_0000039], where one leaps a fixed number of reaction-events.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000583,minimum order,,Minimum order of method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000594,,,,,kisao:KISAO_0000583,,2020-10-29,JRK,,https://identifiers.org/biosimulators/jsim,,,Minimum order of method.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000583,minimum order,,Minimum order of method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000594,,,,,kisao:KISAO_0000583,,2020-10-29,JRK,,http://identifiers.org/biosimulators/jsim,,,Minimum order of method.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000326,number of samples,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which determines the number of samples taken from the distribution.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000326,,2011-06-02,AZ,,,,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which determines the number of samples taken from the distribution.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000550,simplex method,Dantzig's simplex algorithm,"Approach to solving linear programming models by hand using slack variables, tableaus, and pivot variables as a means to finding the optimal solution of an optimization problem.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000547,,,,,kisao:KISAO_0000550,,2020-10-29,JRK,,CPLEX|GLPK|Gurobi,,Dantzig's simplex algorithm,"Approach to solving linear programming models by hand using slack variables, tableaus, and pivot variables as a means to finding the optimal solution of an optimization problem.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000505,particle swarm,,"The particle swarm optimization method suggested by Kennedy and Eberhart is inspired by a flock of birds or a school of fish searching for food. Each particle has a position Xi and a velocity Vi in the parameter space. Additionally, it remembers its best achieved objective value O and position Mi. Dependent on its own information and the position of its best neighbor (a random subset of particles of the swarm) a new velocity is calculated. With this information the position is updated.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000505,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,,"The particle swarm optimization method suggested by Kennedy and Eberhart is inspired by a flock of birds or a school of fish searching for food. Each particle has a position Xi and a velocity Vi in the parameter space. Additionally, it remembers its best achieved objective value O and position Mi. Dependent on its own information and the position of its best neighbor (a random subset of particles of the swarm) a new velocity is calculated. With this information the position is updated.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000622,flux balance method,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000622,,2021--04-27,JRK,,,true,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000505,particle swarm,,"The particle swarm optimization method suggested by Kennedy and Eberhart is inspired by a flock of birds or a school of fish searching for food. Each particle has a position Xi and a velocity Vi in the parameter space. Additionally, it remembers its best achieved objective value O and position Mi. Dependent on its own information and the position of its best neighbor (a random subset of particles of the swarm) a new velocity is calculated. With this information the position is updated.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000505,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,,"The particle swarm optimization method suggested by Kennedy and Eberhart is inspired by a flock of birds or a school of fish searching for food. Each particle has a position Xi and a velocity Vi in the parameter space. Additionally, it remembers its best achieved objective value O and position Mi. Dependent on its own information and the position of its best neighbor (a random subset of particles of the swarm) a new velocity is calculated. With this information the position is updated.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000622,flux balance method,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000685,,,,,kisao:KISAO_0000622,,2021--04-27,JRK,,,true,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000655,rate,,"Speed at which a process is occuring such as the temporal rate of a chemical reaction,",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000655,,06-04-2021,JRK,,,,,"Speed at which a process is occuring such as the temporal rate of a chemical reaction,",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000087,Dormand-Prince method,Prince-Dormand method|DOPRI,"Dormand-Prince is an explicit method for the numerical integration of ODES with a given initial value. -It is an 'embedded Runge-Kutta method' [http://identifiers.org/biomodels.kisao/KISAO_0000302] RK5 (4) which (a) has a 'small' principal truncation term in the Fifth order and (b) has an extended region of absolute stability.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000435,,,,,kisao:KISAO_0000087,,2007-11-12,dk,,https://identifiers.org/biosimulators/jsim|ECell3|https://identifiers.org/biosimulators/ibiosim|GSL|Matlab,,Prince-Dormand method|DOPRI,"Dormand-Prince is an explicit method for the numerical integration of ODES with a given initial value. +It is an 'embedded Runge-Kutta method' [http://identifiers.org/biomodels.kisao/KISAO_0000302] RK5 (4) which (a) has a 'small' principal truncation term in the Fifth order and (b) has an extended region of absolute stability.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000435,,,,,kisao:KISAO_0000087,,2007-11-12,dk,,ECell3|GSL|http://identifiers.org/biosimulators/ibiosim|Matlab|http://identifiers.org/biosimulators/jsim,,Prince-Dormand method|DOPRI,"Dormand-Prince is an explicit method for the numerical integration of ODES with a given initial value. It is an 'embedded Runge-Kutta method' [http://identifiers.org/biomodels.kisao/KISAO_0000302] RK5 (4) which (a) has a 'small' principal truncation term in the Fifth order and (b) has an extended region of absolute stability.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000283,IDA,solver for differential-algebraic equation systems|implicit differential-algebraic solver,"IDA solves real differential-algebraic systems in N-space, in the general form F(t,y,y')=0, y(t0)=y0, y'(t0)=y'0. At each step, a Newton iteration [http://identifiers.org/biomodels.kisao/KISAO_0000408] leads to linear systems Jx=b, which are solved by one of five methods - two direct (dense or band; serial version only) and three Krylov [http://identifiers.org/biomodels.kisao/KISAO_0000354] (GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000353], BiCGStab [http://identifiers.org/biomodels.kisao/KISAO_0000392], or TFQMR [http://identifiers.org/biomodels.kisao/KISAO_0000396]). -IDA is written in C, but derived from the package DASPK [http://identifiers.org/biomodels.kisao/KISAO_0000355] which is written in Fortran.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000432,,,,,kisao:KISAO_0000283,,2011-05-09,AZ,,https://identifiers.org/biosimulators/vcell|SUNDIALS|https://identifiers.org/biosimulators/opencor,,solver for differential-algebraic equation systems|implicit differential-algebraic solver,"IDA solves real differential-algebraic systems in N-space, in the general form F(t,y,y')=0, y(t0)=y0, y'(t0)=y'0. At each step, a Newton iteration [http://identifiers.org/biomodels.kisao/KISAO_0000408] leads to linear systems Jx=b, which are solved by one of five methods - two direct (dense or band; serial version only) and three Krylov [http://identifiers.org/biomodels.kisao/KISAO_0000354] (GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000353], BiCGStab [http://identifiers.org/biomodels.kisao/KISAO_0000392], or TFQMR [http://identifiers.org/biomodels.kisao/KISAO_0000396]). +IDA is written in C, but derived from the package DASPK [http://identifiers.org/biomodels.kisao/KISAO_0000355] which is written in Fortran.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000432,,,,,kisao:KISAO_0000283,,2011-05-09,AZ,,http://identifiers.org/biosimulators/opencor|http://identifiers.org/biosimulators/vcell|SUNDIALS,,solver for differential-algebraic equation systems|implicit differential-algebraic solver,"IDA solves real differential-algebraic systems in N-space, in the general form F(t,y,y')=0, y(t0)=y0, y'(t0)=y'0. At each step, a Newton iteration [http://identifiers.org/biomodels.kisao/KISAO_0000408] leads to linear systems Jx=b, which are solved by one of five methods - two direct (dense or band; serial version only) and three Krylov [http://identifiers.org/biomodels.kisao/KISAO_0000354] (GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000353], BiCGStab [http://identifiers.org/biomodels.kisao/KISAO_0000392], or TFQMR [http://identifiers.org/biomodels.kisao/KISAO_0000396]). IDA is written in C, but derived from the package DASPK [http://identifiers.org/biomodels.kisao/KISAO_0000355] which is written in Fortran.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000480,lower half-bandwidth,,the lower half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000482,,,,,kisao:KISAO_0000480,,2015-09-10,AZ,,https://identifiers.org/biosimulators/opencor,,,the lower half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000480,lower half-bandwidth,,the lower half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000482,,,,,kisao:KISAO_0000480,,2015-09-10,AZ,,http://identifiers.org/biosimulators/opencor,,,the lower half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000352,hybrid method,,A simulation methods which combines the advantages of complementary simulation approaches: the whole system is subdivided into appropriate parts and different simulation methods operate on these parts at the same time.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000352,,2011-06-09,AZ,,,true,,A simulation methods which combines the advantages of complementary simulation approaches: the whole system is subdivided into appropriate parts and different simulation methods operate on these parts at the same time.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000688,type of system described,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000688,,2022-03-29,EN|JRK|WL,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000296,Hammer-Hollingsworth method,,The numerical integration of ordinary differential equations by the use of Gaussian quadrature methods.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000296,,2011-05-10,AZ,,,,,The numerical integration of ordinary differential equations by the use of Gaussian quadrature methods.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000518,evolutionary algorithm parameter,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000518,,2019-01-18,AZ,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000098,type of variable,,Type of variables used for the simulation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000098,,,AZ,,,true,,Type of variables used for the simulation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000449,synchronous logical model simulation method,,Qualitative (logical) models specify the evolution rules of their components. In the case of a synchronous updating all enabled transitions are processed simultaneously. ,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000448,,,,,kisao:KISAO_0000449,,2013-01-28,AZ,,,,,Qualitative (logical) models specify the evolution rules of their components. In the case of a synchronous updating all enabled transitions are processed simultaneously. ,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000692,cellular system,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000690,,,,,kisao:KISAO_0000692,,2022-03-29,EN|JRK|WL,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000416,partial least squares regression method,PLSR method|PLSR,Multivariate regression method based on estimated latent variables. Related to Principal Component Analysis (PCA) and Principal Component Regression (PCR).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000423,,,,,kisao:KISAO_0000416,,2012-01-18,AZ,,,,PLSR method|PLSR,Multivariate regression method based on estimated latent variables. Related to Principal Component Analysis (PCA) and Principal Component Regression (PCR).,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000802,Control coefficient (scaled),,A scaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000802,,06-03-2021,LPS,,,,,A scaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000803,Control coefficient (unscaled),,An unscaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000803,,06-03-2021,LPS,,,,,An unscaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000825,mean,,The mean (average) of a set of values,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000825,,06-03-2021,LPS,,,,,The mean (average) of a set of values,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000588,flux sampling,,Method for sampling fluxes from the null space of a flux balance analysis model,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407,,,,,kisao:KISAO_0000588,,2020-10-29,JRK,,,true,,Method for sampling fluxes from the null space of a flux balance analysis model,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000858,variance ignoring NaN,,"The variance of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000858,,2021-10-09,MK,,,,,"The variance of a set of values, ignoring Nan entries.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000825,mean ignoring NaN,,"The mean (average) of a set of values, ignoring NaN entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000825,,2021-06-03,"LPS, MK",,,,,"The mean (average) of a set of values, ignoring NaN entries.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000588,flux sampling,,Method for sampling fluxes from the null space of a flux balance analysis model,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000588,,2020-10-29,JRK,,,true,,Method for sampling fluxes from the null space of a flux balance analysis model,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000420,number of partial least squares components,,Parameter used by 'partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000416] describing number of PLS components to include in the regression analysis.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000420,,2012-01-18,AZ,,,,,Parameter used by 'partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000416] describing number of PLS components to include in the regression analysis.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000555,absolute quadrature tolerance,,Absolute error tolerance of the adjoint solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000211,,,,,kisao:KISAO_0000555,,2020-10-29,JRK,,https://identifiers.org/biosimulators/amici|SUNDIALS,,,Absolute error tolerance of the adjoint solution.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000555,absolute quadrature tolerance,,Absolute error tolerance of the adjoint solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000211,,,,,kisao:KISAO_0000555,,2020-10-29,JRK,,http://identifiers.org/biosimulators/amici|SUNDIALS,,,Absolute error tolerance of the adjoint solution.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000522,start temperature,,"Initial temperature of the system. The higher the temperature, the larger the probability that a global optimum is found. Note that the temperature should be very high in the beginning of the method (the system should be above the ""melting"" temperature). This value has the same units as the objective function, so what represents ""high"" is different from problem to problem.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000521,,,,,kisao:KISAO_0000522,,2019-01-18,AZ,,,,,"Initial temperature of the system. The higher the temperature, the larger the probability that a global optimum is found. Note that the temperature should be very high in the beginning of the method (the system should be above the ""melting"" temperature). This value has the same units as the objective function, so what represents ""high"" is different from problem to problem.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000093,LSODPK,Livermore solver for ordinary differential equations for stiff and nonstiff systems with krylov corrector iteration,"LSODPK is a set of FORTRAN subroutines for solving the initial value problem for stiff and nonstiff systems of ordinary differential equations. In solving stiff systems, LSODPK uses a corrector iteration composed of Newton iteration and one of four preconditioned Krylov subspace iteration methods [http://identifiers.org/biomodels.kisao/KISAO_0000354]. The user must select the desired Krylov method and supply a pair of routine to evaluate, preprocess, and solve the (left and/or right) preconditioner matrices. Aside from preconditioning, the implementation is matrix-free, meaning that explicit storage of the Jacobian (or related) matrix is not required. The method is experimental because the scope of problems for which it is effective is not well-known, and users are forewarned that LSODPK may or may not be competitive with traditional methods on a given problem. LSODPK also includes an option for a user-supplied linear system solver to be used without Krylov iteration.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000093,,2008-07-08,NLN,,,,Livermore solver for ordinary differential equations for stiff and nonstiff systems with krylov corrector iteration,"LSODPK is a set of FORTRAN subroutines for solving the initial value problem for stiff and nonstiff systems of ordinary differential equations. In solving stiff systems, LSODPK uses a corrector iteration composed of Newton iteration and one of four preconditioned Krylov subspace iteration methods [http://identifiers.org/biomodels.kisao/KISAO_0000354]. The user must select the desired Krylov method and supply a pair of routine to evaluate, preprocess, and solve the (left and/or right) preconditioner matrices. Aside from preconditioning, the implementation is matrix-free, meaning that explicit storage of the Jacobian (or related) matrix is not required. The method is experimental because the scope of problems for which it is effective is not well-known, and users are forewarned that LSODPK may or may not be competitive with traditional methods on a given problem. LSODPK also includes an option for a user-supplied linear system solver to be used without Krylov iteration.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000235,type of solution,,"A characteristic describing the type of the solution produced by the method, specifically whether it is exact or approximate.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000235,,,AZ,,,true,,"A characteristic describing the type of the solution produced by the method, specifically whether it is exact or approximate.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000337,finite element method,finite element analysis|FEA|FEM,"A numerical technique for finding approximate solutions of partial differential equations (PDE) as well as of integral equations. The solution approach is based either on eliminating the differential equation completely (steady state problems), or rendering the PDE into an approximating system of ordinary differential equations, which are then numerically integrated using standard techniques such as Euler method [http://identifiers.org/biomodels.kisao/KISAO_0000261], Runge-Kutta [http://identifiers.org/biomodels.kisao/KISAO_0000064], etc.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000337,,2011-06-07,AZ,,,,finite element analysis|FEA|FEM,"A numerical technique for finding approximate solutions of partial differential equations (PDE) as well as of integral equations. The solution approach is based either on eliminating the differential equation completely (steady state problems), or rendering the PDE into an approximating system of ordinary differential equations, which are then numerically integrated using standard techniques such as Euler method [http://identifiers.org/biomodels.kisao/KISAO_0000261], Runge-Kutta [http://identifiers.org/biomodels.kisao/KISAO_0000064], etc.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000433,CVODE-like method,,"Solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000433,,2012-05-24,AZ,,,true,,"Solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000433,CVODE-like method,,"Solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000694,,,,,kisao:KISAO_0000433,,2012-05-24,AZ,,,true,,"Solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000363,rule-based simulation method,,Rule-based models provide a powerful alternative to approaches that require explicit enumeration of all possible molecular species of a system. Such models consist of formal rules governing interactive behaviour. Rule-based simulation methods simulate such models.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000363,,2011-06-13,AZ,,,true,,Rule-based models provide a powerful alternative to approaches that require explicit enumeration of all possible molecular species of a system. Such models consist of formal rules governing interactive behaviour. Rule-based simulation methods simulate such models.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000330,R-leaping algorithm,R-leap method,"A novel algorithm is proposed for the acceleration of the exact stochastic simulation algorithm by a predefined number of reaction firings (R-leaping) that may occur across several reaction channels. In the present approach, the numbers of reaction firings are correlated binomial distributions and the sampling procedure is independent of any permutation of the reaction channels. This enables the algorithm to efficiently handle large systems with disparate rates, providing substantial computational savings in certain cases.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000621,,,,,kisao:KISAO_0000330,,2011-06-03,AZ,,,,R-leap method,"A novel algorithm is proposed for the acceleration of the exact stochastic simulation algorithm by a predefined number of reaction firings (R-leaping) that may occur across several reaction channels. In the present approach, the numbers of reaction firings are correlated binomial distributions and the sampling procedure is independent of any permutation of the reaction channels. This enables the algorithm to efficiently handle large systems with disparate rates, providing substantial computational savings in certain cases.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000803,control coefficient (unscaled),,An unscaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000803,,06-03-2021,LPS,,,,,An unscaled control coefficient of any dependent element (such as a reaction or a floating species) with respect to an independent element (such as a global parameter or boundary species).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000100,type of progression time step,,Type of time steps used by the algorithm.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000100,,,AZ,,,true,,Type of time steps used by the algorithm.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000498,number of runs,,The number of runs that a simulation should perform. Typically used to specify the number of runs for a stochastic simulation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000498,,2017-07-18,AZ,,,,,The number of runs that a simulation should perform. Typically used to specify the number of runs for a stochastic simulation.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000607,Hierarchical Fehlberg method,hODE|Hierarchical ordinary differential equation integration method|Hierarchical ODE integration method,"Method for continuous simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000086,,,,,kisao:KISAO_0000607,,2021-01-25,JRK,,https://identifiers.org/biosimulators/ibiosim,,hODE|Hierarchical ordinary differential equation integration method|Hierarchical ODE integration method,"Method for continuous simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000607,hierarchical Fehlberg method,hODE|hierarchical ODE integration method|hierarchical ordinary differential equation integration method,"Method for continuous simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000086,,,,,kisao:KISAO_0000607,,2021-01-25,JRK,,http://identifiers.org/biosimulators/ibiosim,,hODE|hierarchical ODE integration method|hierarchical ordinary differential equation integration method,"Method for continuous simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000549,non-linear programming,,Process of solving an optimization problem where some of the constraints or the objective function are nonlinear.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000549,,2020-10-29,JRK,,,,,Process of solving an optimization problem where some of the constraints or the objective function are nonlinear.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000516,Hooke&Jeeves,Hooke and Jeeves method|method of Hooke and Jeeves,The method of Hooke and Jeeves is a direct search algorithm that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of the function. Instead it is based on a heuristic that suggests a descent direction using the values of the function calculated in a number of previous iterations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000516,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,Hooke and Jeeves method|method of Hooke and Jeeves,The method of Hooke and Jeeves is a direct search algorithm that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of the function. Instead it is based on a heuristic that suggests a descent direction using the values of the function calculated in a number of previous iterations.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000666,Jacobian epsilon,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000666,,2021-08-07,JRK,,http://identifiers.org/biosimulators/xpp,,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000516,Hooke&Jeeves,Hooke and Jeeves method|method of Hooke and Jeeves|Hooke-Jeeves method,The method of Hooke and Jeeves is a direct search algorithm that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of the function. Instead it is based on a heuristic that suggests a descent direction using the values of the function calculated in a number of previous iterations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000516,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,Hooke and Jeeves method|method of Hooke and Jeeves|Hooke-Jeeves method,The method of Hooke and Jeeves is a direct search algorithm that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of the function. Instead it is based on a heuristic that suggests a descent direction using the values of the function calculated in a number of previous iterations.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000633,computational function,,"A mathematical function such as the calculation of a minimum, maximum, or mean of a set of values.",false,,,http://www.w3.org/2002/07/owl#Thing,,,,,kisao:KISAO_0000633,,2021-06-04,JRK,,,true,,"A mathematical function such as the calculation of a minimum, maximum, or mean of a set of values.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000491,discrete event simulation algorithm,DES,Discrete Event Simulation algorithm refers to the simulation of systems whose (countable) discrete states change over time and are event-driven.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000491,,,,,,,DES,Discrete Event Simulation algorithm refers to the simulation of systems whose (countable) discrete states change over time and are event-driven.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000261,Euler method,,"The Euler method, named after Leonhard Euler, is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000377,,,,,kisao:KISAO_0000261,,,AZ,,https://identifiers.org/biosimulators/jsim|ByoDyn,,,"The Euler method, named after Leonhard Euler, is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000261,Euler method,,"The Euler method, named after Leonhard Euler, is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000377,,,,,kisao:KISAO_0000261,,,AZ,,ByoDyn|http://identifiers.org/biosimulators/jsim,,,"The Euler method, named after Leonhard Euler, is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000396,transpose-free quasi-minimal residual algorithm,TFQMR,"A version of CGS [http://identifiers.org/biomodels.kisao/KISAO_0000393] which 'quasi-minimizes' the residual in the space spanned by the vectors generated by the CGS iteration. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000389,,,,,kisao:KISAO_0000396,,2011-07-19,AZ,,CVODE,,TFQMR,"A version of CGS [http://identifiers.org/biomodels.kisao/KISAO_0000393] which 'quasi-minimizes' the residual in the space spanned by the vectors generated by the CGS iteration. ",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000819,Nr matrix,,The Nr matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:KISAO_0000819,,06-03-2021,LPS,,,,,The Nr matrix of a model.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000553,optimization solver,,"Optimization solver such as CPLEX, GLPK, or Gurobi.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000553,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cobrapy|https://identifiers.org/biosimulators/cbmpy|https://identifiers.org/biosimulators/raven|OptLang|ConvOpt,,,"Optimization solver such as CPLEX, GLPK, or Gurobi.",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000801,Concentration control coefficient matrix (unscaled),,The unscaled concentration control coefficient matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000801,,06-03-2021,LPS,,,,,The unscaled concentration control coefficient matrix. The dimensions are species by reactions.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000819,Nr matrix,,The Nr matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000819,,06-03-2021,LPS,,,,,The Nr matrix of a model.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000553,optimization solver,,"Optimization solver such as CPLEX, GLPK, or Gurobi.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000553,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cbmpy|http://identifiers.org/biosimulators/raven|OptLang|ConvOpt|http://identifiers.org/biosimulators/cobrapy,,,"Optimization solver such as CPLEX, GLPK, or Gurobi.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000427,number of clusters,,Parameter specifying the number of clusters used by C-means algorithm.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000429,,,,,kisao:KISAO_0000427,,2012-01-18,AZ,,,,,Parameter specifying the number of clusters used by C-means algorithm.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000670,use multiple steps,,Whether to perform a multiple time step simulation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000670,,2021-08-07,JRK,,http://identifiers.org/biosimulators/tellurium,,,Whether to perform a multiple time step simulation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000022,Elf and Ehrenberg method,Elf algorithm|NSM|next-subvolume method,"Sub-volume stochastic reaction-diffusion method that is a combination of the Direct Method [http://identifiers.org/biomodels.kisao/KISAO_0000029] for sampling the time for a next reaction or diffusion event in each subvolume, with Gibson and Bruck's Next Reaction Method [http://identifiers.org/biomodels.kisao/KISAO_0000027], which is used to keep track of in which subvolume an event occurs next. The subvolumes are kept sorted in a queue, implemented as a binary tree, according to increasing time of the next event. When an event has occurred in the subvolume at the top of the queue, new event times need to be sampled only for one (the event is a chemical reaction) or two (the event is a diffusion jump) subvolume(s).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000095,,,,,kisao:KISAO_0000022,,,,,MesoRD|SmartCell,,Elf algorithm|NSM|next-subvolume method,"Sub-volume stochastic reaction-diffusion method that is a combination of the Direct Method [http://identifiers.org/biomodels.kisao/KISAO_0000029] for sampling the time for a next reaction or diffusion event in each subvolume, with Gibson and Bruck's Next Reaction Method [http://identifiers.org/biomodels.kisao/KISAO_0000027], which is used to keep track of in which subvolume an event occurs next. The subvolumes are kept sorted in a queue, implemented as a binary tree, according to increasing time of the next event. When an event has occurred in the subvolume at the top of the queue, new event times need to be sampled only for one (the event is a chemical reaction) or two (the event is a diffusion jump) subvolume(s).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000520,evolutionary algorithm,,An optimisation algorithm that mimics evolution and is based on reproduction and selection.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000520,,2019-01-18,AZ,,,true,,An optimisation algorithm that mimics evolution and is based on reproduction and selection.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000324,nested stochastic simulation algorithm,nested SSA,"This multiscale method is a small modification of the Gillespie's direct method [http://identifiers.org/biomodels.kisao/KISAO_0000029], in the form of a nested SSA, with inner loops for the fast reactions, and outer loop for the slow reactions. The number of groups can be more than two, and the grouping into fast and slow variables can be done dynamically in an adaptive version of the scheme.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000324,,2011-06-02,AZ,,,,nested SSA,"This multiscale method is a small modification of the Gillespie's direct method [http://identifiers.org/biomodels.kisao/KISAO_0000029], in the form of a nested SSA, with inner loops for the fast reactions, and outer loop for the slow reactions. The number of groups can be more than two, and the grouping into fast and slow variables can be done dynamically in an adaptive version of the scheme.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000076,Stundzia and Lumsden method,RD SSA|reaction-diffusion stochastic simulation algorithm,Sub-volume stochastic reaction-diffusion method that using Green's function to link the bulk diffusion coefficient D in Fick's differential law to the corresponding transition rate probability for diffusion of a particle between finite volume elements. This generalized stochastic algorithm enables to numerically calculate the time evolution of a spatially inhomogeneous mixture of reaction-diffusion species in a finite volume. The time step is stochastic and is generated by a probability distribution determined by the intrinsic reaction kinetics and diffusion dynamics.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000095,,,,,kisao:KISAO_0000076,,,,,,,RD SSA|reaction-diffusion stochastic simulation algorithm,Sub-volume stochastic reaction-diffusion method that using Green's function to link the bulk diffusion coefficient D in Fick's differential law to the corresponding transition rate probability for diffusion of a particle between finite volume elements. This generalized stochastic algorithm enables to numerically calculate the time evolution of a spatially inhomogeneous mixture of reaction-diffusion species in a finite volume. The time step is stochastic and is generated by a probability distribution determined by the intrinsic reaction kinetics and diffusion dynamics.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000533,pFBA factor,,Maximum permissible sum of absolute fluxes.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000533,,,,,,,,Maximum permissible sum of absolute fluxes.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000566,stochastic second order Runge-Kutta method,RI5,Technique for the second order approximate numerical solution of a systems of stochastic differential equations (SDEs). The method is a generalisation of the Runge-Kutta method for ordinary differential equations to stochastic differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000564,,,,,kisao:KISAO_0000566,,2020-10-29,JRK,,https://identifiers.org/biosimulators/copasi,,RI5,Technique for the second order approximate numerical solution of a systems of stochastic differential equations (SDEs). The method is a generalisation of the Runge-Kutta method for ordinary differential equations to stochastic differential equations.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000605,SDE Tolerance,Maximum allowed value of the drift and diffusion errors.,Stochastic differential equation tolerance,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000605,,2021-01-13,MLB|JRK,,,,Maximum allowed value of the drift and diffusion errors.,Stochastic differential equation tolerance,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000566,stochastic second order Runge-Kutta method,RI5,Technique for the second order approximate numerical solution of a systems of stochastic differential equations (SDEs). The method is a generalisation of the Runge-Kutta method for ordinary differential equations to stochastic differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000564,,,,,kisao:KISAO_0000566,,2020-10-29,JRK,,http://identifiers.org/biosimulators/copasi,,RI5,Technique for the second order approximate numerical solution of a systems of stochastic differential equations (SDEs). The method is a generalisation of the Runge-Kutta method for ordinary differential equations to stochastic differential equations.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000605,SDE tolerance,Maximum allowed value of the drift and diffusion errors.|maximum allowed value of the drift and diffusion errors.,Stochastic differential equation tolerance,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000605,,2021-01-13,MLB|JRK,,,,Maximum allowed value of the drift and diffusion errors.|maximum allowed value of the drift and diffusion errors.,Stochastic differential equation tolerance,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000303,Zonneveld method,,"An embedded Runge-Kutta method [http://identifiers.org/biomodels.kisao/KISAO_0000302] of order 4(3), proposed by J.A. Zonneveld in 1964.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000303,,2011-05-10,AZ,,,,,"An embedded Runge-Kutta method [http://identifiers.org/biomodels.kisao/KISAO_0000302] of order 4(3), proposed by J.A. Zonneveld in 1964.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000107,progression with adaptive time step,,"Algorithm, possessing this characteristic, does not use fixed time steps to update the state of a system during the whole simulation, but on the contrary adapts the length of the time steps to the local situation.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000100,,,,,kisao:KISAO_0000107,,2008-07-08,NLN,,,,,"Algorithm, possessing this characteristic, does not use fixed time steps to update the state of a system during the whole simulation, but on the contrary adapts the length of the time steps to the local situation.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000598,Hybrid Gibson - Milstein Method,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000598,,2021-01-13,MLB|JRK,,https://identifiers.org/biosimulators/vcell,,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000598,hybrid Gibson - Milstein method,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000598,,2021-01-13,MLB|JRK,,http://identifiers.org/biosimulators/vcell,,,"A hybrid stochastic method partitions the system into subsets of fast and slow reactions and approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the Gibson algorithm. Fixed time step Milstein is used for approximate numerical solution of CLE.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000836,amount,,The extensive quantity amount.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000836,,06-03-2021,LPS,,,,,The extensive quantity amount.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000335,generalized stochastic simulation algorithm,,"Gillespie direct method [http://identifiers.org/biomodels.kisao/KISAO_0000029] follows unit-by-unit changes in the total numbers of each reactant species, it is especially well suited to the study of systems in which reactant densities are low and the application of methods based on continuum approximations, such as the traditional ordinary differential equations of chemical kinetics, is questionable. The 'generalized stochastic simulation algorithm' branch presents methods, which extend Gillespie direct method [http://identifiers.org/biomodels.kisao/KISAO_0000029] to suit to systems with other characteristics.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000241,,,,,kisao:KISAO_0000335,,2011-06-03,AZ,,,true,,"Gillespie direct method [http://identifiers.org/biomodels.kisao/KISAO_0000029] follows unit-by-unit changes in the total numbers of each reactant species, it is especially well suited to the study of systems in which reactant densities are low and the application of methods based on continuum approximations, such as the traditional ordinary differential equations of chemical kinetics, is questionable. The 'generalized stochastic simulation algorithm' branch presents methods, which extend Gillespie direct method [http://identifiers.org/biomodels.kisao/KISAO_0000029] to suit to systems with other characteristics.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000209,relative tolerance,RTOL,This parameter is a numeric value specifying the desired relative tolerance the user wants to achieve. A smaller value means that the trajectory is calculated more accurately.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000597,,,,,kisao:KISAO_0000209,,,,,,,RTOL,This parameter is a numeric value specifying the desired relative tolerance the user wants to achieve. A smaller value means that the trajectory is calculated more accurately.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000847,product,,The product of a set of values. If the values contain NaN the product is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000847,,2021-10-09,MK,,,,,The product of a set of values. If the values contain NaN the product is NaN.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000374,ordinary differential equation problem,ODE problem,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000405,,,,,kisao:KISAO_0000374,,,AZ,,,,ODE problem,,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000476,iteration type,,the type of iteration used by the solver,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000476,,2015-09-10,AZ,,https://identifiers.org/biosimulators/opencor,,,the type of iteration used by the solver,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000618,bunker,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is equated to the mean inter-event time (inverse of the sum of the propensitites of the reactions) rather than sampled from a distribution parameterized by this mean inter-event time. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000618,,2021-01-30,JRK,,https://identifiers.org/biosimulators/ibiosim,,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is equated to the mean inter-event time (inverse of the sum of the propensitites of the reactions) rather than sampled from a distribution parameterized by this mean inter-event time. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000476,iteration type,,the type of iteration used by the solver,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000476,,2015-09-10,AZ,,http://identifiers.org/biosimulators/opencor,,,the type of iteration used by the solver,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000618,bunker,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is equated to the mean inter-event time (inverse of the sum of the propensitites of the reactions) rather than sampled from a distribution parameterized by this mean inter-event time. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000618,,2021-01-30,JRK,,http://identifiers.org/biosimulators/ibiosim,,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is equated to the mean inter-event time (inverse of the sum of the propensitites of the reactions) rather than sampled from a distribution parameterized by this mean inter-event time. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000279,Adams-Bashforth method,explicit Adams method,"Given an initial value problem: y' = f(x,y), y(x0) = y0 together with additional starting values y1 = y(x0 + h), . . . , yk-1 = y(x0 + (k-1) h) the k-step Adams-Bashforth method is an explicit linear multistep method that approximates the solution, y(x) at x = x0+kh, of the initial value problem by yk = yk - 1 + h * ( a0 f(xk - 1,yk - 1) + a1 f(xk - 2,yk - 2) + . . . + ak - 1 f(x0,y0) ), where a0, a1, . . . , ak - 1 are constants.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000289,,,,,kisao:KISAO_0000279,,2011-05-09,AZ,,,,explicit Adams method,"Given an initial value problem: y' = f(x,y), y(x0) = y0 together with additional starting values y1 = y(x0 + h), . . . , yk-1 = y(x0 + (k-1) h) the k-step Adams-Bashforth method is an explicit linear multistep method that approximates the solution, y(x) at x = x0+kh, of the initial value problem by yk = yk - 1 + h * ( a0 f(xk - 1,yk - 1) + a1 f(xk - 2,yk - 2) + . . . + ak - 1 f(x0,y0) ), where a0, a1, . . . , ak - 1 are constants.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000840,minimum,,The minimum of a set of values. If the values contain NaN the minimum is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000840,,2021-10-09,MK,,,,,The minimum of a set of values. If the values contain NaN the minimum is NaN.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000071,LSODE,Livermore solver for ordinary differential equations,"LSODE solves stiff and nonstiff systems of the form dy/dt = f. In the stiff case, it treats the Jacobian matrix sf/dy as either a dense (full) or a banded matrix, and as either user-supplied or internally approximated by difference quotients. It uses Adams methods (predictor-corrector) [http://identifiers.org/biomodels.kisao/KISAO_0000364] in the nonstiff case, and Backward Differentiation Formula (BDF) methods (the Gear methods) [http://identifiers.org/biomodels.kisao/KISAO_0000288] in the stiff case.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000071,,2007-11-16,dk,,,,Livermore solver for ordinary differential equations,"LSODE solves stiff and nonstiff systems of the form dy/dt = f. In the stiff case, it treats the Jacobian matrix sf/dy as either a dense (full) or a banded matrix, and as either user-supplied or internally approximated by difference quotients. It uses Adams methods (predictor-corrector) [http://identifiers.org/biomodels.kisao/KISAO_0000364] in the nonstiff case, and Backward Differentiation Formula (BDF) methods (the Gear methods) [http://identifiers.org/biomodels.kisao/KISAO_0000288] in the stiff case.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000570,auto reduce tolerances,,Whether to automatically reduce tolerances.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243|http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000570,,2020-10-29,JRK,,https://identifiers.org/biosimulators/pysces,,,Whether to automatically reduce tolerances.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000570,auto reduce tolerances,,Whether to automatically reduce tolerances.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243|http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000570,,2020-10-29,JRK,,http://identifiers.org/biosimulators/pysces,,,Whether to automatically reduce tolerances.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000048,adaptive explicit-implicit tau-leaping method,,"Modification of the original tau-selection strategy [http://identifiers.org/biomodels.kisao/KISAO_0000040], designed for explicit tau-leaping, is modified to apply to implicit tau-leaping, allowing for longer steps when the system is stiff. Further, an adaptive strategy is proposed that identifies stiffness and automatically chooses between the explicit and the (new) implicit tau-selection methods to achieve better efficiency.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000048,,,,,,,,"Modification of the original tau-selection strategy [http://identifiers.org/biomodels.kisao/KISAO_0000040], designed for explicit tau-leaping, is modified to apply to implicit tau-leaping, allowing for longer steps when the system is stiff. Further, an adaptive strategy is proposed that identifies stiffness and automatically chooses between the explicit and the (new) implicit tau-selection methods to achieve better efficiency.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000329,constant-time kinetic Monte Carlo algorithm,SSA-CR,"The computational cost of the original SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] scaled linearly with the number of reactions in the network. Gibson and Bruck developed a logarithmic scaling version of the SSA which uses a priority queue or binary tree for more efficient reaction selection [http://identifiers.org/biomodels.kisao/KISAO_0000027]. More generally, this problem is one of dynamic discrete random variate generation which finds many uses in kinetic Monte Carlo and discrete event simulation. We present here a constant-time algorithm, whose cost is independent of the number of reactions, enabled by a slightly more complex underlying data structure.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000329,,2011-06-03,AZ,,,,SSA-CR,"The computational cost of the original SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] scaled linearly with the number of reactions in the network. Gibson and Bruck developed a logarithmic scaling version of the SSA which uses a priority queue or binary tree for more efficient reaction selection [http://identifiers.org/biomodels.kisao/KISAO_0000027]. More generally, this problem is one of dynamic discrete random variate generation which finds many uses in kinetic Monte Carlo and discrete event simulation. We present here a constant-time algorithm, whose cost is independent of the number of reactions, enabled by a slightly more complex underlying data structure.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000074,binomial tau-leaping method,BtauL|binomial tau-leap spatial stochastic simulation algorithm,Coarse grained modified version of the next subvolume method [http://identifiers.org/biomodels.kisao/KISAO_0000022] that allows the user to consider both diffusion and reaction events in relatively long simulation time spans as compared with the original method and other commonly used fully stochastic computational methods.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000074,,2007-10-16,dk,,,,BtauL|binomial tau-leap spatial stochastic simulation algorithm,Coarse grained modified version of the next subvolume method [http://identifiers.org/biomodels.kisao/KISAO_0000022] that allows the user to consider both diffusion and reaction events in relatively long simulation time spans as compared with the original method and other commonly used fully stochastic computational methods.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000425,standardising of variables,,A boolean parameter of the 'hierarchical cluster-based partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000417] specifying whether the variables were standardised (divided by their standard deviations) prior to the regression analysis.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000430,,,,,kisao:KISAO_0000425,,2012-01-18,AZ,,,,,A boolean parameter of the 'hierarchical cluster-based partial least squares regression method' [http://identifiers.org/biomodels.kisao/KISAO_0000417] specifying whether the variables were standardised (divided by their standard deviations) prior to the regression analysis.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000677,maximum number of steps for presimulation,,Maximum number of steps to take in presimulating a model prior to analysis.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000415,,,,,kisao:KISAO_0000677,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Maximum number of steps to take in presimulating a model prior to analysis.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000020,PVODE,parallel code value ordinary differential equation solver,"PVODE is a general-purpose solver for ordinary differential equation (ODE) systems that implements methods for both stiff and nonstiff systems. [...] In the stiff case, PVODE uses a backward differentiation formula method [http://identifiers.org/biomodels.kisao/KISAO_0000288] combined with preconditioned GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000253] iteration. Parallelism is achieved by distributing the ODE solution vector into user-specified segments and parallelizing a set of vector kernels accordingly. For PDE-based ODE systems, we provide a module that generates a band block-diagonal preconditioner for use with the GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000253] iteration. PVODE is based on CVODE [http://identifiers.org/biomodels.kisao/KISAO_0000019].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000020,,,,,SUNDIALS,,parallel code value ordinary differential equation solver,"PVODE is a general-purpose solver for ordinary differential equation (ODE) systems that implements methods for both stiff and nonstiff systems. [...] In the stiff case, PVODE uses a backward differentiation formula method [http://identifiers.org/biomodels.kisao/KISAO_0000288] combined with preconditioned GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000253] iteration. Parallelism is achieved by distributing the ODE solution vector into user-specified segments and parallelizing a set of vector kernels accordingly. For PDE-based ODE systems, we provide a module that generates a band block-diagonal preconditioner for use with the GMRES [http://identifiers.org/biomodels.kisao/KISAO_0000253] iteration. PVODE is based on CVODE [http://identifiers.org/biomodels.kisao/KISAO_0000019].",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000527,geometric flux balance analysis,gFBA|geometric FBA,Method for determining the central flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437,,,,,kisao:KISAO_0000527,,2020-08-11,AZ,,https://identifiers.org/biosimulators/cobrapy,,gFBA|geometric FBA,Method for determining the central flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000032,explicit fourth-order Runge-Kutta method,ERK4|Runge-Kutta method|RK4,"The Runge-Kutta method is a method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t, plus the product of the size of the interval and an estimated slope. The slope is a weighted average of 4 single slope points (beginning of interval-midpoint-midpoint-end of interval).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000032,,2007-11-12,dk,,https://identifiers.org/biosimulators/jsim|GSL|https://identifiers.org/biosimulators/vcell,,ERK4|Runge-Kutta method|RK4,"The Runge-Kutta method is a method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t, plus the product of the size of the interval and an estimated slope. The slope is a weighted average of 4 single slope points (beginning of interval-midpoint-midpoint-end of interval).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000527,geometric flux balance analysis,gFBA|geometric FBA,Method for determining the central flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437,,,,,kisao:KISAO_0000527,,2020-08-11,AZ,,http://identifiers.org/biosimulators/cobrapy,,gFBA|geometric FBA,Method for determining the central flux distribution among all flux distributions that satisfy the constraints of the flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000032,explicit fourth-order Runge-Kutta method,ERK4|Runge-Kutta method|RK4,"The Runge-Kutta method is a method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t, plus the product of the size of the interval and an estimated slope. The slope is a weighted average of 4 single slope points (beginning of interval-midpoint-midpoint-end of interval).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000032,,2007-11-12,dk,,GSL|http://identifiers.org/biosimulators/jsim|http://identifiers.org/biosimulators/vcell,,ERK4|Runge-Kutta method|RK4,"The Runge-Kutta method is a method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t, plus the product of the size of the interval and an estimated slope. The slope is a weighted average of 4 single slope points (beginning of interval-midpoint-midpoint-end of interval).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000801,concentration control coefficient matrix (unscaled),,The unscaled concentration control coefficient matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000801,,06-03-2021,LPS,,,,,The unscaled concentration control coefficient matrix. The dimensions are species by reactions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000834,rate of change,rate,The rate of change of one variable with respect to a second variable.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000834,,06-03-2021,LPS,,,,rate,The rate of change of one variable with respect to a second variable.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000681,maximum time,,Maximum amount of wall time for an operation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000681,,2021-08-23,JRK,,,,,Maximum amount of wall time for an operation.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000564,stochastic Runge-Kutta method,,Technique for the approximate numerical solution of a systems of stochastic differential equations (SDEs). The method is a generalisation of the Runge-Kutta method for ordinary differential equations to stochastic differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000564,,2020-10-29,JRK,,,,,Technique for the approximate numerical solution of a systems of stochastic differential equations (SDEs). The method is a generalisation of the Runge-Kutta method for ordinary differential equations to stochastic differential equations.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000033,Rosenbrock method,generalized fourth order Runge-Kutta method|Kaps-Rentrop method,"Some general implicit processes are given for the solution of simultaneous first-order differential equations. These processes, which use successive substitution, are implicit analogues of the (explicit) Runge-Kutta processes. They require the solution in each time step of one or more set of simultaneous linear equations, usually of a special and simple form. Processes of any required order can be devised, and they can be made to have a wide margin of stability when applied to a linear problem.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000033,,2007-11-12,dk,,https://identifiers.org/biosimulators/ecell4,,generalized fourth order Runge-Kutta method|Kaps-Rentrop method,"Some general implicit processes are given for the solution of simultaneous first-order differential equations. These processes, which use successive substitution, are implicit analogues of the (explicit) Runge-Kutta processes. They require the solution in each time step of one or more set of simultaneous linear equations, usually of a special and simple form. Processes of any required order can be devised, and they can be made to have a wide margin of stability when applied to a linear problem.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000033,Rosenbrock method,generalized fourth order Runge-Kutta method|Kaps-Rentrop method,"Some general implicit processes are given for the solution of simultaneous first-order differential equations. These processes, which use successive substitution, are implicit analogues of the (explicit) Runge-Kutta processes. They require the solution in each time step of one or more set of simultaneous linear equations, usually of a special and simple form. Processes of any required order can be devised, and they can be made to have a wide margin of stability when applied to a linear problem.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000033,,2007-11-12,dk,,http://identifiers.org/biosimulators/ecell4,,generalized fourth order Runge-Kutta method|Kaps-Rentrop method,"Some general implicit processes are given for the solution of simultaneous first-order differential equations. These processes, which use successive substitution, are implicit analogues of the (explicit) Runge-Kutta processes. They require the solution in each time step of one or more set of simultaneous linear equations, usually of a special and simple form. Processes of any required order can be devised, and they can be made to have a wide margin of stability when applied to a linear problem.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000808,reduced stoichiometry matrix,,The reduced stoichiometry matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000808,,06-03-2021,LPS,,,,,The reduced stoichiometry matrix. The dimensions are species by reactions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000531,fraction of optimum,,Fraction of the optimum solution which must be maintained.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000531,,,,,,,,Fraction of the optimum solution which must be maintained.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000105,discrete variable,,"Algorithm, possessing this characteristic, allows values of system's variables to change by discrete (integral) amounts.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000098,,,,,kisao:KISAO_0000105,,2008-07-08,NLN,,,,,"Algorithm, possessing this characteristic, allows values of system's variables to change by discrete (integral) amounts.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000346,mesh-based geometry handling,,"In most large-scale numerical simulations of physical phenomena, a large percentage of the overall computational effort is expended on technical details connected with meshing. These details include, in particular, grid generation, mesh adaptation to domain geometry, element or cell connectivity, grid motion and separation to model fracture, fragmentation, free surfaces, etc.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000311,,,,,kisao:KISAO_0000346,,,AZ,,,,,"In most large-scale numerical simulations of physical phenomena, a large percentage of the overall computational effort is expended on technical details connected with meshing. These details include, in particular, grid generation, mesh adaptation to domain geometry, element or cell connectivity, grid motion and separation to model fracture, fragmentation, free surfaces, etc.",,,,,,, @@ -364,59 +402,72 @@ http://www.biomodels.net/kisao/KISAO#KISAO_0000577,flux minimization weight,,"Th Min sum((fluxAi - fluxBi)^2) + sum(fluxAi)^(fluxMinimizationWeight) + sum(fluxBi)^(fluxMinimizationWeight)",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000577,,2020-10-29,JRK,,,,,"The degree to which minimization of the sum of fluxes should be taken into account in Minimization of Metabolic Adjustment (MOMA) which solvers the optimization problem Min sum((fluxAi - fluxBi)^2) + sum(fluxAi)^(fluxMinimizationWeight) + sum(fluxBi)^(fluxMinimizationWeight)",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000812,Jacobian matrix,full Jacobian matrix,The (full) Jacobian matrix. The dimensions are species by species.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000812,,06-03-2021,LPS,,,,full Jacobian matrix,The (full) Jacobian matrix. The dimensions are species by species.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000649,shadow price,,"Change, per infinitesimal unit of the constraint, in the optimal value of the objective function of an optimization problem obtained by relaxing the constraint.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000650,,,,,kisao:KISAO_0000649,,2021-06-04,JRK,,,,,"Change, per infinitesimal unit of the constraint, in the optimal value of the objective function of an optimization problem obtained by relaxing the constraint.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000616,semi-implicit regular grid finite volume method with a fixed time step,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000285,,,,,kisao:KISAO_0000616,,2021-01-30,JRK,,https://identifiers.org/biosimulators/vcell,,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000616,semi-implicit regular grid finite volume method with a fixed time step,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000285,,,,,kisao:KISAO_0000616,,2021-01-30,JRK,,http://identifiers.org/biosimulators/vcell,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000244,granularity control parameter,,Parameter controlling granularity.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000244,,,AZ,,,true,,Parameter controlling granularity.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000379,Bulirsch-Stoer algorithm,GBS|Gragg-Bulirsch-Stoer algorithm,"The Bulirsch-Stoer method is an adaptive method which uses Gragg's modified midpoint method [http://identifiers.org/biomodels.kisao/KISAO_0000382] to estimate the solution of an initial value problem for various step sizes. The estimates are fit to a ""diagonal"" rational function or a polynomial as a function of the step size and the limit as the step size tends to zero is taken as the final estimate.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000380,,,,,kisao:KISAO_0000379,,2011-07-01,AZ,,,,GBS|Gragg-Bulirsch-Stoer algorithm,"The Bulirsch-Stoer method is an adaptive method which uses Gragg's modified midpoint method [http://identifiers.org/biomodels.kisao/KISAO_0000382] to estimate the solution of an initial value problem for various step sizes. The estimates are fit to a ""diagonal"" rational function or a polynomial as a function of the step size and the limit as the step size tends to zero is taken as the final estimate.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000211,absolute tolerance,ATOL,This parameter is a positive numeric value specifying the desired absolute tolerance the user wants to achieve.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000597,,,,,kisao:KISAO_0000211,,,,,,,ATOL,This parameter is a positive numeric value specifying the desired absolute tolerance the user wants to achieve.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000452,random updating policy,,An updating policy that chooses a transition randomly.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000451,,,,,kisao:KISAO_0000452,,2013-01-28,AZ,,,,,An updating policy that chooses a transition randomly.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000581,BKMC,Boolean Kinetic Monte-Carlo,"The Boolean Kinetic Monte Carlo method (BKMC) is a natural generalization of the asynchronous Boolean simulation method, with a direct probabilistic interpretation. In the BKMC framework, the dynamics is parameterized by a biological time and the order of update is noisy, which is less strict than priority classes introduced in GINsin. A BKMC model is specified by logical rules as in regular Boolean models but with a more precise information: a numerical rate is added for each transition of each node.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000319|http://www.biomodels.net/kisao/KISAO#KISAO_0000450,,,,,kisao:KISAO_0000581,,2020-10-29,JRK,,https://identifiers.org/biosimulators/maboss,,Boolean Kinetic Monte-Carlo,"The Boolean Kinetic Monte Carlo method (BKMC) is a natural generalization of the asynchronous Boolean simulation method, with a direct probabilistic interpretation. In the BKMC framework, the dynamics is parameterized by a biological time and the order of update is noisy, which is less strict than priority classes introduced in GINsin. A BKMC model is specified by logical rules as in regular Boolean models but with a more precise information: a numerical rate is added for each transition of each node.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000581,BKMC,Boolean kinetic Monte-Carlo|Boolean Kinetic Monte-Carlo,"The Boolean kinetic Monte Carlo method (BKMC) is a natural generalization of the asynchronous Boolean simulation method, with a direct probabilistic interpretation. In the BKMC framework, the dynamics is parameterized by a biological time and the order of update is noisy, which is less strict than priority classes introduced in GINsin. A BKMC model is specified by logical rules as in regular Boolean models but with a more precise information: a numerical rate is added for each transition of each node.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000319|http://www.biomodels.net/kisao/KISAO#KISAO_0000573,,,,,kisao:KISAO_0000581,,2020-10-29,JRK,,http://identifiers.org/biosimulators/maboss,,Boolean kinetic Monte-Carlo|Boolean Kinetic Monte-Carlo,"The Boolean kinetic Monte Carlo method (BKMC) is a natural generalization of the asynchronous Boolean simulation method, with a direct probabilistic interpretation. In the BKMC framework, the dynamics is parameterized by a biological time and the order of update is noisy, which is less strict than priority classes introduced in GINsin. A BKMC model is specified by logical rules as in regular Boolean models but with a more precise information: a numerical rate is added for each transition of each node.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000653,particle number rate,,Rate of a process in extensive/absolute units such as mole reactions per second.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000834,,,,,kisao:KISAO_0000653,,2021-06-04,JRK,,,,,Rate of a process in extensive/absolute units such as mole reactions per second.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000620,parsimonius flux balance analysis,pFBA,"A technique for selecting a flux distribution which is parsimonious by some metric, such as a solution which has the minimal number of active fluxes or a solution which has the smallest sum of active fluxes.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437,,,,,kisao:KISAO_0000620,,2021-04-27,JRK,,,true,pFBA,"A technique for selecting a flux distribution which is parsimonious by some metric, such as a solution which has the minimal number of active fluxes or a solution which has the smallest sum of active fluxes.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000257,surface-bound epsilon,,"A parameter of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057]. Molecules that are bound to a surface are given locations that are extremely close to that surface. However, this position does not need to be exactly at the surface, and in fact it usually cannot be exactly at the surface due to round-off error. The tolerance for how far a surface-bound molecule is allowed to be away from the surface can be set with the epsilon statement.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000257,,,,,,,,"A parameter of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057]. Molecules that are bound to a surface are given locations that are extremely close to that surface. However, this position does not need to be exactly at the surface, and in fact it usually cannot be exactly at the surface due to round-off error. The tolerance for how far a surface-bound molecule is allowed to be away from the surface can be set with the epsilon statement.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000580,ROOM,Regulatory on/off minimization of metabolic flux changes,Constraint-based algorithm for predicting the metabolic steady state after gene knockouts which aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000580,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cobratoolbox|https://identifiers.org/biosimulators/optflux,,Regulatory on/off minimization of metabolic flux changes,Constraint-based algorithm for predicting the metabolic steady state after gene knockouts which aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000027,Gibson-Bruck next reaction algorithm,next reaction method|Gibson and Bruck algorithm|Gibson-Bruck's next reaction algorithm|Gillespie-Gibson stochastic simulation algorithm|SSA-GB,"As with the first reaction method [http://identifiers.org/biomodels.kisao/KISAO_0000015], a putative reaction time is calculated for each reaction, and the reaction with the shortest reaction time will be realized. However, the unused calculated reaction times are not wasted. The set of reactions is organized in a priority queue to allow for the efficient search for the fastest reaction. In addition, by using a so-called dependency graph only those reaction times are recalculated in each step, that are dependent on the reaction, which has been realized.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000027,,2007-11-10,dk,,Cain|https://identifiers.org/biosimulators/vcell|https://identifiers.org/biosimulators/ecell4|SmartCell,,next reaction method|Gibson and Bruck algorithm|Gibson-Bruck's next reaction algorithm|Gillespie-Gibson stochastic simulation algorithm|SSA-GB,"As with the first reaction method [http://identifiers.org/biomodels.kisao/KISAO_0000015], a putative reaction time is calculated for each reaction, and the reaction with the shortest reaction time will be realized. However, the unused calculated reaction times are not wasted. The set of reactions is organized in a priority queue to allow for the efficient search for the fastest reaction. In addition, by using a so-called dependency graph only those reaction times are recalculated in each step, that are dependent on the reaction, which has been realized.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000580,ROOM,regulatory on/off minimization of metabolic flux changes|Regulatory on/off minimization of metabolic flux changes,Constraint-based algorithm for predicting the metabolic steady state after gene knockouts which aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000407|http://www.biomodels.net/kisao/KISAO#KISAO_0000622,,,,,kisao:KISAO_0000580,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cobratoolbox|http://identifiers.org/biosimulators/optflux,,regulatory on/off minimization of metabolic flux changes|Regulatory on/off minimization of metabolic flux changes,Constraint-based algorithm for predicting the metabolic steady state after gene knockouts which aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000027,Gibson-Bruck next reaction algorithm,next reaction method|Gibson and Bruck algorithm|Gibson-Bruck's next reaction algorithm|Gillespie-Gibson stochastic simulation algorithm|SSA-GB,"As with the first reaction method [http://identifiers.org/biomodels.kisao/KISAO_0000015], a putative reaction time is calculated for each reaction, and the reaction with the shortest reaction time will be realized. However, the unused calculated reaction times are not wasted. The set of reactions is organized in a priority queue to allow for the efficient search for the fastest reaction. In addition, by using a so-called dependency graph only those reaction times are recalculated in each step, that are dependent on the reaction, which has been realized.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000027,,2007-11-10,dk,,Cain|http://identifiers.org/biosimulators/ecell4|http://identifiers.org/biosimulators/vcell|SmartCell,,next reaction method|Gibson and Bruck algorithm|Gibson-Bruck's next reaction algorithm|Gillespie-Gibson stochastic simulation algorithm|SSA-GB,"As with the first reaction method [http://identifiers.org/biomodels.kisao/KISAO_0000015], a putative reaction time is calculated for each reaction, and the reaction with the shortest reaction time will be realized. However, the unused calculated reaction times are not wasted. The set of reactions is organized in a priority queue to allow for the efficient search for the fastest reaction. In addition, by using a so-called dependency graph only those reaction times are recalculated in each step, that are dependent on the reaction, which has been realized.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000350,probability-weighted dynamic Monte Carlo method,probability-weighted DMC|PW-DMC,We have developed a probability-weighted DMC method by incorporating the weighted sampling algorithm of equilibrium molecular simulations. This new algorithm samples the slow reactions very efficiently and makes it possible to simulate in a computationally efficient manner the reaction kinetics of physical systems in which the rates of reactions vary by several orders of magnitude.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000350,,2011-06-09,AZ,,,,probability-weighted DMC|PW-DMC,We have developed a probability-weighted DMC method by incorporating the weighted sampling algorithm of equilibrium molecular simulations. This new algorithm samples the slow reactions very efficiently and makes it possible to simulate in a computationally efficient manner the reaction kinetics of physical systems in which the rates of reactions vary by several orders of magnitude.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000308,MacCormack method,,"In computational fluid dynamics, the MacCormack method is a widely used discretization scheme for the numerical solution of hyperbolic partial differential equations. This second-order finite difference method [http://identifiers.org/biomodels.kisao/KISAO_0000307] is introduced by R. W. MacCormack in 1969.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000307,,,,,kisao:KISAO_0000308,,2011-05-11,AZ,,https://identifiers.org/biosimulators/jsim,,,"In computational fluid dynamics, the MacCormack method is a widely used discretization scheme for the numerical solution of hyperbolic partial differential equations. This second-order finite difference method [http://identifiers.org/biomodels.kisao/KISAO_0000307] is introduced by R. W. MacCormack in 1969.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000538,safety factor on new step selection,safe|safety,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000538,,2020-10-29,JRK,,Odespy|https://identifiers.org/biosimulators/gillespy2|JModelica|SciPy,,safe|safety,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000851,cumulative product,,The cumulative product of a set of values. If the values contain NaN the cumulative product is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000851,,2021-10-09,MK,,,,,The cumulative product of a set of values. If the values contain NaN the cumulative product is NaN.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000308,MacCormack method,,"In computational fluid dynamics, the MacCormack method is a widely used discretization scheme for the numerical solution of hyperbolic partial differential equations. This second-order finite difference method [http://identifiers.org/biomodels.kisao/KISAO_0000307] is introduced by R. W. MacCormack in 1969.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000307,,,,,kisao:KISAO_0000308,,2011-05-11,AZ,,http://identifiers.org/biosimulators/jsim,,,"In computational fluid dynamics, the MacCormack method is a widely used discretization scheme for the numerical solution of hyperbolic partial differential equations. This second-order finite difference method [http://identifiers.org/biomodels.kisao/KISAO_0000307] is introduced by R. W. MacCormack in 1969.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000806,elasticity matrix (scaled),,The scaled elasticity matrix. The dimensions are reactions by species.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000806,,06-03-2021,LPS,,,,,The scaled elasticity matrix. The dimensions are reactions by species.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000538,safety factor on new step selection,safe|safety,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000538,,2020-10-29,JRK,,Odespy|JModelica|http://identifiers.org/biosimulators/gillespy2|SciPy,,safe|safety,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000468,maximal timestep method,,Hybrid simulation algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000352] combining Gibson and Bruck algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000027] with the Gillespie tau-leap method [http://www.biomodels.net/kisao/KISAO#KISAO_0000039].,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000468,,2014-04-25,AZ,,,,,Hybrid simulation algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000352] combining Gibson and Bruck algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000027] with the Gillespie tau-leap method [http://www.biomodels.net/kisao/KISAO#KISAO_0000039].,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000205,partitioning interval,,This positive integer value specifies after how many steps the internal partitioning of the system should be recalculated.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000205,,,,,,,,This positive integer value specifies after how many steps the internal partitioning of the system should be recalculated.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000030,Euler forward method,explicit Euler method|explicit Gaussian first order Runge-Kutta,The Euler method is an explicit one-step method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000261,,,,,kisao:KISAO_0000030,,2007-11-10,dk,,https://identifiers.org/biosimulators/ibiosim|https://identifiers.org/biosimulators/vcell,,explicit Euler method|explicit Gaussian first order Runge-Kutta,The Euler method is an explicit one-step method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t.,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,systems property,,A systems-level property of an entire model or simulation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:_KISAO_0000800,,06-03-2021,LPS,,,true,,A systems-level property of an entire model or simulation.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000686,Enzyme Cost Minimization,ECM,"For a given metabolic network model, the Enzyme Cost Minimization method determines plausible metabolite and enzyme concentrations (which determine thermodynamic forces and enzyme catalytic rates). Fluxes, enzyme kinetic constants, admissible metabolite concentration ranges, and enzyme cost weights (e.g. enzyme molecular masses) are given as input data. The method applies the principle that high enzyme (or enzyme plus metabolite) concentrations must be avoided, and maximizes a weighted sum of enzyme and metabolite concentrations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000685,,,,,kisao:KISAO_0000686,,2022-03-27,EN|JRK|WL,,https://gitlab.com/equilibrator/metabolic-pathway-analysis,,ECM,"For a given metabolic network model, the Enzyme Cost Minimization method determines plausible metabolite and enzyme concentrations (which determine thermodynamic forces and enzyme catalytic rates). Fluxes, enzyme kinetic constants, admissible metabolite concentration ranges, and enzyme cost weights (e.g. enzyme molecular masses) are given as input data. The method applies the principle that high enzyme (or enzyme plus metabolite) concentrations must be avoided, and maximizes a weighted sum of enzyme and metabolite concentrations.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000030,Euler forward method,explicit Euler method|explicit Gaussian first order Runge-Kutta,The Euler method is an explicit one-step method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000261,,,,,kisao:KISAO_0000030,,2007-11-10,dk,,http://identifiers.org/biosimulators/ibiosim|http://identifiers.org/biosimulators/vcell,,explicit Euler method|explicit Gaussian first order Runge-Kutta,The Euler method is an explicit one-step method for the numerical integration of ODES with a given initial value. The calculation of the next integration step at time t+1 is based on the state of the system at time point t.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000436,"Dormand-Prince 8(5,3) method",,This method is based on an 8(6) method by Dormand and Prince (i.e. order 8 for the integration and order 6 for error estimation) modified by Hairer and Wanner to use a 5th order error estimator with 3rd order correction.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000436,,2012-09-26,AZ,,,,,This method is based on an 8(6) method by Dormand and Prince (i.e. order 8 for the integration and order 6 for error estimation) modified by Hairer and Wanner to use a 5th order error estimator with 3rd order correction.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000403,homogeneousness of equation,,"Homogeneous equations are of the form Ly = 0, where the differential operator L is a linear operator and y is the unknown function.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000376,,,,,kisao:KISAO_0000403,,2011-07-19,AZ,,,,,"Homogeneous equations are of the form Ly = 0, where the differential operator L is a linear operator and y is the unknown function.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000614,Implementation,,"An implementation of an algorithm. For example, simulation tools can this parameter to differentiate among C, Python, and Java implementations of the same algorithms and allow investigators to select one of these specific implementations through SED-ML.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000614,,2021-01-25,JRK,,,,,"An implementation of an algorithm. For example, simulation tools can this parameter to differentiate among C, Python, and Java implementations of the same algorithms and allow investigators to select one of these specific implementations through SED-ML.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000614,implementation,,"An implementation of an algorithm. For example, simulation tools can this parameter to differentiate among C, Python, and Java implementations of the same algorithms and allow investigators to select one of these specific implementations through SED-ML.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000614,,2021-01-25,JRK,,,,,"An implementation of an algorithm. For example, simulation tools can this parameter to differentiate among C, Python, and Java implementations of the same algorithms and allow investigators to select one of these specific implementations through SED-ML.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000472,global optimization algorithm,global optimization method,"A global optimization algorithm is an optimization algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000470] that tries to find the global optimum of a function. If a function has several minima/maxima with in the allowed range of variable values, the global minimum/maximum is the one with the smallest/largest function value.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000470,,,,,kisao:KISAO_0000472,,2015-04-23,AZ,,,,global optimization method,"A global optimization algorithm is an optimization algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000470] that tries to find the global optimum of a function. If a function has several minima/maxima with in the allowed range of variable values, the global minimum/maximum is the one with the smallest/largest function value.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000845,sum,,The sum of a set of values. If the values contain NaN the sum is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000845,,2021-10-09,MK,,,,,The sum of a set of values. If the values contain NaN the sum is NaN.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000311,type of domain geometry handling,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000311,,,AZ,,,true,,,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000575,Hybrid tau-leaping method,,"A continuously coupled hybrid deterministic/stochastic simulation algorithm for biochemical networks. Biochemical species are classified as continuous, discrete, or switch. Tau-leaping is used to simulate stochastic species, and LSODA or another ODE integration method is used to simulate continuous species. Switch species are dynamically classified as either continuous or discrete at each timestep depending on a user defined error tolerance.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039|http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000575,,2020-11-06,BD|JRK,,https://identifiers.org/biosimulators/gillespy2,,,"A continuously coupled hybrid deterministic/stochastic simulation algorithm for biochemical networks. Biochemical species are classified as continuous, discrete, or switch. Tau-leaping is used to simulate stochastic species, and LSODA or another ODE integration method is used to simulate continuous species. Switch species are dynamically classified as either continuous or discrete at each timestep depending on a user defined error tolerance.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000542,correction step should use internally generated full Jacobian,with Jacobian|MF=22,Specifies whether the iteration method of the ODE solver’s correction step is chord iteration with an internally generated full Jacobian or functional iteration with no Jacobian. Option is only considered when the user has not supplied a Jacobian function and has not indicated (by setting either upper or lower band) that the Jacobian is banded.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000542,,2020-10-29,JRK,,https://identifiers.org/biosimulators/gillespy2|SciPy,,with Jacobian|MF=22,Specifies whether the iteration method of the ODE solver’s correction step is chord iteration with an internally generated full Jacobian or functional iteration with no Jacobian. Option is only considered when the user has not supplied a Jacobian function and has not indicated (by setting either upper or lower band) that the Jacobian is banded.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000288,backward differentiation formula,BDF|Gear method|Gear's method,The backward differentiation formulas (BDF) are implicit multistep methods based on the numerical differentiation of a given function and are wildly used for integration of stiff differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000281,,,,,kisao:KISAO_0000288,,2011-05-10,AZ,,ByoDyn|https://identifiers.org/biosimulators/ibiosim|GSL,,BDF|Gear method|Gear's method,The backward differentiation formulas (BDF) are implicit multistep methods based on the numerical differentiation of a given function and are wildly used for integration of stiff differential equations.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000575,hybrid tau-leaping method,,"A continuously coupled hybrid deterministic/stochastic simulation algorithm for biochemical networks. Biochemical species are classified as continuous, discrete, or switch. Tau-leaping is used to simulate stochastic species, and LSODA or another ODE integration method is used to simulate continuous species. Switch species are dynamically classified as either continuous or discrete at each timestep depending on a user defined error tolerance.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039|http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000575,,2020-11-06,BD|JRK,,http://identifiers.org/biosimulators/gillespy2,,,"A continuously coupled hybrid deterministic/stochastic simulation algorithm for biochemical networks. Biochemical species are classified as continuous, discrete, or switch. Tau-leaping is used to simulate stochastic species, and LSODA or another ODE integration method is used to simulate continuous species. Switch species are dynamically classified as either continuous or discrete at each timestep depending on a user defined error tolerance.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000542,correction step should use internally generated full Jacobian,with Jacobian|MF=22,Specifies whether the iteration method of the ODE solver’s correction step is chord iteration with an internally generated full Jacobian or functional iteration with no Jacobian. Option is only considered when the user has not supplied a Jacobian function and has not indicated (by setting either upper or lower band) that the Jacobian is banded.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000542,,2020-10-29,JRK,,http://identifiers.org/biosimulators/gillespy2|SciPy,,with Jacobian|MF=22,Specifies whether the iteration method of the ODE solver’s correction step is chord iteration with an internally generated full Jacobian or functional iteration with no Jacobian. Option is only considered when the user has not supplied a Jacobian function and has not indicated (by setting either upper or lower band) that the Jacobian is banded.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000288,backward differentiation formula,BDF|Gear method|Gear's method,The backward differentiation formulas (BDF) are implicit multistep methods based on the numerical differentiation of a given function and are wildly used for integration of stiff differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000281,,,,,kisao:KISAO_0000288,,2011-05-10,AZ,,ByoDyn|GSL|http://identifiers.org/biosimulators/ibiosim,,BDF|Gear method|Gear's method,The backward differentiation formulas (BDF) are implicit multistep methods based on the numerical differentiation of a given function and are wildly used for integration of stiff differential equations.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000627,diagonal approximate Jacobian solver,diagonal,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000624,,,,,kisao:KISAO_0000627,,2021-06-01,JRK,,CVODE,,diagonal,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000485,minimum step size,,"A lower limit, in the units of the bound variable over which a numerical integration is being performed, that a numerical integration algorithm with variable step size should take.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000485,,,AZ,,,,,"A lower limit, in the units of the bound variable over which a numerical integration is being performed, that a numerical integration algorithm with variable step size should take.",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000815,Flux control coefficient matrix (scaled),,The scaled flux control coefficient matrix. The dimensions are reactions by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000815,,06-03-2021,LPS,,,,,The scaled flux control coefficient matrix. The dimensions are reactions by reactions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000255,molecules per virtual box,,"Target molecules per virtual box is a parameter of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057], which sets the box sizes so that the average number of molecules per box, at simulation initiation, is close to the requested number.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000260,,,,,kisao:KISAO_0000255,,,,,Smoldyn,,,"Target molecules per virtual box is a parameter of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057], which sets the box sizes so that the average number of molecules per box, at simulation initiation, is close to the requested number.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000357,conjugate gradient method,CG,"Conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive-definite. The conjugate gradient method is an iterative method, so it can be applied to sparse systems that are too large to be handled by direct methods. Such systems often arise when numerically solving partial differential equations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000354,,,,,kisao:KISAO_0000357,,2011-06-10,AZ,,,,CG,"Conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive-definite. The conjugate gradient method is an iterative method, so it can be applied to sparse systems that are too large to be handled by direct methods. Such systems often arise when numerically solving partial differential equations.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000856,median ignoring NaN,,"The median of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000856,,2021-10-09,MK,,,,,"The median of a set of values, ignoring Nan entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000383,Bader-Deuflhard method,,The Bader-Deuflhard method is an extrapolation method based on a semi-implicit discretization [http://identifiers.org/biomodels.kisao/KISAO_0000387]. It is a generalization of the Bulirsch-Stoer algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000379] for solving ordinary differential equations.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000380,,,,,kisao:KISAO_0000383,,2011-07-01,AZ,,GSL,,,The Bader-Deuflhard method is an extrapolation method based on a semi-implicit discretization [http://identifiers.org/biomodels.kisao/KISAO_0000387]. It is a generalization of the Bulirsch-Stoer algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000379] for solving ordinary differential equations.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000038,sorting stochastic simulation algorithm,sorting direct method|sorting SSA,"In order to overcome the problem of high complexity of the Stochastic Simulation Algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] when simulating large systems, the sorting direct method maintains a loosely sorted order of the reactions as the simulation executes.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000038,,,,,,,sorting direct method|sorting SSA,"In order to overcome the problem of high complexity of the Stochastic Simulation Algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] when simulating large systems, the sorting direct method maintains a loosely sorted order of the reactions as the simulation executes.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000038,sorting stochastic simulation algorithm,sorting direct method|sorting SSA,"In order to overcome the problem of high complexity of the stochastic simulation algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] when simulating large systems, the sorting direct method maintains a loosely sorted order of the reactions as the simulation executes.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000038,,,,,,,sorting direct method|sorting SSA,"In order to overcome the problem of high complexity of the stochastic simulation algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000029] when simulating large systems, the sorting direct method maintains a loosely sorted order of the reactions as the simulation executes.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000394,quasi-minimal residual variant of biconjugate gradient stabilized method,QMRCGSTAB,"QMRCGSTAB is a quasi-minimal residual (QMR) variant of the Bi-CGSTAB algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000394] of van der Vorst for solving nonsymmetric linear systems. The motivation for the QMR variant is to obtain smoother convergence behavior of the underlying method. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000392,,,,,kisao:KISAO_0000394,,2011-07-19,AZ,,,,QMRCGSTAB,"QMRCGSTAB is a quasi-minimal residual (QMR) variant of the Bi-CGSTAB algorithm [http://identifiers.org/biomodels.kisao/KISAO_0000394] of van der Vorst for solving nonsymmetric linear systems. The motivation for the QMR variant is to obtain smoother convergence behavior of the underlying method. ",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000591,mdFBA,metabolic dilution flux balance analysis,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437,,,,,kisao:KISAO_0000591,,2020-10-29,JRK,,https://identifiers.org/biosimulators/cobratoolbox,,metabolic dilution flux balance analysis,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000591,mdFBA,metabolic dilution flux balance analysis,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437,,,,,kisao:KISAO_0000591,,2020-10-29,JRK,,http://identifiers.org/biosimulators/cobratoolbox,,metabolic dilution flux balance analysis,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000638,level,,A level such as of a qualitative variable.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000638,,2021-06-04,JRK,,,,,A level such as of a qualitative variable.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000496,CVODES,,"CVODES is a solver for stiff and nonstiff ODE systems (initial value problem) given in explicit form y’ = f(t,y,p) with sensitivity analysis capabilities (both forward and adjoint modes).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000496,,,,,https://identifiers.org/biosimulators/amici|SUNDIALS,,,"CVODES is a solver for stiff and nonstiff ODE systems (initial value problem) given in explicit form y’ = f(t,y,p) with sensitivity analysis capabilities (both forward and adjoint modes).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000496,CVODES,,"CVODES is a solver for stiff and nonstiff ODE systems (initial value problem) given in explicit form y’ = f(t,y,p) with sensitivity analysis capabilities (both forward and adjoint modes).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000496,,,,,http://identifiers.org/biosimulators/amici|SUNDIALS,,,"CVODES is a solver for stiff and nonstiff ODE systems (initial value problem) given in explicit form y’ = f(t,y,p) with sensitivity analysis capabilities (both forward and adjoint modes).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000664,Second order backward implicit product Euler scheme,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000664,Method for solving Volterra equations. Uses the trapezoidal rule.,2021-08-07,JRK,,http://identifiers.org/biosimulators/xpp,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000631,iterative root-finding method,,Iterative method for finding the root of a function (f(x) = 0).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000630,,,,,kisao:KISAO_0000631,,,2021-06-01|JRK,,,true,,Iterative method for finding the root of a function (f(x) = 0).,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000514,Nelder-Mead,simplex method,"This method also known as the simplex method is due to Nelder and Mead. A simplex is a polytope of N+1 vertices in N dimensions. The objective function is evaluated at each vertex. Dependent on these calculated values a new simplex is constructed. The simplest step is to replace the worst point with a point reflected through the centroid of the remaining N points. If this point is better than the best current point, then we can try stretching exponentially out along this line. On the other hand, if this new point isn't much better than the previous value then we are stepping across a valley, so we shrink the simplex towards the best point.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000514,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,simplex method,"This method also known as the simplex method is due to Nelder and Mead. A simplex is a polytope of N+1 vertices in N dimensions. The objective function is evaluated at each vertex. Dependent on these calculated values a new simplex is constructed. The simplest step is to replace the worst point with a point reflected through the centroid of the remaining N points. If this point is better than the best current point, then we can try stretching exponentially out along this line. On the other hand, if this new point isn't much better than the previous value then we are stepping across a valley, so we shrink the simplex towards the best point.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000318,Gauss-Legendre Runge-Kutta method,Open Formula,"So called 'Open Formula', two points formula, three points formula, four points formula, five points formula and six points formula of the Runge-Kutta method to solve the initial value problem of the ordinary differential equation. These formulas use the points and weights from the Gauss-Legendre Quadrature formulas for finding the value of the definite integral.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000318,,2011-05-26,AZ,,https://identifiers.org/biosimulators/ibiosim,,Open Formula,"So called 'Open Formula', two points formula, three points formula, four points formula, five points formula and six points formula of the Runge-Kutta method to solve the initial value problem of the ordinary differential equation. These formulas use the points and weights from the Gauss-Legendre Quadrature formulas for finding the value of the definite integral.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000514,Nelder-Mead,simplex method,"This method also known as the simplex method is due to Nelder and Mead. A simplex is a polytope of N+1 vertices in N dimensions. The objective function is evaluated at each vertex. Dependent on these calculated values a new simplex is constructed. The simplest step is to replace the worst point with a point reflected through the centroid of the remaining N points. If this point is better than the best current point, then we can try stretching exponentially out along this line. On the other hand, if this new point isn't much better than the previous value then we are stepping across a valley, so we shrink the simplex towards the best point.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000514,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,simplex method,"This method also known as the simplex method is due to Nelder and Mead. A simplex is a polytope of N+1 vertices in N dimensions. The objective function is evaluated at each vertex. Dependent on these calculated values a new simplex is constructed. The simplest step is to replace the worst point with a point reflected through the centroid of the remaining N points. If this point is better than the best current point, then we can try stretching exponentially out along this line. On the other hand, if this new point isn't much better than the previous value then we are stepping across a valley, so we shrink the simplex towards the best point.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000318,Gauss-Legendre Runge-Kutta method,Open Formula,"So called 'Open Formula', two points formula, three points formula, four points formula, five points formula and six points formula of the Runge-Kutta method to solve the initial value problem of the ordinary differential equation. These formulas use the points and weights from the Gauss-Legendre Quadrature formulas for finding the value of the definite integral.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000318,,2011-05-26,AZ,,http://identifiers.org/biosimulators/ibiosim,,Open Formula,"So called 'Open Formula', two points formula, three points formula, four points formula, five points formula and six points formula of the Runge-Kutta method to solve the initial value problem of the ordinary differential equation. These formulas use the points and weights from the Gauss-Legendre Quadrature formulas for finding the value of the definite integral.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000447,COAST,controllable approximative stochastic reaction algorithm,"An approximative algorithm for stochastic simulations of chemical reaction systems based on three different modeling levels: for small numbers of particles an exact [http://identifiers.org/biomodels.kisao/KISAO_0000236] stochastic [http://identifiers.org/biomodels.kisao/KISAO_0000104] model; for intermediate numbers an approximative [http://identifiers.org/biomodels.kisao/KISAO_0000237], but computationally more efficient stochastic [http://identifiers.org/biomodels.kisao/KISAO_0000104] model based on discrete Gaussian distributions; and for large numbers the deterministic [http://identifiers.org/biomodels.kisao/KISAO_0000103] reaction kinetics. In every simulation time step, the subdivision of the reaction channels into the three different modeling levels is done automatically, where all approximations applied can be controlled by a single error parameter for which an appropriate value can easily be found.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000447,,2013-01-28,AZ,,,,controllable approximative stochastic reaction algorithm,"An approximative algorithm for stochastic simulations of chemical reaction systems based on three different modeling levels: for small numbers of particles an exact [http://identifiers.org/biomodels.kisao/KISAO_0000236] stochastic [http://identifiers.org/biomodels.kisao/KISAO_0000104] model; for intermediate numbers an approximative [http://identifiers.org/biomodels.kisao/KISAO_0000237], but computationally more efficient stochastic [http://identifiers.org/biomodels.kisao/KISAO_0000104] model based on discrete Gaussian distributions; and for large numbers the deterministic [http://identifiers.org/biomodels.kisao/KISAO_0000103] reaction kinetics. In every simulation time step, the subdivision of the reaction channels into the three different modeling levels is done automatically, where all approximations applied can be controlled by a single error parameter for which an appropriate value can easily be found.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000540,maximum factor to change step size by,ifactor|fac2,Maximum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000540,,2020-10-29,JRK,,Odespy|https://identifiers.org/biosimulators/gillespy2|JModelica|SciPy,,ifactor|fac2,Maximum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000573,probabilistic logical model simulation method,,"Qualitative (logical) models specify the evolution rules of their components. Probabilistic networks allow for specifying more than one transition function per variable/gene. Each of these functions has a probability to be chosen, where the probabilities of all functions for one variable sum up to 1. Transitions are performed synchronously by choosing one transition function for each gene according to their probabilities and applying them to the current state.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000319|http://www.biomodels.net/kisao/KISAO#KISAO_0000448,,,,,kisao:KISAO_0000573,,2020-10-29,JRK,,https://identifiers.org/biosimulators/boolnet,,,"Qualitative (logical) models specify the evolution rules of their components. Probabilistic networks allow for specifying more than one transition function per variable/gene. Each of these functions has a probability to be chosen, where the probabilities of all functions for one variable sum up to 1. Transitions are performed synchronously by choosing one transition function for each gene according to their probabilities and applying them to the current state.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000817,Kernel matrix,,The Kernel matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:KISAO_0000817,,06-03-2021,LPS,,,,,The Kernel matrix of a model.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000479,upper half-bandwidth,,the upper half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000482,,,,,kisao:KISAO_0000479,,2015-09-10,AZ,,https://identifiers.org/biosimulators/opencor,,,the upper half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000540,maximum factor to change step size by,ifactor|fac2,Maximum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000540,,2020-10-29,JRK,,Odespy|JModelica|http://identifiers.org/biosimulators/gillespy2|SciPy,,ifactor|fac2,Maximum factor to increase/decrease step size by in one step. The new step-size is chosen subject to the restriction fac1 <= current step-size / old step-size <= fac2.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000573,probabilistic logical model simulation method,,"Qualitative (logical) models specify the evolution rules of their components. Probabilistic networks allow for specifying more than one transition function per variable/gene. Each of these functions has a probability to be chosen, where the probabilities of all functions for one variable sum up to 1. Transitions are performed synchronously by choosing one transition function for each gene according to their probabilities and applying them to the current state.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000319|http://www.biomodels.net/kisao/KISAO#KISAO_0000448,,,,,kisao:KISAO_0000573,,2020-10-29,JRK,,http://identifiers.org/biosimulators/boolnet,,,"Qualitative (logical) models specify the evolution rules of their components. Probabilistic networks allow for specifying more than one transition function per variable/gene. Each of these functions has a probability to be chosen, where the probabilities of all functions for one variable sum up to 1. Transitions are performed synchronously by choosing one transition function for each gene according to their probabilities and applying them to the current state.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000690,biological system,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000688,,,,,kisao:KISAO_0000690,,2022-03-29,EN|JRK|WL,,,,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000817,kernel matrix,,The Kernel matrix of a model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000817,,06-03-2021,LPS,,,,,The Kernel matrix of a model.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000479,upper half-bandwidth,,the upper half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000482,,,,,kisao:KISAO_0000479,,2015-09-10,AZ,,http://identifiers.org/biosimulators/opencor,,,the upper half-bandwidth value used by the Banded linear solver or preconditioner (a value between 0 and n-1 with n the number of ODEs/DAEs in the model).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000249,critical firing threshold,nonnegative tau-leaping second control parameter,"The 'nonnegative Poisson tau-leaping method' [http://identifiers.org/biomodels.kisao/KISAO_0000084] is based on the fact that negative populations typically arise from multiple firings of reactions that are only a few firings away from consuming all the molecules of one of their reactants. To focus on those reaction channels, the modified tau-leaping algorithm introduces a second control parameter nc, a positive integer that is usually set somewhere between 5 and 20. Any reaction channel with a positive propensity function that is currently within nc firings of exhausting one of its reactants is then classified as a critical reaction. The modified algorithm chooses tau in such a way that no more than one firing of all the critical reactions can occur during the leap.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000249,,,,,,,nonnegative tau-leaping second control parameter,"The 'nonnegative Poisson tau-leaping method' [http://identifiers.org/biomodels.kisao/KISAO_0000084] is based on the fact that negative populations typically arise from multiple firings of reactions that are only a few firings away from consuming all the molecules of one of their reactants. To focus on those reaction channels, the modified tau-leaping algorithm introduces a second control parameter nc, a positive integer that is usually set somewhere between 5 and 20. Any reaction channel with a positive propensity function that is currently within nc firings of exhausting one of its reactants is then classified as a critical reaction. The modified algorithm chooses tau in such a way that no more than one firing of all the critical reactions can occur during the leap.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000019,CVODE,VODEPK|code value ordinary differential equation solver|VODE,"The CVODE is a package written in C that solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000019,,2007-11-30,dk,,https://identifiers.org/biosimulators/jsim|SBML-SAT|https://identifiers.org/biosimulators/tellurium|https://identifiers.org/biosimulators/bionetgen|libRoadRunner|https://identifiers.org/biosimulators/vcell|https://identifiers.org/biosimulators/pysces|SUNDIALS|https://identifiers.org/biosimulators/opencor,,VODEPK|code value ordinary differential equation solver|VODE,"The CVODE is a package written in C that solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000216,integrate reduced model,,"This parameter is a boolean value to determine whether the integration shall be performed using the mass conservation laws (true), i.e., reducing the number of system variables or to use the complete model (false).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000216,,,,,,,,"This parameter is a boolean value to determine whether the integration shall be performed using the mass conservation laws (true), i.e., reducing the number of system variables or to use the complete model (false).",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000586,Gibson-Bruck next reaction algorithm with indexed priority queue,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000027,,,,,kisao:KISAO_0000586,,2020-10-29,JRK,,https://identifiers.org/biosimulators/sbscl,,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000019,CVODE,VODEPK|code value ordinary differential equation solver|VODE,"The CVODE is a package written in C that solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000019,,2007-11-30,dk,,SBML-SAT|http://identifiers.org/biosimulators/opencor|http://identifiers.org/biosimulators/tellurium|libRoadRunner|http://identifiers.org/biosimulators/jsim|http://identifiers.org/biosimulators/vcell|SUNDIALS|http://identifiers.org/biosimulators/bionetgen|http://identifiers.org/biosimulators/pysces,,VODEPK|code value ordinary differential equation solver|VODE,"The CVODE is a package written in C that solves ODE initial value problems, in real N-space, written as y'=f(t,y), y(t0)=y0. It is capable for stiff and non-stiff systems and uses two different linear multi-step methods, namely the Adam-Moulton [http://identifiers.org/biomodels.kisao/KISAO_0000280] method and the backward differentiation formula [http://identifiers.org/biomodels.kisao/KISAO_0000288].",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000216,use reduced model,integrate reduced model,"Boolean value which indicates whether the simulation/analysis should be performed using the complete model or an equivalent reduced model. + +Reduced models can be determined in many ways such as using mass conservation laws.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000216,,,,,,,integrate reduced model,"Boolean value which indicates whether the simulation/analysis should be performed using the complete model or an equivalent reduced model. + +Reduced models can be determined in many ways such as using mass conservation laws.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000810,reduced eigenvalue matrix,,"The reduced eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000810,,06-03-2021,LPS,,,,,"The reduced eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000843,standard error,,The standard error of a set of values. If the values contain NaN the standard deviation is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000843,,2021-10-09,MK,,,,,The standard error of a set of values. If the values contain NaN the standard deviation is NaN.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000586,Gibson-Bruck next reaction algorithm with indexed priority queue,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000027,,,,,kisao:KISAO_0000586,,2020-10-29,JRK,,http://identifiers.org/biosimulators/sbscl,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000821,intensive property,,An intensive variable such as a concentration or temperature.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000821,,06-03-2021,LPS,,,true,,An intensive variable such as a concentration or temperature.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000658,logical model analysis method,,"A method for analyzing a logical model, such as finding its fixed points.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000658,,2021-07-08,JRK,,,true,,"A method for analyzing a logical model, such as finding its fixed points.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000483,step size,,the size of every step of the algorithm,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000483,,2015-09-10,AZ,,,,,the size of every step of the algorithm,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000625,dense direct solver,denese,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000624,,,,,kisao:KISAO_0000625,,2021-06-01,JRK,,CVODE,,denese,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000253,coarse-graining factor,,"The time in each Monte-Carlo iteration of 'binomial tau-leaping method' [http://identifiers.org/biomodels.kisao/KISAO_0000074] is updated with the time increments tau=f/(a1+a2+...+aM). Here 1/(a1+a2+...+aM) is the averaged microscopic increment of the SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] and f is a coarse-graining factor, controlling the speed-up.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000253,,,,,,,,"The time in each Monte-Carlo iteration of 'binomial tau-leaping method' [http://identifiers.org/biomodels.kisao/KISAO_0000074] is updated with the time increments tau=f/(a1+a2+...+aM). Here 1/(a1+a2+...+aM) is the averaged microscopic increment of the SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] and f is a coarse-graining factor, controlling the speed-up.",,,,,,, @@ -429,55 +480,56 @@ DASPK is written in Fortran.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000322,hybridity,,The basic idea of hybrid simulation methods is to combine the advantages of complementary simulation approaches: the whole system is subdivided into appropriate parts and different simulation methods operate on these parts at the same time.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000322,,2011-05-27,AZ,,,,,The basic idea of hybrid simulation methods is to combine the advantages of complementary simulation approaches: the whole system is subdivided into appropriate parts and different simulation methods operate on these parts at the same time.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000419,metamodelling method,,"Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000419,,2012-01-18,AZ,,,true,,"Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000299,Butcher-Kuntzmann method,Gauss method,"From a theoretical point of view, the Butcher-Kuntzmann Runge-Kutta methods belong to the best step-by-step methods for nonstiff problems. These methods integrate first-order initial-value problems by means of formulas based on Gauss-Legendre quadrature, and combine excellent stability features with the property of superconvergence at the step points.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000299,,2011-05-10,AZ,,,,Gauss method,"From a theoretical point of view, the Butcher-Kuntzmann Runge-Kutta methods belong to the best step-by-step methods for nonstiff problems. These methods integrate first-order initial-value problems by means of formulas based on Gauss-Legendre quadrature, and combine excellent stability features with the property of superconvergence at the step points.",,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000814,Flux control coefficient matrix (unscaled),,The unscaled flux control coefficient matrix. The dimensions are reactions by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000814,,06-03-2021,LPS,,,,,The unscaled flux control coefficient matrix. The dimensions are reactions by reactions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000233,LSODES,Livermore solver for ordinary differential equations with general sparse Jacobian matrix,"LSODES solves systems dy/dt = f and in the stiff case treats the Jacobian matrix in general sparse form. It determines the sparsity structure on its own, or optionally accepts this information from the user. It then uses parts of the Yale Sparse Matrix Package (YSMP) to solve the linear systems that arise, by a sparse (direct) LU factorization/backsolve method.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000233,,,,,,,Livermore solver for ordinary differential equations with general sparse Jacobian matrix,"LSODES solves systems dy/dt = f and in the stiff case treats the Jacobian matrix in general sparse form. It determines the sparsity structure on its own, or optionally accepts this information from the user. It then uses parts of the Yale Sparse Matrix Package (YSMP) to solve the linear systems that arise, by a sparse (direct) LU factorization/backsolve method.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000091,LSODIS,"Livermore solver for ordinary differential equations, implicit sparse version","LSODIS is a set of general-purpose FORTRAN routines solver for the initial value problem for ordinary differential equation systems. It is suitable for both stiff and nonstiff systems. LSODIS treat systems in the linearly implicit form A(t,y) dy/dt = g(t,y), A = a square matrix, i.e. with the derivative dy/dt implicit, but linearly so.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000091,,2007-11-30,dk,,,,"Livermore solver for ordinary differential equations, implicit sparse version","LSODIS is a set of general-purpose FORTRAN routines solver for the initial value problem for ordinary differential equation systems. It is suitable for both stiff and nonstiff systems. LSODIS treat systems in the linearly implicit form A(t,y) dy/dt = g(t,y), A = a square matrix, i.e. with the derivative dy/dt implicit, but linearly so.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000392,biconjugate gradient stabilized method,Bi-CGSTAB|BiCGSTAB,An iterative method for the numerical solution of nonsymmetric linear systems. It is a variant of the biconjugate gradient method (BiCG) [http://identifiers.org/biomodels.kisao/KISAO_0000358] and has faster and smoother convergence than the original BiCG.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000395,,,,,kisao:KISAO_0000392,,,,,CVODE,,Bi-CGSTAB|BiCGSTAB,An iterative method for the numerical solution of nonsymmetric linear systems. It is a variant of the biconjugate gradient method (BiCG) [http://identifiers.org/biomodels.kisao/KISAO_0000358] and has faster and smoother convergence than the original BiCG.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000636,primary property,,A primary output of a simulation.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000636,,2021-06-04,JRK,,,,,A primary output of a simulation.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000603,Minimum reaction rate for continuous approximation,Lambda,Minimum reaction rate required for approximation as a continuous Markov process.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000603,,2021-01-13,MLB|JRK,,,,Lambda,Minimum reaction rate required for approximation as a continuous Markov process.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000603,minimum reaction rate for continuous approximation,lambda|Lambda,Minimum reaction rate required for approximation as a continuous Markov process.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000603,,2021-01-13,MLB|JRK,,,,lambda|Lambda,Minimum reaction rate required for approximation as a continuous Markov process.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000068,deterministic cellular automata update algorithm,,"A cellular automaton is a discrete model of a regular grid of cells with a finite number of dimensions. Each cell has a finite number of defined states. The automaton changes its state in a discrete manner, meaning that the state of a cell at time t is determined by a function of the states of its neighbours at time t - 1. These neighbours are a selection of cells relative to the specified cell. Famous examples for deterministic cellular automata are Conway's game of life or Wolfram's elementary cellular automata.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000264,,,,,kisao:KISAO_0000068,,2007-11-30,dk,,,,,"A cellular automaton is a discrete model of a regular grid of cells with a finite number of dimensions. Each cell has a finite number of defined states. The automaton changes its state in a discrete manner, meaning that the state of a cell at time t is determined by a function of the states of its neighbours at time t - 1. These neighbours are a selection of cells relative to the specified cell. Famous examples for deterministic cellular automata are Conway's game of life or Wolfram's elementary cellular automata.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000558,relative steady-state tolerance,,Relative error tolerance of the steady-state.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000558,,2020-10-29,JRK,,https://identifiers.org/biosimulators/amici|SUNDIALS,,,Relative error tolerance of the steady-state.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000525,stop condition,,A condition upon which a simulation should terminate.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000525,,2020-05-29,AZ,,https://identifiers.org/biosimulators/bionetgen,,,A condition upon which a simulation should terminate.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000675,Broyden method,,Family of Quasi-Newton methods for finding roots in k variables originally described by C. G. Broyden in 1965.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000675,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Family of Quasi-Newton methods for finding roots in k variables originally described by C. G. Broyden in 1965.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000558,relative steady-state tolerance,,Relative error tolerance of the steady-state.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000209,,,,,kisao:KISAO_0000558,,2020-10-29,JRK,,http://identifiers.org/biosimulators/amici|SUNDIALS,,,Relative error tolerance of the steady-state.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000525,stop condition,,A condition upon which a simulation should terminate.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000525,,2020-05-29,AZ,,http://identifiers.org/biosimulators/bionetgen,,,A condition upon which a simulation should terminate.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000642,minimum flux,,"Minimum possible flux such as computed by flux variability analysis (FVA, KISAO_0000526).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000639,,,,,kisao:KISAO_0000642,,2021-06-04,JRK,,,,,"Minimum possible flux such as computed by flux variability analysis (FVA, KISAO_0000526).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000423,partial least squares regression-like method,,Method for building regression models between independent and dependent variables.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000419,,,,,kisao:KISAO_0000423,,2012-01-18,AZ,,,true,,Method for building regression models between independent and dependent variables.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000854,length ignoring NaN,,"The number of elements of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000854,,2021-10-09,MK,,,,,"The number of elements of a set of values, ignoring Nan entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000353,generalized minimal residual algorithm,GMRES,"An iterative method for solving linear systems, which has the property of minimizing at every step the norm of the residual vector over a Krylov subspace. The generalized minimal residual method extends the minimal residual method (MINRES) [http://identifiers.org/biomodels.kisao/KISAO_0000388], which is only applicable to symmetric systems, to non-symmetric systems.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000354,,,,,kisao:KISAO_0000353,,2011-06-10,AZ,,CVODE,,GMRES,"An iterative method for solving linear systems, which has the property of minimizing at every step the norm of the residual vector over a Krylov subspace. The generalized minimal residual method extends the minimal residual method (MINRES) [http://identifiers.org/biomodels.kisao/KISAO_0000388], which is only applicable to symmetric systems, to non-symmetric systems.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000320,BioRica hybrid method,,The simulation schema for a given BioRica node is given by a hybrid algorithm that deals with continuous time and allows for discrete events that roll back the time according to these discrete interruptions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000320,,2011-05-26,AZ,,BioRica,,,The simulation schema for a given BioRica node is given by a hybrid algorithm that deals with continuous time and allows for discrete events that roll back the time according to these discrete interruptions.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000455,prioritized updating policy,,An updating policy that chooses a transition in a prioritized way.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000453,,,,,kisao:KISAO_0000455,,2013-01-28,AZ,,,,,An updating policy that chooses a transition in a prioritized way.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000584,initial order,,Initial order of method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000594,,,,,kisao:KISAO_0000584,,2020-10-29,JRK,,https://identifiers.org/biosimulators/jsim,,,Initial order of method.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000584,initial order,,Initial order of method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000594,,,,,kisao:KISAO_0000584,,2020-10-29,JRK,,http://identifiers.org/biosimulators/jsim,,,Initial order of method.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000551,primal-dual interior point method,,The Interior Point method approximates the constraints of a linear programming model as a set of boundaries surrounding a region.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000547,,,,,kisao:KISAO_0000551,,2020-10-29,JRK,,CPLEX|GLPK|Gurobi,,,The Interior Point method approximates the constraints of a linear programming model as a set of boundaries surrounding a region.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000828,maximum,,The maximum value of a set of values.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000828,,06-03-2021,LPS,,,,,The maximum value of a set of values.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000828,maximum ignoring NaN,,"The maximum value of a set of values, ignoring NaN entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000828,,2021-06-03,"LPS, MK",,,,,"The maximum value of a set of values, ignoring NaN entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000596,srFBA,SR-FBA|steady-state regulatory flux balance analysis,Method for predicting steady-state metabolic fluxes under patterns of the regulation of gene expression,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437|http://www.biomodels.net/kisao/KISAO#KISAO_0000595,,,,,kisao:KISAO_0000596,,2020-10-29,JRK,,,,SR-FBA|steady-state regulatory flux balance analysis,Method for predicting steady-state metabolic fluxes under patterns of the regulation of gene expression,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000601,Number of trials,trials,"Number of multiple trials (e.g., of a scatter search method).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000601,,2021-01-13,MLB|JRK,,,,trials,"Number of multiple trials (e.g., of a scatter search method).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000601,number of trials,trials,"Number of multiple trials (e.g., of a scatter search method).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000601,,2021-01-13,MLB|JRK,,,,trials,"Number of multiple trials (e.g., of a scatter search method).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000333,accelerated stochastic simulation algorithm,accelerated SSA,"An algorithm, which accelerates SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] either at the expense of its accuracy or exact.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000241,,,,,kisao:KISAO_0000333,,2011-06-03,AZ,,,true,accelerated SSA,"An algorithm, which accelerates SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] either at the expense of its accuracy or exact.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000366,symplecticness,,"Roughly speaking, ‘symplecticness’ is a characteristic property possessed by the solutions of Hamiltonian problems. A numerical method is called symplectic if, when applied to Hamiltonian problems, it generates numerical solutions which inherit the property of symplecticness (phase volume preservation).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000366,,2011-06-16,AZ,,,,,"Roughly speaking, ‘symplecticness’ is a characteristic property possessed by the solutions of Hamiltonian problems. A numerical method is called symplectic if, when applied to Hamiltonian problems, it generates numerical solutions which inherit the property of symplecticness (phase volume preservation).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000103,deterministic system behaviour,,"Algorithm, possessing this characteristic, simulates the temporal evolution of a system deterministically, so that from a precise initial state the algorithm will always end up in the same final state.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000099,,,,,kisao:KISAO_0000103,,2008-07-08,NLN,,,,,"Algorithm, possessing this characteristic, simulates the temporal evolution of a system deterministically, so that from a precise initial state the algorithm will always end up in the same final state.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000832,time,,The implied time variable of the model state.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000832,,06-03-2021,LPS,,,,,The implied time variable of the model state.,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000812,Jacobian matrix,Full Jacobian matrix,The (full) Jacobian matrix. The dimensions are species by species.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000812,,06-03-2021,LPS,,,,Full Jacobian matrix,The (full) Jacobian matrix. The dimensions are species by species.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000669,Resource Balance Analysis,RBA,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000685|http://www.biomodels.net/kisao/KISAO#KISAO_0000407,,,,,kisao:KISAO_0000669,,2021-08-12,JRK,,http://identifiers.org/biosimulators/rbapy,,RBA,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000264,cellular automata update method,cellular automata|CA|iterative arrays|tessellation structures|tessellation automata|cellular structures|cellular spaces|homogeneous structures,"Cellular automata are mathematical idealizations of physical systems in which space and time are discrete, and physical quantities take on a finite set of discrete values. A cellular automaton consists of a regular uniform lattice (or ''array''), usually infinite in extent, with a discrete variable at each site (''cell''). A cellular automaton evolves in discrete time steps, with the value of the variable at one site being affected by the values of variables at sites in its ''neighbourhood'' on the previous time step. The neighbourhood of a site is typically taken to be the site itself and all immediately adjacent sites. The variables at each site are updated simultaneously (''synchronously''), based on the values of the variables in their neighbourhood at the preceding time step, and according to a definite set of ''local rules''.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000363,,,,,kisao:KISAO_0000264,,2011-04-07,AZ,,,,cellular automata|CA|iterative arrays|tessellation structures|tessellation automata|cellular structures|cellular spaces|homogeneous structures,"Cellular automata are mathematical idealizations of physical systems in which space and time are discrete, and physical quantities take on a finite set of discrete values. A cellular automaton consists of a regular uniform lattice (or ''array''), usually infinite in extent, with a discrete variable at each site (''cell''). A cellular automaton evolves in discrete time steps, with the value of the variable at one site being affected by the values of variables at sites in its ''neighbourhood'' on the previous time step. The neighbourhood of a site is typically taken to be the site itself and all immediately adjacent sites. The variables at each site are updated simultaneously (''synchronously''), based on the values of the variables in their neighbourhood at the preceding time step, and according to a definite set of ''local rules''.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000297,Lobatto method,implicit Runge-Kutta method based on Lobatto quadrature,"There are three families of Lobatto methods, called IIIA, IIIB and IIIC. These are named after Rehuel Lobatto. All are implicit Runge-Kutta methods, have order 2s − 2 and they all have c1 = 0 and cs = 1.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000297,,2011-05-10,AZ,,,,implicit Runge-Kutta method based on Lobatto quadrature,"There are three families of Lobatto methods, called IIIA, IIIB and IIIC. These are named after Rehuel Lobatto. All are implicit Runge-Kutta methods, have order 2s − 2 and they all have c1 = 0 and cs = 1.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000519,population size,,"The parameter is a positive integer value to determine the size of the population, i.e., the number of individuals that survive after each generation.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000518,,,,,kisao:KISAO_0000519,,2019-01-18,AZ,,,,,"The parameter is a positive integer value to determine the size of the population, i.e., the number of individuals that survive after each generation.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000494,fully asynchronous updating policy,,An updating policy where all enabled transitions occur either independently or (partially) simultaneously. (i.e. considering all possible combinations of enabled transitions). Thus a state has as many successors as the number of combinations of transitions enabled in this state.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000492,,,,,kisao:KISAO_0000494,,,,,,,,An updating policy where all enabled transitions occur either independently or (partially) simultaneously. (i.e. considering all possible combinations of enabled transitions). Thus a state has as many successors as the number of combinations of transitions enabled in this state.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000231,Pahle hybrid method,,"The hybrid method combines the stochastic 'Gibson-Bruck's next reaction method' [http://identifiers.org/biomodels.kisao/KISAO_0000027] with different algorithms for the numerical integration of ODEs [http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000374]. The biochemical network is dynamically partitioned into a deterministic and a stochastic subnet depending on the current particle numbers in the system. The user can define limits for when a particle number should be considered low or high. The stochastic subnet contains reactions involving low numbered species as substrate or product. All the other reactions form the deterministic subnet. The two subnets are then simulated in parallel using the stochastic and deterministic solver, respectively. The reaction probabilities in the stochastic subnet are approximated as constant between two stochastic reaction events.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000231,,,AZ,,https://identifiers.org/biosimulators/copasi,true,,"The hybrid method combines the stochastic 'Gibson-Bruck's next reaction method' [http://identifiers.org/biomodels.kisao/KISAO_0000027] with different algorithms for the numerical integration of ODEs [http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000374]. The biochemical network is dynamically partitioned into a deterministic and a stochastic subnet depending on the current particle numbers in the system. The user can define limits for when a particle number should be considered low or high. The stochastic subnet contains reactions involving low numbered species as substrate or product. All the other reactions form the deterministic subnet. The two subnets are then simulated in parallel using the stochastic and deterministic solver, respectively. The reaction probabilities in the stochastic subnet are approximated as constant between two stochastic reaction events.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000231,Pahle hybrid method,,"The hybrid method combines the stochastic 'Gibson-Bruck's next reaction method' [http://identifiers.org/biomodels.kisao/KISAO_0000027] with different algorithms for the numerical integration of ODEs [http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000374]. The biochemical network is dynamically partitioned into a deterministic and a stochastic subnet depending on the current particle numbers in the system. The user can define limits for when a particle number should be considered low or high. The stochastic subnet contains reactions involving low numbered species as substrate or product. All the other reactions form the deterministic subnet. The two subnets are then simulated in parallel using the stochastic and deterministic solver, respectively. The reaction probabilities in the stochastic subnet are approximated as constant between two stochastic reaction events.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000231,,,AZ,,http://identifiers.org/biosimulators/copasi,true,,"The hybrid method combines the stochastic 'Gibson-Bruck's next reaction method' [http://identifiers.org/biomodels.kisao/KISAO_0000027] with different algorithms for the numerical integration of ODEs [http://identifiers.org/biomodels.kisao/KISAO_0000245 some http://identifiers.org/biomodels.kisao/KISAO_0000374]. The biochemical network is dynamically partitioned into a deterministic and a stochastic subnet depending on the current particle numbers in the system. The user can define limits for when a particle number should be considered low or high. The stochastic subnet contains reactions involving low numbered species as substrate or product. All the other reactions form the deterministic subnet. The two subnets are then simulated in parallel using the stochastic and deterministic solver, respectively. The reaction probabilities in the stochastic subnet are approximated as constant between two stochastic reaction events.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000647,derivative,,Rate of change of a variable with respect to another variable.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000647,,2021-06-04,JRK,,,,,Rate of change of a variable with respect to another variable.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000673,skip reactions that produce negative species amounts,,Parameter which instructs a simulation tool to skip reactions that would result in negative amounts of species.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000673,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Parameter which instructs a simulation tool to skip reactions that would result in negative amounts of species.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000046,trapezoidal tau-leaping method,trapezoidal implicit tau-leaping method,"Formula for accelerated discrete efficient stochastic simulation of chemically reacting system [which] has better accuracy and stiff stability properties than the explicit and implicit [http://identifiers.org/biomodels.kisao/KISAO_0000045] tau-leaping formulas for discrete stochastic systems, and it limits to the trapezoidal rule in the deterministic regime.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000039,,,,,kisao:KISAO_0000046,,2007-10-16,dk,,,,trapezoidal implicit tau-leaping method,"Formula for accelerated discrete efficient stochastic simulation of chemically reacting system [which] has better accuracy and stiff stability properties than the explicit and implicit [http://identifiers.org/biomodels.kisao/KISAO_0000045] tau-leaping formulas for discrete stochastic systems, and it limits to the trapezoidal rule in the deterministic regime.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000640,lower bound,,A lower bound on an estimate of a quantity.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000641,,,,,kisao:KISAO_0000640,,2021-06-04,JRK,,,,,A lower bound on an estimate of a quantity.,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000813,Eigenvalue matrix,Full eigenvalue matrix,"The (full) eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000813,,06-03-2021,LPS,,,,Full eigenvalue matrix,"The (full) eigenvalue matrix of a model. The dimensions are species by two, where the first column is the real part of the eigenvalues, and the second column is the imaginary part of the eigenvalues.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000370,type of problem,,A characteristic describing the type of the problems which can be solved by the algorithm.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000370,,2011-06-29,AZ,,,true,,A characteristic describing the type of the problems which can be solved by the algorithm.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000839,temperature,,The intensive quantity temperature.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000839,,06-03-2021,LPS,,,,,The intensive quantity temperature.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000434,Higham-Hall method,RK5(4)7FEql,"The equilibrium theory of Hall and Higham (1988) can be used to determine whether a Runge-Kutta algorithm will perform smoothly when stability restricts the stepsize. Higham-Hall method is a fifth order embedded Runge-Kutta method [http://identifiers.org/biomodels.kisao/KISAO_0000302], which behaves smoothly with respect to the standard type of stepsize controllers.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000435,,,,,kisao:KISAO_0000434,,2012-09-25,AZ,,,,RK5(4)7FEql,"The equilibrium theory of Hall and Higham (1988) can be used to determine whether a Runge-Kutta algorithm will perform smoothly when stability restricts the stepsize. Higham-Hall method is a fifth order embedded Runge-Kutta method [http://identifiers.org/biomodels.kisao/KISAO_0000302], which behaves smoothly with respect to the standard type of stepsize controllers.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000569,NLEQ2,Numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems|Newton-type method for solveing non-linear (NL) equations (EQ),"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. +http://www.biomodels.net/kisao/KISAO#KISAO_0000569,NLEQ2,Newton-type method for solveing non-linear (NL) equations (EQ)|numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems,"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. -Global Newton method with error oriented convergence criterion; QR-decomposition with subcondition number estimate.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000569,,2020-10-29,JRK,,https://identifiers.org/biosimulators/tellurium,,Numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems|Newton-type method for solveing non-linear (NL) equations (EQ),"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. +Global Newton method with error oriented convergence criterion; QR-decomposition with subcondition number estimate.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000569,,2020-10-29,JRK,,http://identifiers.org/biosimulators/tellurium,,Newton-type method for solveing non-linear (NL) equations (EQ)|numerical solution of nonlinear (NL) equations (EQ) especially designed for numerically sensitive problems,"Damped Newton-algorithm with rank strategy for systems of highly nonlinear equations. Global Newton method with error oriented convergence criterion; QR-decomposition with subcondition number estimate.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000306,Lagrangian sliding fluid element algorithm,LSFEA|BTEX|blood-tissue exchange method,"Because the analytic solutions to the partial differential equations require convolution integration, solutions are obtained relatively efficiently by a fast numerical method. Our approach centers on the use of a sliding fluid element algorithm for capillary convection, with the time step set equal to the length step divided by the fluid velocity. Radial fluxes by permeation between plasma, interstitial fluid, and cells and axial diffusion exchanges within each time step are calculated analytically. The method enforces mass conservation unless there is regional consumption.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000306,,2011-05-11,AZ,,https://identifiers.org/biosimulators/jsim,,LSFEA|BTEX|blood-tissue exchange method,"Because the analytic solutions to the partial differential equations require convolution integration, solutions are obtained relatively efficiently by a fast numerical method. Our approach centers on the use of a sliding fluid element algorithm for capillary convection, with the time step set equal to the length step divided by the fluid velocity. Radial fluxes by permeation between plasma, interstitial fluid, and cells and axial diffusion exchanges within each time step are calculated analytically. The method enforces mass conservation unless there is regional consumption.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000536,ZVODE,"Complex-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation",ZVODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000536,,2020-10-29,JRK,,Odespy|https://identifiers.org/biosimulators/gillespy2|deSolve|SciPy,,"Complex-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation",ZVODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000306,Lagrangian sliding fluid element algorithm,LSFEA|BTEX|blood-tissue exchange method,"Because the analytic solutions to the partial differential equations require convolution integration, solutions are obtained relatively efficiently by a fast numerical method. Our approach centers on the use of a sliding fluid element algorithm for capillary convection, with the time step set equal to the length step divided by the fluid velocity. Radial fluxes by permeation between plasma, interstitial fluid, and cells and axial diffusion exchanges within each time step are calculated analytically. The method enforces mass conservation unless there is regional consumption.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000306,,2011-05-11,AZ,,http://identifiers.org/biosimulators/jsim,,LSFEA|BTEX|blood-tissue exchange method,"Because the analytic solutions to the partial differential equations require convolution integration, solutions are obtained relatively efficiently by a fast numerical method. Our approach centers on the use of a sliding fluid element algorithm for capillary convection, with the time step set equal to the length step divided by the fluid velocity. Radial fluxes by permeation between plasma, interstitial fluid, and cells and axial diffusion exchanges within each time step are calculated analytically. The method enforces mass conservation unless there is regional consumption.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000536,ZVODE,"Complex-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation|complex-valued variable-coefficient ordinary differential equation solver, with fixed-leading-coefficient implementation",ZVODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000433,,,,,kisao:KISAO_0000536,,2020-10-29,JRK,,Odespy|http://identifiers.org/biosimulators/gillespy2|deSolve|SciPy,,"Complex-valued Variable-coefficient Ordinary Differential Equation solver, with fixed-leading-coefficient implementation|complex-valued variable-coefficient ordinary differential equation solver, with fixed-leading-coefficient implementation",ZVODE provides implicit Adams method (for non-stiff problems) and a method based on backward differentiation formulas (BDF) (for stiff problems).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000281,multistep method,multi-value method,A numerical method for differential equations which is based on several values of the solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000281,,2011-05-09,AZ,,,true,multi-value method,A numerical method for differential equations which is based on several values of the solution.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000503,simulated annealing,,"Simulated annealing is an optimization algorithm first proposed by Kirkpatrick et al. and was inspired by statistical mechanics and the way in which perfect crystals are formed. Perfect crystals are formed by first melting the substance of interest, and then cooling it very slowly. At large temperatures the particles vibrate with wide amplitude and this allows a search for global optimum. As the temperature decreases so do the vibrations until the system settles to the global optimum (the perfect crystal). The simulated annealing optimization algorithm uses a similar concept: the objective function is considered a measure of the energy of the system and this is maintained constant for a certain number of iterations (a temperature cycle). In each iteration, the parameters are changed to a nearby location in parameter space and the new objective function value calculated; if it decreased, then the new state is accepted, if it increased then the new state is accepted with a probability that follows a Boltzmann distribution (higher temperature means higher probability of accepting the new state). After a fixed number of iterations, the stopping criterion is checked; if it is not time to stop, then the system's temperature is reduced and the algorithm continues. -Simulated annealing is a stochastic algorithm that is guaranteed to converge if ran for an infinite number of iterations. It is one of the most robust global optimization algorithms, although it is also one of the slowest. (Be warned that simulated annealing can run for hours or even days!).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000503,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,,"Simulated annealing is an optimization algorithm first proposed by Kirkpatrick et al. and was inspired by statistical mechanics and the way in which perfect crystals are formed. Perfect crystals are formed by first melting the substance of interest, and then cooling it very slowly. At large temperatures the particles vibrate with wide amplitude and this allows a search for global optimum. As the temperature decreases so do the vibrations until the system settles to the global optimum (the perfect crystal). +Simulated annealing is a stochastic algorithm that is guaranteed to converge if ran for an infinite number of iterations. It is one of the most robust global optimization algorithms, although it is also one of the slowest. (Be warned that simulated annealing can run for hours or even days!).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000472,,,,,kisao:KISAO_0000503,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,,"Simulated annealing is an optimization algorithm first proposed by Kirkpatrick et al. and was inspired by statistical mechanics and the way in which perfect crystals are formed. Perfect crystals are formed by first melting the substance of interest, and then cooling it very slowly. At large temperatures the particles vibrate with wide amplitude and this allows a search for global optimum. As the temperature decreases so do the vibrations until the system settles to the global optimum (the perfect crystal). The simulated annealing optimization algorithm uses a similar concept: the objective function is considered a measure of the energy of the system and this is maintained constant for a certain number of iterations (a temperature cycle). In each iteration, the parameters are changed to a nearby location in parameter space and the new objective function value calculated; if it decreased, then the new state is accepted, if it increased then the new state is accepted with a probability that follows a Boltzmann distribution (higher temperature means higher probability of accepting the new state). After a fixed number of iterations, the stopping criterion is checked; if it is not time to stop, then the system's temperature is reduced and the algorithm continues. @@ -486,7 +538,8 @@ http://www.biomodels.net/kisao/KISAO#KISAO_0000408,Newton-type method,,"A method The solution of the system F(x)=0 can be interpreted as a steady state of a dynamic system x'(t)=F(x(t)). The Newton approach will only work if the fixed point [http://identifiers.org/biomodels.teddy/TEDDY_0000086] of the dinamic system is attractive [http://identifiers.org/biomodels.teddy/TEDDY_0000094].",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000631,,,,,kisao:KISAO_0000408,,2012-01-18,AZ,,,,,"A method which attacks the solution of a nonlinear problem F(x) = 0 by solving a sequence of liner problems of the same kind. The solution of the system F(x)=0 can be interpreted as a steady state of a dynamic system x'(t)=F(x(t)). The Newton approach will only work if the fixed point [http://identifiers.org/biomodels.teddy/TEDDY_0000086] of the dinamic system is attractive [http://identifiers.org/biomodels.teddy/TEDDY_0000094].",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000651,reduced costs,,The amount by which an objective function coefficient would have to improve before it would be possible for a corresponding variable to assume a positive value in the optimal solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000650,,,,,kisao:KISAO_0000651,,2021-06-04,JRK,,,,,The amount by which an objective function coefficient would have to improve before it would be possible for a corresponding variable to assume a positive value in the optimal solution.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000562,Pahle hybrid Gibson-Bruck Next Reaction method/LSODA method,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with LSODA. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000231,,,,,kisao:KISAO_0000562,,2020-10-29,JRK,,https://identifiers.org/biosimulators/copasi,,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with LSODA. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000830,maximum,,The maximum of a set of values. If the values contain NaN the maximum is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000830,,2021-10-09,MK,,,,,The maximum of a set of values. If the values contain NaN the maximum is NaN.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000562,Pahle hybrid Gibson-Bruck Next Reaction method/LSODA method,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with LSODA. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000231,,,,,kisao:KISAO_0000562,,2020-10-29,JRK,,http://identifiers.org/biosimulators/copasi,,,Combines a deterministic numerical integration of ODEs with a stochastic simulation algorithm. The whole biochemical network is partitioned into a deterministic and a stochastic subnet internally. The deterministic subnet contains all reactions in which only species with high particle numbers take part. All reactions with at least one low-numbered species are in the stochastic subnet. The partitioning of the biochemical network can change dynamically during the simulation. The reaction probabilities of the stochastic subnet are approximated as constant during one stochastic step. The deterministic subnet is integrated with LSODA. The stochastic subnet is simulated by the Gibson-Bruck Next Reaction Method.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000492,asynchronous updating policy,,An updating policy where all enabled transitions (events) occur independently. Thus a state has as many successors as the number of transitions enabled in this state.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000451,,,,,kisao:KISAO_0000492,,,,,,,,An updating policy where all enabled transitions (events) occur independently. Thus a state has as many successors as the number of transitions enabled in this state.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000634,scaled property,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000820,,,,,kisao:KISAO_0000634,,2021-06-04|JRK,,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000238,type of method,,"A characteristic describing the way the method finds a solution, specifically whether it solves an equation involving only the current state of the system (explicit) or both the current and the later one (implicit). ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000097,,,,,kisao:KISAO_0000238,,,AZ,,,true,,"A characteristic describing the way the method finds a solution, specifically whether it solves an equation involving only the current state of the system (explicit) or both the current and the later one (implicit). ",,,,,,, @@ -495,21 +548,24 @@ http://www.biomodels.net/kisao/KISAO#KISAO_0000397,preconditioning technique,,"P http://www.biomodels.net/kisao/KISAO#KISAO_0000331,exact R-leaping algorithm,ER-leap method|exact accelerated stochastic simulation algorithm|exact R-leap method,"We present a SSA which, similar to R-leap [http://identifiers.org/biomodels.kisao/KISAO_0000330], accelerates SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] by executing multiple reactions per algorithmic step, but which samples the reactant trajectories from the same probability distribution as the SSA. This 'exact R-leap' or 'ER-leap' algorithm is a modification of the R-leap algorithm which is both exact and capable of substantial speed-up over SSA.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000621,,,,,kisao:KISAO_0000331,,2011-06-03,AZ,,,,ER-leap method|exact accelerated stochastic simulation algorithm|exact R-leap method,"We present a SSA which, similar to R-leap [http://identifiers.org/biomodels.kisao/KISAO_0000330], accelerates SSA [http://identifiers.org/biomodels.kisao/KISAO_0000029] by executing multiple reactions per algorithmic step, but which samples the reactant trajectories from the same probability distribution as the SSA. This 'exact R-leap' or 'ER-leap' algorithm is a modification of the R-leap algorithm which is both exact and capable of substantial speed-up over SSA.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000364,Adams predictor-corrector method,,The combination of evaluating a single explicit integration method ('Adams-Bashforth method' [http://identifiers.org/biomodels.kisao/KISAO_0000279]) (the predictor step) in order to provide a good initial guess for the successive evaluation of an implicit method ('Adams-Moulton method' [http://identifiers.org/biomodels.kisao/KISAO_0000280]) (the corrector step) using iteration.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000289,,,,,kisao:KISAO_0000364,,2011-06-16,AZ,,GSL,,,The combination of evaluating a single explicit integration method ('Adams-Bashforth method' [http://identifiers.org/biomodels.kisao/KISAO_0000279]) (the predictor step) in order to provide a good initial guess for the successive evaluation of an implicit method ('Adams-Moulton method' [http://identifiers.org/biomodels.kisao/KISAO_0000280]) (the corrector step) using iteration.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000499,dynamic flux balance analysis,DFBA,Dynamic flux balance analysis (DFBA) enables the simulation of dynamic biological systems by assuming organisms reach steady state rapidly in response to changes in the extracellular environment. DFBA couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000622|http://www.biomodels.net/kisao/KISAO#KISAO_0000352,,,,,kisao:KISAO_0000499,,2017-09-12,AZ,,,true,DFBA,Dynamic flux balance analysis (DFBA) enables the simulation of dynamic biological systems by assuming organisms reach steady state rapidly in response to changes in the extracellular environment. DFBA couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000608,Hierarchical flux balance analysis,Hierarchical FBA|hFBA,"Method for constraint-based simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437,,,,,kisao:KISAO_0000608,,2021-01-25,JRK,,https://identifiers.org/biosimulators/ibiosim,,Hierarchical FBA|hFBA,"Method for constraint-based simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000608,hierarchical flux balance analysis,Hierarchical FBA|hFBA|hierarchical FBA,"Method for constraint-based simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000437,,,,,kisao:KISAO_0000608,,2021-01-25,JRK,,http://identifiers.org/biosimulators/ibiosim,,Hierarchical FBA|hFBA|hierarchical FBA,"Method for constraint-based simulation of hierarchically organized models, such as a model of a cellular population where each cell in the population is represented by the same species and reactions.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000242,error control parameter,,Parameter controlling method accuracy.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000242,,,AZ,,,true,,Parameter controlling method accuracy.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000377,one-step method,,A numerical method for differential equations which uses one starting value at each step.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000377,,2011-06-30,AZ,,,true,,A numerical method for differential equations which uses one starting value at each step.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000377,one-step method,,A numerical method for differential equations which uses one starting value at each step.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000694,,,,,kisao:KISAO_0000377,,2011-06-30,AZ,,,true,,A numerical method for differential equations which uses one starting value at each step.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000645,objective value,,Value of an objective function such as of a constraint-based model.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000831,,,,,kisao:KISAO_0000645,,2021-06-04,JRK,,,,,Value of an objective function such as of a constraint-based model.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000612,implicit 4th order Runge-Kutta method at Gaussian points,RK4IMP,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000612,,2021-01-25,JRK,,https://identifiers.org/biosimulators/ibiosim,,RK4IMP,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000612,implicit 4th order Runge-Kutta method at Gaussian points,RK4IMP,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000612,,2021-01-25,JRK,,http://identifiers.org/biosimulators/ibiosim,,RK4IMP,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000470,optimization algorithm,optimization method,An optimization algorithm tries to find the minumum or maximum of an arbitrary function. It takes a function of one or several variables and determines the values for the variables so that the function value is optimal.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000470,,2015-04-23,AZ,,,true,optimization method,An optimization algorithm tries to find the minumum or maximum of an arbitrary function. It takes a function of one or several variables and determines the values for the variables so that the function value is optimal.,,,,,,, -http://www.biomodels.net/kisao/KISAO#_KISAO_0000811,Stoichiometry matrix,Full stochiometry matrix,The (full) stoichiometry matrix. The dimensions are species by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#_KISAO_0000800,,,,,kisao:_KISAO_0000811,,06-03-2021,LPS,,,,Full stochiometry matrix,The (full) stoichiometry matrix. The dimensions are species by reactions.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000003,weighted stochastic simulation algorithm,weighted SSA,"The weighted Stochastic Simulation Algorithm manipulates the probabilities measure of biochemical systems by sampling, in order to increase the fraction of simulation runs exhibiting rare events.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000003,,24JAN2009,NLN,,,,weighted SSA,"The weighted Stochastic Simulation Algorithm manipulates the probabilities measure of biochemical systems by sampling, in order to increase the fraction of simulation runs exhibiting rare events.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000003,weighted stochastic simulation algorithm,weighted SSA,"The weighted stochastic simulation algorithm manipulates the probabilities measure of biochemical systems by sampling, in order to increase the fraction of simulation runs exhibiting rare events.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000003,,24JAN2009,NLN,,,,weighted SSA,"The weighted stochastic simulation algorithm manipulates the probabilities measure of biochemical systems by sampling, in order to increase the fraction of simulation runs exhibiting rare events.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000501,DOA-DFBA,dynamic optimization approach dynamic flux balance analysis|DOA,Dynamic Flux Balance Analysis (DFBA) [http://identifiers.org/biomodels.kisao/KISAO_0000499] couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches. The dynamic optimization approach (DOA) discretizes the time horizon and optimizes simultaneously over the entire time period of interest by solving a nonlinear programming problem (NLP).,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000499,,,,,kisao:KISAO_0000501,,2017-09-12,AZ,,DFBAlab,,dynamic optimization approach dynamic flux balance analysis|DOA,Dynamic Flux Balance Analysis (DFBA) [http://identifiers.org/biomodels.kisao/KISAO_0000499] couples flux balance analysis (FBA) [http://identifiers.org/biomodels.kisao/KISAO_0000437] model approaches with dynamic model approaches. The dynamic optimization approach (DOA) discretizes the time horizon and optimizes simultaneously over the entire time period of interest by solving a nonlinear programming problem (NLP).,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000381,midpoint method,,"The midpoint method is an explicit method for approximating the solution of the initial value problem y' = f(x,y); y(x0) = y0 at x for a given step size h. For the midpoint method the derivative of y(x) is approximated by the symmetric difference y'(x) = ( y(x+h) - y(x-h) ) / 2h + O(h2).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000381,,2011-07-01,AZ,,,,,"The midpoint method is an explicit method for approximating the solution of the initial value problem y' = f(x,y); y(x0) = y0 at x for a given step size h. For the midpoint method the derivative of y(x) is approximated by the symmetric difference y'(x) = ( y(x+h) - y(x-h) ) / 2h + O(h2).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000804,elasticity matrix (unscaled),,The unscaled elasticity matrix. The dimensions are reactions by species.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000804,,06-03-2021,LPS,,,,,The unscaled elasticity matrix. The dimensions are reactions by species.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000057,Brownian diffusion Smoluchowski method,,"In the Brownian diffusion Smoluchowski method, ''each molecule is treated as a point-like particle that diffuses freely in three-dimensional space. When a pair of reactive molecules collide, such as an enzyme and its substrate, a reaction occurs and the simulated reactants are replaced by products. [..] Analytic solutions are presented for some simulation parameters while others are calculated using look-up tables''. Supported chemical processes include molecular diffusion, treatment of surfaces, zeroth-order-, unimolecular-, and bimolecular reactions.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000056,,,,,kisao:KISAO_0000057,,,,,Smoldyn,,,"In the Brownian diffusion Smoluchowski method, ''each molecule is treated as a point-like particle that diffuses freely in three-dimensional space. When a pair of reactive molecules collide, such as an enzyme and its substrate, a reaction occurs and the simulated reactants are replaced by products. [..] Analytic solutions are presented for some simulation parameters while others are calculated using look-up tables''. Supported chemical processes include molecular diffusion, treatment of surfaces, zeroth-order-, unimolecular-, and bimolecular reactions.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000304,Radau method,implicit Runge-Kutta method based on Radau quadrature,Implicit Runge-Kutta methods based on Radau quadrature.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000304,,2011-05-11,AZ,,https://identifiers.org/biosimulators/jsim,,implicit Runge-Kutta method based on Radau quadrature,Implicit Runge-Kutta methods based on Radau quadrature.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000684,number of steps per output,,Number of simulation steps between each simulation output.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000244,,,,,kisao:KISAO_0000684,,2021-09-22,JRK,,http://identifiers.org/biosimulators/bionetgen,,,Number of simulation steps between each simulation output.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000304,Radau method,implicit Runge-Kutta method based on Radau quadrature,Implicit Runge-Kutta methods based on Radau quadrature.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000064,,,,,kisao:KISAO_0000304,,2011-05-11,AZ,,http://identifiers.org/biosimulators/jsim,,implicit Runge-Kutta method based on Radau quadrature,Implicit Runge-Kutta methods based on Radau quadrature.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000534,reactions,,FVA algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000526] parameter: reactions to compute the variablity of.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000534,,,,,,,,FVA algorithm [http://www.biomodels.net/kisao/KISAO#KISAO_0000526] parameter: reactions to compute the variablity of.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000695,parameters for,,"The children parameters of this term are applied when the parent general term is implented as the more-specific value of this term. For example: a 'parameters for' term might be used as a child of an 'ODE Solver' ([http://identifiers.org/biomodels.kisao/KISAO_0000694]), have a value of 'KISAO_0000019' (CVODE)', and have a child term 'use stiff method' of 'true' ([http://identifiers.org/biomodels.kisao/KISAO_0000671])",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000695,,2023/05/19,lps,,,,,"The children parameters of this term are applied when the parent general term is implented as the more-specific value of this term. For example: a 'parameters for' term might be used as a child of an 'ODE Solver' ([http://identifiers.org/biomodels.kisao/KISAO_0000694]), have a value of 'KISAO_0000019' (CVODE)', and have a child term 'use stiff method' of 'true' ([http://identifiers.org/biomodels.kisao/KISAO_0000671])",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000349,method of finite spheres,MFS,Method of finite spheres is truly meshless in the sense that the nodes are placed and the numerical integration is performed without a mesh. Some of the novel features of the method of finite spheres are the numerical integration scheme and the way in which the Dirichlet boundary conditions are incorporated.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000349,,2011-06-09,AZ,,,,MFS,Method of finite spheres is truly meshless in the sense that the nodes are placed and the numerical integration is performed without a mesh. Some of the novel features of the method of finite spheres are the numerical integration scheme and the way in which the Dirichlet boundary conditions are incorporated.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000094,Livermore solver,,Method to solve ordinary differential equations developed at the Lawrence Livermore National Laboratory.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000094,,2008-07-08,NLN,,,true,,Method to solve ordinary differential equations developed at the Lawrence Livermore National Laboratory.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000662,Klarner ASP logical model trap space identification method,Klarner Answer Set Programming logical model trap space identification method,Optimization-based method rooted in answer set programming (ASP) for computing the trap spaces of a regulatory graph.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000661,,,,,kisao:KISAO_0000662,,2021-07-08,JRK,,,,Klarner Answer Set Programming logical model trap space identification method,Optimization-based method rooted in answer set programming (ASP) for computing the trap spaces of a regulatory graph.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000094,Livermore solver,,Method to solve ordinary differential equations developed at the Lawrence Livermore National Laboratory.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000694,,,,,kisao:KISAO_0000094,,2008-07-08,NLN,,,true,,Method to solve ordinary differential equations developed at the Lawrence Livermore National Laboratory.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000316,enhanced Greens function reaction dynamics,enhanced Greens function reaction dynamics|eGFRD,GFRD [http://identifiers.org/biomodels.kisao/KISAO_0000058] decomposes the multi­body reaction diffusion problem to a set of single and two body problems. Analytical solutions for two body reaction diffusion are available via Smoluchowski equation. eGFRD allows to solve each sub­problem asynchronously by introducing the concept of first passage processes.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000056,,,,,kisao:KISAO_0000316,,2011-05-23,AZ,,,,enhanced Greens function reaction dynamics|eGFRD,GFRD [http://identifiers.org/biomodels.kisao/KISAO_0000058] decomposes the multi­body reaction diffusion problem to a set of single and two body problems. Analytical solutions for two body reaction diffusion are available via Smoluchowski equation. eGFRD allows to solve each sub­problem asynchronously by introducing the concept of first passage processes.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000412,inexact Newton method,truncated Newton method|iterative Newton method,"For extremely large scale nonlinear problems the arising linear systems for the Newton corrections can no longer be solved directly ('exactly'), but must be solved iterativly ('inexactly) - which gives the name inexact Newton methods. The whole scheme then consists of an inner iteration (at Newton step k): F'(x[k])deltaxi[k]=-F(x[k])+ri[k], k=0,1,... @@ -519,30 +575,38 @@ F'(x[k])deltaxi[k]=-F(x[k])+ri[k], k=0,1,... xi[k+1]=x[k]+deltaxi[k], i=0,1,..,imax[k] in terms of residuals ri[k] and an outer iteration where, given x[0], the iterates are defined as x[k+1]=xi[k+1] for i=imax[k], k=0,1,...",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000547,linear programming,LP,Method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000546,,,,,kisao:KISAO_0000547,,2020-10-29,JRK,,CPLEX|CVXOPT|SoPlex|GLPK|Mosek|MATLAB|OptLang|XPRESS|Gurobi|SciPy|ConvOpt,,LP,Method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000619,emc-sim,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is a constant equal to 1 time unit. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000619,,2021-01-30,JRK,,https://identifiers.org/biosimulators/ibiosim,,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is a constant equal to 1 time unit. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000477,linear solver,,the linear solver used by the solver during a Newton iteration,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000477,,2015-09-10,AZ,,https://identifiers.org/biosimulators/opencor,,,the linear solver used by the solver during a Newton iteration,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000848,cumulative sum ignoring NaN,,"The cumulative sum of a set of values, ignoring Nan entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000848,,2021-10-09,MK,,,,,"The cumulative sum of a set of values, ignoring Nan entries.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000619,emc-sim,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is a constant equal to 1 time unit. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000335,,,,,kisao:KISAO_0000619,,2021-01-30,JRK,,http://identifiers.org/biosimulators/ibiosim,,,"A variant of the stochastic simulation algorithm (SSA) in which the time to the next reaction is a constant equal to 1 time unit. In this method, the next reaction time is deterministic rather than stochastic as in SSA.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000815,flux control coefficient matrix (scaled),,The scaled flux control coefficient matrix. The dimensions are reactions by reactions.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000800,,,,,kisao:KISAO_0000815,,06-03-2021,LPS,,,,,The scaled flux control coefficient matrix. The dimensions are reactions by reactions.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000477,linear solver,,the linear solver used by the solver during a Newton iteration,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000477,,2015-09-10,AZ,,http://identifiers.org/biosimulators/opencor,,,the linear solver used by the solver during a Newton iteration,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000841,mean,,The mean of a set of values. If the values contain NaN the mean is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000841,,2021-10-09,MK,,,,,The mean of a set of values. If the values contain NaN the mean is NaN.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000678,maximum number of steps for approximation,,Maximum number of steps to take in approximating an analysis.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000415,,,,,kisao:KISAO_0000678,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Maximum number of steps to take in approximating an analysis.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000273,hard-particle molecular dynamics,,A collision-driven molecular dynamics algorithm for a system of non-spherical particles.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000273,,2011-05-05,AZ,,,,,A collision-driven molecular dynamics algorithm for a system of non-spherical particles.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000240,implicit method type,,"Implicit methods find a solution by solving an equation involving both the current state of the system and the later one. Mathematically, if Y(t) is the current system state and Y((t+delta t) is the state at the later time (delta t is a small time step), then, for an implicit method one solves an equation G(Y(t), Y(t+delta t))=0, to find Y(t+delta t).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000238,,,,,kisao:KISAO_0000240,,,AZ,,,,,"Implicit methods find a solution by solving an equation involving both the current state of the system and the later one. Mathematically, if Y(t) is the current system state and Y((t+delta t) is the state at the later time (delta t is a small time step), then, for an implicit method one solves an equation G(Y(t), Y(t+delta t))=0, to find Y(t+delta t).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000375,delay differential equation problem,DDE problem,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000405,,,,,kisao:KISAO_0000375,,,AZ,,,,DDE problem,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000342,level set method,LSM|level-set method,"An algorithm for moving surfaces under their curvature. This algorithm rely on numerically solving Hamilton-Jacobi equations with viscous terms, using approximation techniques from hyperbolic conservation laws.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000369,,,,,kisao:KISAO_0000342,,2011-06-07,AZ,,,,LSM|level-set method,"An algorithm for moving surfaces under their curvature. This algorithm rely on numerically solving Hamilton-Jacobi equations with viscous terms, using approximation techniques from hyperbolic conservation laws.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000610,Composite-rejection stochastic simulation algorithm,SSA-CR,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000610,,2021-01-25,JRK,,https://identifiers.org/biosimulators/ibiosim,,SSA-CR,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000610,composite-rejection stochastic simulation algorithm,SSA-CR,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000333,,,,,kisao:KISAO_0000610,,2021-01-25,JRK,,http://identifiers.org/biosimulators/ibiosim,,SSA-CR,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000088,LSODA,Livermore solver for ordinary differential equations with automatic method switching,"LSODA solves systems dy/dt = f with a dense or banded Jacobian when the problem is stiff, but it automatically selects between non-stiff (Adams [http://identifiers.org/biomodels.kisao/KISAO_0000289]) and stiff (BDF [http://identifiers.org/biomodels.kisao/KISAO_0000288]) methods. It uses the non-stiff method initially, and dynamically monitors data in order to decide which method to use.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000094,,,,,kisao:KISAO_0000088,,2007-11-30,dk,,,,Livermore solver for ordinary differential equations with automatic method switching,"LSODA solves systems dy/dt = f with a dense or banded Jacobian when the problem is stiff, but it automatically selects between non-stiff (Adams [http://identifiers.org/biomodels.kisao/KISAO_0000289]) and stiff (BDF [http://identifiers.org/biomodels.kisao/KISAO_0000288]) methods. It uses the non-stiff method initially, and dynamically monitors data in order to decide which method to use.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000508,evolutionary programming,EP,"Evolutionary programming (EP) is a computational technique that mimics evolution and is based on reproduction and selection. An EP algorithm is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the EP, each individual reproduces asexually, i.e. divides into two individuals. One of these contains exactly the same ""genome"" as the parent while the other suffers some mutations (the parameter values of each gene change slightly). At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000520,,,,,kisao:KISAO_0000508,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,EP,"Evolutionary programming (EP) is a computational technique that mimics evolution and is based on reproduction and selection. An EP algorithm is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the EP, each individual reproduces asexually, i.e. divides into two individuals. One of these contains exactly the same ""genome"" as the parent while the other suffers some mutations (the parameter values of each gene change slightly). At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000682,allow approximation,,Whether to find an approximate solution if an exact solution could not be found.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000682,,2021-08-23,JRK,,http://identifiers.org/biosimulators/tellurium,,,Whether to find an approximate solution if an exact solution could not be found.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000508,evolutionary programming,EP,"Evolutionary programming (EP) is a computational technique that mimics evolution and is based on reproduction and selection. An EP algorithm is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the EP, each individual reproduces asexually, i.e. divides into two individuals. One of these contains exactly the same ""genome"" as the parent while the other suffers some mutations (the parameter values of each gene change slightly). At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000520,,,,,kisao:KISAO_0000508,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,EP,"Evolutionary programming (EP) is a computational technique that mimics evolution and is based on reproduction and selection. An EP algorithm is composed of individuals that reproduce and compete, each one is a potential solution to the (optimization) problem and is represented by a ""genome"" where each gene corresponds to one adjustable parameter. At each generation of the EP, each individual reproduces asexually, i.e. divides into two individuals. One of these contains exactly the same ""genome"" as the parent while the other suffers some mutations (the parameter values of each gene change slightly). At the end of the generation, the algorithm has double the number of individuals. Then each of the individuals is confronted with a number of others to count how many does it outperform (the number of wins is the number of these competitors that represent worse solutions than itself). All the individuals are ranked by their number of wins, and the population is again reduced to the original number of individuals by eliminating those which have worse fitness (solutions).",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000286,Euler-Maruyama method,stochastic Euler scheme,"The Euler-Maruyama method is a method for the approximate numerical solution of a stochastic differential equation, which truncates the Ito and Stratonovich Taylor series of the exact solution after the first order stochastic terms. This converges to the Ito solution with strong global order accuracy 1/2 or weak global order accuracy 1. It is a simple generalization of the Euler method [http://identifiers.org/biomodels.kisao/KISAO_0000261] for ordinary differential equations to stochastic differential equations.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000377,,,,,kisao:KISAO_0000286,,2011-05-09,AZ,,,,stochastic Euler scheme,"The Euler-Maruyama method is a method for the approximate numerical solution of a stochastic differential equation, which truncates the Ito and Stratonovich Taylor series of the exact solution after the first order stochastic terms. This converges to the Ito solution with strong global order accuracy 1/2 or weak global order accuracy 1. It is a simple generalization of the Euler method [http://identifiers.org/biomodels.kisao/KISAO_0000261] for ordinary differential equations to stochastic differential equations.",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000656,use adaptive time steps,,Whether an algorithm should use adaptive or fixed time steps.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000656,,2021-06-05,JRK,,https://identifiers.org/biosimulators/tellurium,,,Whether an algorithm should use adaptive or fixed time steps.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000481,interpolate solution,,whether the solver returns an interpolated solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000481,,2015-09-10,AZ,,https://identifiers.org/biosimulators/opencor,,,whether the solver returns an interpolated solution.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000689,mathematical system,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000688,,,,,kisao:KISAO_0000689,,2022-03-29,EN|JRK|WL,,,,,,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000656,use adaptive time steps,,Whether an algorithm should use adaptive or fixed time steps.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000656,,2021-06-05,JRK,,http://identifiers.org/biosimulators/tellurium,,,Whether an algorithm should use adaptive or fixed time steps.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000481,interpolate solution,,whether the solver returns an interpolated solution.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000481,,2015-09-10,AZ,,http://identifiers.org/biosimulators/opencor,,,whether the solver returns an interpolated solution.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000623,flux balance problem,,,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000370,,,,,kisao:KISAO_0000623,,2021-04-28,JRK,,,,,,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000410,simlified Newton method,,"A 'Newton-type method' [http://identifiers.org/biomodels.kisao/KISAO_0000408] which is characterized by keeping the initial derivative throughout the whole iteration: F'(x[0])deltax[k]=-F(x[k]), x[k+1]=x[k]+deltax[k], k=0,1,...",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000408,,,,,kisao:KISAO_0000410,,2012-01-18,AZ,,,,,"A 'Newton-type method' [http://identifiers.org/biomodels.kisao/KISAO_0000408] which is characterized by keeping the initial derivative throughout the whole iteration: F'(x[0])deltax[k]=-F(x[k]), x[k+1]=x[k]+deltax[k], k=0,1,...",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000578,nested algorithm,subalgorithm|nested method,A nested algorithm of an algorithm,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243,,,,,kisao:KISAO_0000578,,2020-10-29,JRK,,,,subalgorithm|nested method,A nested algorithm of an algorithm,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000290,Merson method,KM|Merson's method|Kutta-Merson method|Runge-Kutta-Merson method,A five-stage Runge-Kutta method with fourth-order accuracy.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000290,,2011-05-10,AZ,,https://identifiers.org/biosimulators/jsim,,KM|Merson's method|Kutta-Merson method|Runge-Kutta-Merson method,A five-stage Runge-Kutta method with fourth-order accuracy.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000512,praxis,,"Praxis is a direct search method that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of that function. Praxis was developed by Brent after the method proposed by Powell. The inspiration for Praxis was the well-known method of minimising each adjustable parameter (direction) at a time - the principal axes method. In Praxis directions are chosen that do not coincide with the principal axes, in fact if the objective function is quadratic then these will be conjugate directions, assuring a fast convergence rate.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000512,,2019-01-18,AZ,,https://identifiers.org/biosimulators/copasi,,,"Praxis is a direct search method that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of that function. Praxis was developed by Brent after the method proposed by Powell. The inspiration for Praxis was the well-known method of minimising each adjustable parameter (direction) at a time - the principal axes method. In Praxis directions are chosen that do not coincide with the principal axes, in fact if the objective function is quadratic then these will be conjugate directions, assuring a fast convergence rate.",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000290,Merson method,KM|Merson's method|Kutta-Merson method|Runge-Kutta-Merson method,A five-stage Runge-Kutta method with fourth-order accuracy.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000302,,,,,kisao:KISAO_0000290,,2011-05-10,AZ,,http://identifiers.org/biosimulators/jsim,,KM|Merson's method|Kutta-Merson method|Runge-Kutta-Merson method,A five-stage Runge-Kutta method with fourth-order accuracy.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000512,praxis,,"Praxis is a direct search method that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of that function. Praxis was developed by Brent after the method proposed by Powell. The inspiration for Praxis was the well-known method of minimising each adjustable parameter (direction) at a time - the principal axes method. In Praxis directions are chosen that do not coincide with the principal axes, in fact if the objective function is quadratic then these will be conjugate directions, assuring a fast convergence rate.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000471,,,,,kisao:KISAO_0000512,,2019-01-18,AZ,,http://identifiers.org/biosimulators/copasi,,,"Praxis is a direct search method that searches for the minimum of a nonlinear function without requiring (or attempting to calculate) derivatives of that function. Praxis was developed by Brent after the method proposed by Powell. The inspiration for Praxis was the well-known method of minimising each adjustable parameter (direction) at a time - the principal axes method. In Praxis directions are chosen that do not coincide with the principal axes, in fact if the objective function is quadratic then these will be conjugate directions, assuring a fast convergence rate.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000545,include sensitivity variables in error control mechanism,errconS,Specifies whether sensitivity variables are included or not in the error control mechanism.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000243|http://www.biomodels.net/kisao/KISAO#KISAO_0000242,,,,,kisao:KISAO_0000545,,2020-10-29,JRK,,SUNDIALS,,errconS,Specifies whether sensitivity variables are included or not in the error control mechanism.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000417,hierarchical cluster-based partial least squares regression method,"Multivariate regression method based on separating the observations into clusters and generating Partial Least Squares Regression (PLSR) [http://identifiers.org/biomodels.kisao/KISAO_0000416] models within each cluster. This local regression analysis is suitable for very non-linear systems. PLSR is a regression method based on estimated latent variables, related to Principal Component Analysis (PCA) and Principal Component Regression (PCR). Hierarchical cluster-based partial least squares regression method uses fuzzy C-means clustering, PLSR and Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) or Naive Bayes for classification of new observations to be predicted.|HC-PLSR","Requested by Kristin Tøndel on Thursday, October 13, 2011 11:13:17 AM.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000423,,,,,kisao:KISAO_0000417,,2012-01-18,AZ,,,,"Multivariate regression method based on separating the observations into clusters and generating Partial Least Squares Regression (PLSR) [http://identifiers.org/biomodels.kisao/KISAO_0000416] models within each cluster. This local regression analysis is suitable for very non-linear systems. PLSR is a regression method based on estimated latent variables, related to Principal Component Analysis (PCA) and Principal Component Regression (PCR). Hierarchical cluster-based partial least squares regression method uses fuzzy C-means clustering, PLSR and Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) or Naive Bayes for classification of new observations to be predicted.|HC-PLSR",,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000314,S-System power-law canonical differential equations solver,ESSYNS GMA,"Ordinary differential equations can be recast into a nonlinear canonical form called an S-system. Evidence for the generality of this class comes from extensive empirical examples that have been recast and from the discovery that sets of differential equations and functions, recognized as among the most general, are special cases of S-systems. Identification of this nonlinear canonical form suggests a radically different approach to numerical solution of ordinary differential equations. By capitalizing on the regular structure of S-systems, efficient formulas for a variable-order, variable-step Taylor-series method are developed. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000314,,2011-05-20,AZ,,https://identifiers.org/biosimulators/ecell4,,ESSYNS GMA,"Ordinary differential equations can be recast into a nonlinear canonical form called an S-system. Evidence for the generality of this class comes from extensive empirical examples that have been recast and from the discovery that sets of differential equations and functions, recognized as among the most general, are special cases of S-systems. Identification of this nonlinear canonical form suggests a radically different approach to numerical solution of ordinary differential equations. By capitalizing on the regular structure of S-systems, efficient formulas for a variable-order, variable-step Taylor-series method are developed. ",,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000582,Spatiocyte method,,Lattice-based stochastic particle simulation method for biochemical reaction and diffusion processes.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000056,,,,,kisao:KISAO_0000582,,2020-10-29,JRK,,https://identifiers.org/biosimulators/ecell4,,,Lattice-based stochastic particle simulation method for biochemical reaction and diffusion processes.,,,,,,, -http://www.biomodels.net/kisao/KISAO#KISAO_0000826,standard deviation,,The standard deviation of a set of values.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000826,,06-03-2021,LPS,,,,,The standard deviation of a set of values.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000660,logical model stable state search method,,Method for determining the stable states of a regulatory graph.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000658,,,,,kisao:KISAO_0000660,,2021-07-08,JRK,,,true,,Method for determining the stable states of a regulatory graph.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000314,S-System power-law canonical differential equations solver,ESSYNS GMA,"Ordinary differential equations can be recast into a nonlinear canonical form called an S-system. Evidence for the generality of this class comes from extensive empirical examples that have been recast and from the discovery that sets of differential equations and functions, recognized as among the most general, are special cases of S-systems. Identification of this nonlinear canonical form suggests a radically different approach to numerical solution of ordinary differential equations. By capitalizing on the regular structure of S-systems, efficient formulas for a variable-order, variable-step Taylor-series method are developed. ",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000000,,,,,kisao:KISAO_0000314,,2011-05-20,AZ,,http://identifiers.org/biosimulators/ecell4,,ESSYNS GMA,"Ordinary differential equations can be recast into a nonlinear canonical form called an S-system. Evidence for the generality of this class comes from extensive empirical examples that have been recast and from the discovery that sets of differential equations and functions, recognized as among the most general, are special cases of S-systems. Identification of this nonlinear canonical form suggests a radically different approach to numerical solution of ordinary differential equations. By capitalizing on the regular structure of S-systems, efficient formulas for a variable-order, variable-step Taylor-series method are developed. ",,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000859,variance,,The variance of a set of values. If the values contain NaN the variance is NaN.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000859,,2021-10-09,MK,,,,,The variance of a set of values. If the values contain NaN the variance is NaN.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000582,Spatiocyte method,,Lattice-based stochastic particle simulation method for biochemical reaction and diffusion processes.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000056,,,,,kisao:KISAO_0000582,,2020-10-29,JRK,,http://identifiers.org/biosimulators/ecell4,,,Lattice-based stochastic particle simulation method for biochemical reaction and diffusion processes.,,,,,,, +http://www.biomodels.net/kisao/KISAO#KISAO_0000826,standard deviation ignoring NaN,,"The standard deviation of a set of values, ignoring NaN entries.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000824,,,,,kisao:KISAO_0000826,,2021-06-03,"LPS, MK",,,,,"The standard deviation of a set of values, ignoring NaN entries.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000488,seed,random seed,Random seed of a stochastic algorithm. Setting it allows one to reproduce their results while running the same algorithm on the same computer.,false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000201,,,,,kisao:KISAO_0000488,,,,,,,random seed,Random seed of a stochastic algorithm. Setting it allows one to reproduce their results while running the same algorithm on the same computer.,,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000258,neighbour distance,,"A parameter of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057]. When a surface-bound molecule diffuses off of one surface panel, it can sometimes diffuse onto the neighbouring surface tile. It does so only if the neighbouring panel is declared to be a neighbour and also the neighbour is within a distance that is set with the neighbour distance statement.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000258,,,,,,,,"A parameter of 'Brownian diffusion Smoluchowski method' [http://identifiers.org/biomodels.kisao/KISAO_0000057]. When a surface-bound molecule diffuses off of one surface panel, it can sometimes diffuse onto the neighbouring surface tile. It does so only if the neighbouring panel is declared to be a neighbour and also the neighbour is within a distance that is set with the neighbour distance statement.",,,,,,, http://www.biomodels.net/kisao/KISAO#KISAO_0000327,maximum discrete number,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which controls the maximum number of molecules of some reactant species in order for the reaction to be considered discrete.",false,,,http://www.biomodels.net/kisao/KISAO#KISAO_0000252,,,,,kisao:KISAO_0000327,,2011-06-02,AZ,,,,,"Parameter of 'equation-free probabilistic steady-state approximation' method [http://identifiers.org/biomodels.kisao/KISAO_0000323], which controls the maximum number of molecules of some reactant species in order for the reaction to be considered discrete.",,,,,,, diff --git a/src/kisao/transform_kisao.py b/src/kisao/transform_kisao.py index 292d9c40..0e69e81a 100644 --- a/src/kisao/transform_kisao.py +++ b/src/kisao/transform_kisao.py @@ -5,6 +5,9 @@ @author: Lucian """ +# The KISAO.csv file was taken from https://bioportal.bioontology.org/ontologies/KISAO and will need to be updated periodically. When you do, run this python script to recreate the kisaomap.cpp file. + + import csv kcpp = open("../sedml/kisaomap.cpp", "w") @@ -54,6 +57,7 @@ std::map g_kisaomap = { """) +allterms = [] with open('KISAO.csv', newline='') as csvfile: reader = csv.reader(csvfile) for row in reader: @@ -61,8 +65,12 @@ continue k_id = int(row[0].split("_")[-1]) k_name = row[1] - kcpp.write(' {' + str(k_id) + ', "' + k_name + '"},\n') - print(k_id, k_name) + allterms.append((k_id, k_name)) + +allterms.sort() +for (k_id, k_name) in allterms: + kcpp.write(' {' + str(k_id) + ', "' + k_name + '"},\n') + print(k_id, k_name) kcpp.write("};\n") kcpp.close() diff --git a/src/sedml/kisaomap.cpp b/src/sedml/kisaomap.cpp index c2c43cc0..da6aba56 100644 --- a/src/sedml/kisaomap.cpp +++ b/src/sedml/kisaomap.cpp @@ -41,473 +41,526 @@ #include #include -#include - -LIBSEDML_CPP_NAMESPACE_BEGIN - std::map g_kisaomap = { - {654, "amount rate"}, - {386, "scaled preconditioned generalized minimal residual method"}, - {810, "Reduced eigenvalue matrix"}, - {621, "stochastic simulation leaping method"}, - {808, "Reduced stoichiometry matrix"}, - {404, "symmetricity of matrix"}, - {539, "minimum factor to change step size by"}, - {506, "genetic algorithm"}, - {219, "maximum Adams order"}, - {99, "type of system behaviour"}, - {543, "stability limit detection flag"}, - {510, "truncated Newton"}, - {358, "biconjugate gradient method"}, - {325, "minimum fast/discrete reaction occurrences number"}, - {421, "type of validation"}, - {824, "aggregation function"}, - {556, "relative quadrature tolerance"}, - {589, "ACB flux sampling method"}, - {523, "cooling factor"}, - {453, "ordered updating policy"}, - {428, "matrix for clusterization"}, - {497, "KLU"}, - {338, "h-version of the finite element method"}, - {639, "flux"}, - {108, "progression with fixed time step"}, - {606, "Hierarchical Stochastic Simulation Algorithm"}, - {560, "LSODA/LSODAR hybrid method"}, - {837, "particle number"}, - {39, "tau-leaping method"}, - {236, "exact solution"}, - {592, "dynamic rFBA"}, - {432, "IDA-like method"}, - {203, "particle number lower limit"}, - {517, "number of generations"}, - {319, "Monte Carlo method"}, - {632, "functional iteration root-finding method"}, - {64, "Runge-Kutta based method"}, - {97, "modelling and simulation algorithm characteristic"}, - {415, "maximum number of steps"}, - {448, "logical model simulation method"}, - {521, "simulated annealing parameter"}, - {554, "parsimonius flux balance analysis (minimum number of active fluxes)"}, - {528, "parsimonious enzyme usage flux balance analysis (minimum sum of absolute fluxes)"}, - {587, "IMEX"}, - {599, "Hybrid Gibson - Euler-Maruyama Method"}, - {201, "modelling and simulation algorithm parameter"}, - {234, "LSODKR"}, - {369, "partial differential equation discretization method"}, - {106, "continuous variable"}, - {604, "MSR Tolerance"}, - {336, "D-leaping method"}, - {835, "Concentration control coefficient matrix (scaled)"}, - {567, "force physical correctness"}, - {340, "h-p version of the finite element method"}, + {0, "modelling and simulation algorithm"}, + {3, "weighted stochastic simulation algorithm"}, + {15, "Gillespie first reaction algorithm"}, {17, "multi-state agent-based simulation method"}, - {373, "differential-algebraic equation problem"}, - {617, "IDA-CVODE hybrid method"}, - {475, "integration method"}, - {643, "upper bound"}, - {571, "absolute tolerance adjustment factor"}, - {75, "Gillespie multi-particle method"}, - {829, "minimum"}, + {19, "CVODE"}, + {20, "PVODE"}, {21, "StochSim nearest-neighbour algorithm"}, - {424, "mean-centring of variables"}, - {469, "maximal timestep"}, - {301, "Heun method"}, - {430, "variables preprocessing parameter"}, - {565, "absolute tolerance for root finding"}, - {532, "loopless"}, + {22, "Elf and Ehrenberg method"}, + {27, "Gibson-Bruck next reaction algorithm"}, + {28, "slow-scale stochastic simulation algorithm"}, + {29, "Gillespie direct algorithm"}, + {30, "Euler forward method"}, {31, "Euler backward method"}, - {302, "embedded Runge-Kutta method"}, - {437, "flux balance analysis"}, - {597, "tolerance"}, - {104, "stochastic system behaviour"}, - {334, "multiparticle lattice gas automata"}, - {278, "Metropolis Monte Carlo algorithm"}, - {15, "Gillespie first reaction algorithm"}, - {615, "fully-implicit regular grid finite volume method with a variable time step"}, - {347, "meshless geometry handling"}, - {648, "step"}, - {473, "Bayesian inference algorithm"}, + {32, "explicit fourth-order Runge-Kutta method"}, + {33, "Rosenbrock method"}, + {38, "sorting stochastic simulation algorithm"}, + {39, "tau-leaping method"}, + {40, "Poisson tau-leaping method"}, + {45, "implicit tau-leaping method"}, + {46, "trapezoidal tau-leaping method"}, + {48, "adaptive explicit-implicit tau-leaping method"}, + {51, "Bortz-Kalos-Lebowitz algorithm"}, + {56, "Smoluchowski equation based method"}, + {57, "Brownian diffusion Smoluchowski method"}, + {58, "Greens function reaction dynamics"}, + {64, "Runge-Kutta based method"}, + {68, "deterministic cellular automata update algorithm"}, + {71, "LSODE"}, + {74, "binomial tau-leaping method"}, + {75, "Gillespie multi-particle method"}, + {76, "Stundzia and Lumsden method"}, {81, "estimated midpoint tau-leaping method"}, + {82, "k-alpha leaping method"}, + {84, "nonnegative Poisson tau-leaping method"}, + {86, "Fehlberg method"}, + {87, "Dormand-Prince method"}, + {88, "LSODA"}, + {89, "LSODAR"}, + {90, "LSODI"}, + {91, "LSODIS"}, + {93, "LSODPK"}, + {94, "Livermore solver"}, + {95, "sub-volume stochastic reaction-diffusion algorithm"}, + {97, "modelling and simulation algorithm characteristic"}, + {98, "type of variable"}, + {99, "type of system behaviour"}, + {100, "type of progression time step"}, + {102, "spatial description"}, + {103, "deterministic system behaviour"}, + {104, "stochastic system behaviour"}, + {105, "discrete variable"}, + {106, "continuous variable"}, + {107, "progression with adaptive time step"}, + {108, "progression with fixed time step"}, + {201, "modelling and simulation algorithm parameter"}, + {203, "particle number lower limit"}, + {204, "particle number upper limit"}, + {205, "partitioning interval"}, + {209, "relative tolerance"}, + {211, "absolute tolerance"}, + {216, "use reduced model"}, + {219, "maximum Adams order"}, + {220, "maximum BDF order"}, {223, "number of history bins"}, - {282, "KINSOL"}, - {504, "random search"}, - {28, "slow-scale stochastic simulation algorithm"}, - {384, "semi-implicit midpoint rule"}, - {351, "multinomial tau-leaping method"}, - {486, "maximum iterations"}, - {628, "modelling and simulation algorithm parameter value"}, - {256, "virtual box side length"}, - {435, "embedded Runge-Kutta 5(4) method"}, - {309, "Crank-Nicolson method"}, + {228, "tau-leaping epsilon"}, + {230, "minimum reactions per leap"}, + {231, "Pahle hybrid method"}, + {232, "LSOIBT"}, + {233, "LSODES"}, + {234, "LSODKR"}, + {235, "type of solution"}, + {236, "exact solution"}, + {237, "approximate solution"}, + {238, "type of method"}, {239, "explicit method type"}, - {537, "explicit Runge-Kutta method of order 3(2)"}, - {86, "Fehlberg method"}, - {563, "Pahle hybrid Gibson-Bruck Next Reaction method/RK-45 method"}, - {378, "implicit midpoint rule"}, - {345, "h-p cloud method"}, - {576, "Quadratic MOMA"}, + {240, "implicit method type"}, + {241, "Gillespie-like method"}, + {242, "error control parameter"}, {243, "method switching control parameter"}, - {626, "band direct solver"}, - {451, "type of updating policy"}, - {484, "maximum order"}, - {58, "Greens function reaction dynamics"}, - {289, "Adams method"}, - {382, "modified midpoint method"}, - {630, "root-finding method"}, - {362, "implicit-state Doob-Gillespie algorithm"}, - {590, "ACHR flux sampling method"}, - {548, "quadratic programming"}, - {515, "Levenberg-Marquardt"}, + {244, "granularity control parameter"}, + {248, "tau-leaping delta"}, + {249, "critical firing threshold"}, + {252, "partitioning control parameter"}, + {253, "coarse-graining factor"}, + {254, "Brownian diffusion accuracy"}, + {255, "molecules per virtual box"}, + {256, "virtual box side length"}, + {257, "surface-bound epsilon"}, + {258, "neighbour distance"}, {260, "virtual box size"}, - {807, "Elasticity coefficient (scaled)"}, - {395, "improved biconjugate gradient method"}, - {574, "species transition probabilities"}, - {40, "Poisson tau-leaping method"}, - {541, "Beta parameter for stabilized step size control"}, - {95, "sub-volume stochastic reaction-diffusion algorithm"}, - {317, "E-Cell multi-algorithm simulation method"}, - {818, "L0 matrix"}, - {413, "exact Newton method"}, - {343, "generalized finite element method"}, - {478, "preconditioner"}, - {310, "method of lines"}, - {822, "extensive property"}, + {261, "Euler method"}, + {263, "NFSim agent-based simulation method"}, + {264, "cellular automata update method"}, + {273, "hard-particle molecular dynamics"}, + {274, "first-passage Monte Carlo algorithm"}, + {276, "Gill method"}, + {278, "Metropolis Monte Carlo algorithm"}, + {279, "Adams-Bashforth method"}, + {280, "Adams-Moulton method"}, + {281, "multistep method"}, + {282, "KINSOL"}, + {283, "IDA"}, + {285, "finite volume method"}, + {286, "Euler-Maruyama method"}, {287, "Milstein method"}, - {254, "Brownian diffusion accuracy"}, - {389, "quasi-minimal residual method"}, - {356, "DASSL"}, - {624, "method for solving a system of linear equations"}, - {552, "optimization method"}, - {323, "equation-free probabilistic steady-state approximation"}, - {482, "half-bandwith parameter"}, - {228, "tau-leaping epsilon"}, - {393, "ingenious conjugate gradients-squared method"}, - {495, "random asynchronous updating policy"}, - {637, "derived property"}, - {90, "LSODI"}, - {232, "LSOIBT"}, - {367, "partitioned Runge-Kutta method"}, - {806, "Elasticity matrix (scaled)"}, - {641, "bound"}, - {526, "flux variability analysis"}, - {559, "initial step size"}, + {288, "backward differentiation formula"}, + {289, "Adams method"}, + {290, "Merson method"}, + {296, "Hammer-Hollingsworth method"}, + {297, "Lobatto method"}, + {299, "Butcher-Kuntzmann method"}, + {301, "Heun method"}, + {302, "embedded Runge-Kutta method"}, + {303, "Zonneveld method"}, + {304, "Radau method"}, + {305, "Verner method"}, + {306, "Lagrangian sliding fluid element algorithm"}, + {307, "finite difference method"}, + {308, "MacCormack method"}, + {309, "Crank-Nicolson method"}, + {310, "method of lines"}, + {311, "type of domain geometry handling"}, + {314, "S-System power-law canonical differential equations solver"}, + {315, "lattice gas automata"}, + {316, "enhanced Greens function reaction dynamics"}, + {317, "E-Cell multi-algorithm simulation method"}, + {318, "Gauss-Legendre Runge-Kutta method"}, + {319, "Monte Carlo method"}, + {320, "BioRica hybrid method"}, {321, "Cash-Karp method"}, - {51, "Bortz-Kalos-Lebowitz algorithm"}, - {585, "TOMS731"}, + {322, "hybridity"}, + {323, "equation-free probabilistic steady-state approximation"}, + {324, "nested stochastic simulation algorithm"}, + {325, "minimum fast/discrete reaction occurrences number"}, + {326, "number of samples"}, + {327, "maximum discrete number"}, {328, "minimum fast rate"}, - {820, "model and simulation property characteristic"}, - {354, "Krylov subspace projection method"}, - {602, "Minimum species threshold for continuous approximation"}, - {230, "minimum reactions per leap"}, - {595, "rFBA"}, + {329, "constant-time kinetic Monte Carlo algorithm"}, + {330, "R-leaping algorithm"}, + {331, "exact R-leaping algorithm"}, {332, "ER-leap initial leap"}, - {635, "unscaled property"}, - {493, "synchronous updating policy"}, - {276, "Gill method"}, - {524, "partitioned leaping method"}, - {45, "implicit tau-leaping method"}, - {429, "clusterization parameter"}, - {371, "stochastic differential equation problem"}, - {307, "finite difference method"}, - {652, "concentration rate"}, - {204, "particle number upper limit"}, - {409, "ordinary Newton method"}, - {280, "Adams-Moulton method"}, - {502, "DA-DFBA"}, + {333, "accelerated stochastic simulation algorithm"}, + {334, "multiparticle lattice gas automata"}, + {335, "generalized stochastic simulation algorithm"}, + {336, "D-leaping method"}, + {337, "finite element method"}, + {338, "h-version of the finite element method"}, {339, "p-version of the finite element method"}, - {84, "nonnegative Poisson tau-leaping method"}, - {263, "NFSim agent-based simulation method"}, - {561, "Pahle hybrid Gibson-Bruck Next Reaction method/Runge-Kutta method"}, - {398, "iterative method for solving a system of linear equations"}, + {340, "h-p version of the finite element method"}, + {341, "mixed finite element method"}, + {342, "level set method"}, + {343, "generalized finite element method"}, + {345, "h-p cloud method"}, + {346, "mesh-based geometry handling"}, + {347, "meshless geometry handling"}, + {348, "extended finite element method"}, + {349, "method of finite spheres"}, + {350, "probability-weighted dynamic Monte Carlo method"}, + {351, "multinomial tau-leaping method"}, + {352, "hybrid method"}, + {353, "generalized minimal residual algorithm"}, + {354, "Krylov subspace projection method"}, + {355, "DASPK"}, + {356, "DASSL"}, + {357, "conjugate gradient method"}, + {358, "biconjugate gradient method"}, + {362, "implicit-state Doob-Gillespie algorithm"}, + {363, "rule-based simulation method"}, + {364, "Adams predictor-corrector method"}, {365, "NDSolve method"}, - {838, "concentration"}, - {102, "spatial description"}, - {600, "Hybrid Adaptive Gibson - Milstein Method"}, - {467, "maximum step size"}, - {609, "Embedded Runge-Kutta Prince-Dormand (8,9) method"}, - {593, "MOMA"}, - {237, "approximate solution"}, - {831, "model and simulation property"}, - {613, "Stochastic simulation algorithm with normally-distributed next reaction times"}, - {646, "propensity"}, - {529, "parallelism"}, - {241, "Gillespie-like method"}, - {274, "first-passage Monte Carlo algorithm"}, - {471, "local optimization algorithm"}, - {809, "Reduced Jacobian matrix"}, + {366, "symplecticness"}, + {367, "partitioned Runge-Kutta method"}, + {369, "partial differential equation discretization method"}, + {370, "type of problem"}, + {371, "stochastic differential equation problem"}, + {372, "partial differential equation problem"}, + {373, "differential-algebraic equation problem"}, + {374, "ordinary differential equation problem"}, + {375, "delay differential equation problem"}, {376, "linearity of equation"}, + {377, "one-step method"}, + {378, "implicit midpoint rule"}, + {379, "Bulirsch-Stoer algorithm"}, + {380, "Richardson extrapolation based method"}, + {381, "midpoint method"}, + {382, "modified midpoint method"}, + {383, "Bader-Deuflhard method"}, + {384, "semi-implicit midpoint rule"}, + {386, "scaled preconditioned generalized minimal residual method"}, + {388, "minimal residual method"}, + {389, "quasi-minimal residual method"}, + {392, "biconjugate gradient stabilized method"}, + {393, "ingenious conjugate gradients-squared method"}, + {394, "quasi-minimal residual variant of biconjugate gradient stabilized method"}, + {395, "improved biconjugate gradient method"}, + {396, "transpose-free quasi-minimal residual algorithm"}, + {397, "preconditioning technique"}, + {398, "iterative method for solving a system of linear equations"}, + {403, "homogeneousness of equation"}, + {404, "symmetricity of matrix"}, + {405, "type of differential equation"}, {407, "steady state method"}, - {650, "sensitivity"}, - {509, "evolutionary strategy"}, - {805, "Elasticity coefficient (unscaled)"}, - {535, "VODE"}, - {568, "NLEQ1"}, - {89, "LSODAR"}, - {305, "Verner method"}, - {56, "Smoluchowski equation based method"}, - {546, "convex optimization algorithm"}, - {513, "NL2SOL"}, - {572, "level of superimposed noise"}, - {816, "Link matrix"}, - {579, "Linear MOMA"}, + {408, "Newton-type method"}, + {409, "ordinary Newton method"}, + {410, "simlified Newton method"}, {411, "Newton-like method"}, - {248, "tau-leaping delta"}, - {341, "mixed finite element method"}, - {644, "maximum flux"}, - {611, "Incremental stochastic simulation algorithm"}, - {500, "SOA-DFBA"}, - {405, "type of differential equation"}, - {380, "Richardson extrapolation based method"}, - {285, "finite volume method"}, - {507, "genetic algorithm SR"}, - {0, "modelling and simulation algorithm"}, - {252, "partitioning control parameter"}, - {544, "IDAS"}, + {412, "inexact Newton method"}, + {413, "exact Newton method"}, + {415, "maximum number of steps"}, + {416, "partial least squares regression method"}, + {417, "hierarchical cluster-based partial least squares regression method"}, {418, "N-way partial least squares regression method"}, - {511, "steepest descent"}, - {348, "extended finite element method"}, - {315, "lattice gas automata"}, - {804, "Elasticity matrix (unscaled)"}, - {827, "standard error"}, + {419, "metamodelling method"}, + {420, "number of partial least squares components"}, + {421, "type of validation"}, {422, "number of N-way partial least squares regression factors"}, - {29, "Gillespie direct algorithm"}, - {629, "Null"}, - {557, "absolute steady-state tolerance"}, - {487, "minimum damping"}, + {423, "partial least squares regression-like method"}, + {424, "mean-centring of variables"}, + {425, "standardising of variables"}, + {427, "number of clusters"}, + {428, "matrix for clusterization"}, + {429, "clusterization parameter"}, + {430, "variables preprocessing parameter"}, + {432, "IDA-like method"}, + {433, "CVODE-like method"}, + {434, "Higham-Hall method"}, + {435, "embedded Runge-Kutta 5(4) method"}, + {436, "Dormand-Prince 8(5,3) method"}, + {437, "flux balance analysis"}, + {447, "COAST"}, + {448, "logical model simulation method"}, + {449, "synchronous logical model simulation method"}, + {450, "asynchronous logical model simulation method"}, + {451, "type of updating policy"}, + {452, "random updating policy"}, + {453, "ordered updating policy"}, {454, "constant updating policy"}, - {82, "k-alpha leaping method"}, - {583, "minimum order"}, - {326, "number of samples"}, - {550, "simplex method"}, - {505, "particle swarm"}, - {622, "flux balance method"}, - {655, "rate"}, - {87, "Dormand-Prince method"}, - {283, "IDA"}, + {455, "prioritized updating policy"}, + {467, "maximum step size"}, + {468, "maximal timestep method"}, + {469, "maximal timestep"}, + {470, "optimization algorithm"}, + {471, "local optimization algorithm"}, + {472, "global optimization algorithm"}, + {473, "Bayesian inference algorithm"}, + {475, "integration method"}, + {476, "iteration type"}, + {477, "linear solver"}, + {478, "preconditioner"}, + {479, "upper half-bandwidth"}, {480, "lower half-bandwidth"}, - {352, "hybrid method"}, - {296, "Hammer-Hollingsworth method"}, + {481, "interpolate solution"}, + {482, "half-bandwith parameter"}, + {483, "step size"}, + {484, "maximum order"}, + {485, "minimum step size"}, + {486, "maximum iterations"}, + {487, "minimum damping"}, + {488, "seed"}, + {491, "discrete event simulation algorithm"}, + {492, "asynchronous updating policy"}, + {493, "synchronous updating policy"}, + {494, "fully asynchronous updating policy"}, + {495, "random asynchronous updating policy"}, + {496, "CVODES"}, + {497, "KLU"}, + {498, "number of runs"}, + {499, "dynamic flux balance analysis"}, + {500, "SOA-DFBA"}, + {501, "DOA-DFBA"}, + {502, "DA-DFBA"}, + {503, "simulated annealing"}, + {504, "random search"}, + {505, "particle swarm"}, + {506, "genetic algorithm"}, + {507, "genetic algorithm SR"}, + {508, "evolutionary programming"}, + {509, "evolutionary strategy"}, + {510, "truncated Newton"}, + {511, "steepest descent"}, + {512, "praxis"}, + {513, "NL2SOL"}, + {514, "Nelder-Mead"}, + {515, "Levenberg-Marquardt"}, + {516, "Hooke&Jeeves"}, + {517, "number of generations"}, {518, "evolutionary algorithm parameter"}, - {98, "type of variable"}, - {449, "synchronous logical model simulation method"}, - {416, "partial least squares regression method"}, - {802, "Control coefficient (scaled)"}, - {803, "Control coefficient (unscaled)"}, - {825, "mean"}, - {588, "flux sampling"}, - {420, "number of partial least squares components"}, - {555, "absolute quadrature tolerance"}, + {519, "population size"}, + {520, "evolutionary algorithm"}, + {521, "simulated annealing parameter"}, {522, "start temperature"}, - {93, "LSODPK"}, - {235, "type of solution"}, - {337, "finite element method"}, - {433, "CVODE-like method"}, - {363, "rule-based simulation method"}, - {330, "R-leaping algorithm"}, - {100, "type of progression time step"}, - {498, "number of runs"}, - {607, "Hierarchical Fehlberg method"}, + {523, "cooling factor"}, + {524, "partitioned leaping method"}, + {525, "stop condition"}, + {526, "flux variability analysis"}, + {527, "geometric flux balance analysis"}, + {528, "parsimonious enzyme usage flux balance analysis (minimum sum of absolute fluxes)"}, + {529, "parallelism"}, + {531, "fraction of optimum"}, + {532, "loopless"}, + {533, "pFBA factor"}, + {534, "reactions"}, + {535, "VODE"}, + {536, "ZVODE"}, + {537, "explicit Runge-Kutta method of order 3(2)"}, + {538, "safety factor on new step selection"}, + {539, "minimum factor to change step size by"}, + {540, "maximum factor to change step size by"}, + {541, "beta parameter for stabilized step size control"}, + {542, "correction step should use internally generated full Jacobian"}, + {543, "stability limit detection flag"}, + {544, "IDAS"}, + {545, "include sensitivity variables in error control mechanism"}, + {546, "convex optimization algorithm"}, + {547, "linear programming"}, + {548, "quadratic programming"}, {549, "non-linear programming"}, - {516, "Hooke&Jeeves"}, - {633, "computational function"}, - {491, "discrete event simulation algorithm"}, - {261, "Euler method"}, - {396, "transpose-free quasi-minimal residual algorithm"}, - {819, "Nr matrix"}, + {550, "simplex method"}, + {551, "primal-dual interior point method"}, + {552, "optimization method"}, {553, "optimization solver"}, - {801, "Concentration control coefficient matrix (unscaled)"}, - {427, "number of clusters"}, - {22, "Elf and Ehrenberg method"}, - {520, "evolutionary algorithm"}, - {324, "nested stochastic simulation algorithm"}, - {76, "Stundzia and Lumsden method"}, - {533, "pFBA factor"}, + {554, "parsimonius flux balance analysis (minimum number of active fluxes)"}, + {555, "absolute quadrature tolerance"}, + {556, "relative quadrature tolerance"}, + {557, "absolute steady-state tolerance"}, + {558, "relative steady-state tolerance"}, + {559, "initial step size"}, + {560, "LSODA/LSODAR hybrid method"}, + {561, "Pahle hybrid Gibson-Bruck Next Reaction method/Runge-Kutta method"}, + {562, "Pahle hybrid Gibson-Bruck Next Reaction method/LSODA method"}, + {563, "Pahle hybrid Gibson-Bruck Next Reaction method/RK-45 method"}, + {564, "stochastic Runge-Kutta method"}, + {565, "absolute tolerance for root finding"}, {566, "stochastic second order Runge-Kutta method"}, - {605, "SDE Tolerance"}, - {303, "Zonneveld method"}, - {107, "progression with adaptive time step"}, - {598, "Hybrid Gibson - Milstein Method"}, - {836, "amount"}, - {335, "generalized stochastic simulation algorithm"}, - {209, "relative tolerance"}, - {374, "ordinary differential equation problem"}, - {476, "iteration type"}, - {618, "bunker"}, - {279, "Adams-Bashforth method"}, - {71, "LSODE"}, + {567, "force physical correctness"}, + {568, "NLEQ1"}, + {569, "NLEQ2"}, {570, "auto reduce tolerances"}, - {48, "adaptive explicit-implicit tau-leaping method"}, - {329, "constant-time kinetic Monte Carlo algorithm"}, - {74, "binomial tau-leaping method"}, - {425, "standardising of variables"}, - {20, "PVODE"}, - {527, "geometric flux balance analysis"}, - {32, "explicit fourth-order Runge-Kutta method"}, - {834, "rate of change"}, - {564, "stochastic Runge-Kutta method"}, - {33, "Rosenbrock method"}, - {531, "fraction of optimum"}, - {105, "discrete variable"}, - {346, "mesh-based geometry handling"}, - {372, "partial differential equation problem"}, + {571, "absolute tolerance adjustment factor"}, + {572, "level of superimposed noise"}, + {573, "probabilistic logical model simulation method"}, + {574, "species transition probabilities"}, + {575, "hybrid tau-leaping method"}, + {576, "quadratic MOMA"}, {577, "flux minimization weight"}, - {649, "shadow price"}, - {616, "semi-implicit regular grid finite volume method with a fixed time step"}, - {244, "granularity control parameter"}, - {379, "Bulirsch-Stoer algorithm"}, - {211, "absolute tolerance"}, - {452, "random updating policy"}, + {578, "nested algorithm"}, + {579, "linear MOMA"}, + {580, "ROOM"}, {581, "BKMC"}, - {653, "particle number rate"}, + {582, "Spatiocyte method"}, + {583, "minimum order"}, + {584, "initial order"}, + {585, "TOMS731"}, + {586, "Gibson-Bruck next reaction algorithm with indexed priority queue"}, + {587, "IMEX"}, + {588, "flux sampling"}, + {589, "ACB flux sampling method"}, + {590, "ACHR flux sampling method"}, + {591, "mdFBA"}, + {592, "dynamic rFBA"}, + {593, "MOMA"}, + {594, "order"}, + {595, "rFBA"}, + {596, "srFBA"}, + {597, "tolerance"}, + {598, "hybrid Gibson - Milstein method"}, + {599, "hybrid Gibson - Euler-Maruyama method"}, + {600, "hybrid adaptive Gibson - Milstein method"}, + {601, "number of trials"}, + {602, "minimum species threshold for continuous approximation"}, + {603, "minimum reaction rate for continuous approximation"}, + {604, "MSR tolerance"}, + {605, "SDE tolerance"}, + {606, "hierarchical stochastic simulation algorithm"}, + {607, "hierarchical Fehlberg method"}, + {608, "hierarchical flux balance analysis"}, + {609, "embedded Runge-Kutta Prince-Dormand (8,9) method"}, + {610, "composite-rejection stochastic simulation algorithm"}, + {611, "incremental stochastic simulation algorithm"}, + {612, "implicit 4th order Runge-Kutta method at Gaussian points"}, + {613, "stochastic simulation algorithm with normally-distributed next reaction times"}, + {614, "implementation"}, + {615, "fully-implicit regular grid finite volume method with a variable time step"}, + {616, "semi-implicit regular grid finite volume method with a fixed time step"}, + {617, "IDA-CVODE hybrid method"}, + {618, "bunker"}, + {619, "emc-sim"}, {620, "parsimonius flux balance analysis"}, - {257, "surface-bound epsilon"}, - {580, "ROOM"}, - {27, "Gibson-Bruck next reaction algorithm"}, - {350, "probability-weighted dynamic Monte Carlo method"}, - {308, "MacCormack method"}, - {538, "safety factor on new step selection"}, - {468, "maximal timestep method"}, - {205, "partitioning interval"}, - {30, "Euler forward method"}, - {800, "systems property"}, - {436, "Dormand-Prince 8(5,3) method"}, - {403, "homogeneousness of equation"}, - {614, "Implementation"}, - {472, "global optimization algorithm"}, - {311, "type of domain geometry handling"}, - {575, "Hybrid tau-leaping method"}, - {542, "correction step should use internally generated full Jacobian"}, - {288, "backward differentiation formula"}, + {621, "stochastic simulation leaping method"}, + {622, "flux balance method"}, + {623, "flux balance problem"}, + {624, "method for solving a system of linear equations"}, + {625, "dense direct solver"}, + {626, "band direct solver"}, {627, "diagonal approximate Jacobian solver"}, - {485, "minimum step size"}, - {815, "Flux control coefficient matrix (scaled)"}, - {255, "molecules per virtual box"}, - {357, "conjugate gradient method"}, - {383, "Bader-Deuflhard method"}, - {38, "sorting stochastic simulation algorithm"}, - {394, "quasi-minimal residual variant of biconjugate gradient stabilized method"}, - {591, "mdFBA"}, - {638, "level"}, - {496, "CVODES"}, + {628, "modelling and simulation algorithm parameter value"}, + {629, "null"}, + {630, "root-finding method"}, {631, "iterative root-finding method"}, - {514, "Nelder-Mead"}, - {318, "Gauss-Legendre Runge-Kutta method"}, - {447, "COAST"}, - {540, "maximum factor to change step size by"}, - {573, "probabilistic logical model simulation method"}, - {817, "Kernel matrix"}, - {479, "upper half-bandwidth"}, - {249, "critical firing threshold"}, - {19, "CVODE"}, - {216, "integrate reduced model"}, - {586, "Gibson-Bruck next reaction algorithm with indexed priority queue"}, - {821, "intensive property"}, - {483, "step size"}, - {625, "dense direct solver"}, - {253, "coarse-graining factor"}, - {450, "asynchronous logical model simulation method"}, - {388, "minimal residual method"}, - {220, "maximum BDF order"}, - {355, "DASPK"}, - {322, "hybridity"}, - {419, "metamodelling method"}, - {299, "Butcher-Kuntzmann method"}, - {814, "Flux control coefficient matrix (unscaled)"}, - {233, "LSODES"}, - {91, "LSODIS"}, - {392, "biconjugate gradient stabilized method"}, + {632, "functional iteration root-finding method"}, + {633, "computational function"}, + {634, "scaled property"}, + {635, "unscaled property"}, {636, "primary property"}, - {603, "Minimum reaction rate for continuous approximation"}, - {68, "deterministic cellular automata update algorithm"}, - {558, "relative steady-state tolerance"}, - {525, "stop condition"}, + {637, "derived property"}, + {638, "level"}, + {639, "flux"}, + {640, "lower bound"}, + {641, "bound"}, {642, "minimum flux"}, - {423, "partial least squares regression-like method"}, - {353, "generalized minimal residual algorithm"}, - {320, "BioRica hybrid method"}, - {455, "prioritized updating policy"}, - {584, "initial order"}, - {551, "primal-dual interior point method"}, - {828, "maximum"}, - {596, "srFBA"}, - {601, "Number of trials"}, - {333, "accelerated stochastic simulation algorithm"}, - {366, "symplecticness"}, - {103, "deterministic system behaviour"}, - {832, "time"}, - {812, "Jacobian matrix"}, - {264, "cellular automata update method"}, - {297, "Lobatto method"}, - {519, "population size"}, - {494, "fully asynchronous updating policy"}, - {231, "Pahle hybrid method"}, + {643, "upper bound"}, + {644, "maximum flux"}, + {645, "objective value"}, + {646, "propensity"}, {647, "derivative"}, - {46, "trapezoidal tau-leaping method"}, - {640, "lower bound"}, - {813, "Eigenvalue matrix"}, - {370, "type of problem"}, - {839, "temperature"}, - {434, "Higham-Hall method"}, - {569, "NLEQ2"}, - {306, "Lagrangian sliding fluid element algorithm"}, - {536, "ZVODE"}, - {281, "multistep method"}, - {503, "simulated annealing"}, - {408, "Newton-type method"}, + {648, "step"}, + {649, "shadow price"}, + {650, "sensitivity"}, {651, "reduced costs"}, - {562, "Pahle hybrid Gibson-Bruck Next Reaction method/LSODA method"}, - {492, "asynchronous updating policy"}, - {634, "scaled property"}, - {238, "type of method"}, - {594, "order"}, - {397, "preconditioning technique"}, - {331, "exact R-leaping algorithm"}, - {364, "Adams predictor-corrector method"}, - {499, "dynamic flux balance analysis"}, - {608, "Hierarchical flux balance analysis"}, - {242, "error control parameter"}, - {377, "one-step method"}, - {645, "objective value"}, - {612, "implicit 4th order Runge-Kutta method at Gaussian points"}, - {470, "optimization algorithm"}, - {811, "Stoichiometry matrix"}, - {3, "weighted stochastic simulation algorithm"}, - {501, "DOA-DFBA"}, - {381, "midpoint method"}, - {57, "Brownian diffusion Smoluchowski method"}, - {304, "Radau method"}, - {534, "reactions"}, - {349, "method of finite spheres"}, - {94, "Livermore solver"}, - {316, "enhanced Greens function reaction dynamics"}, - {412, "inexact Newton method"}, - {547, "linear programming"}, - {619, "emc-sim"}, - {477, "linear solver"}, - {273, "hard-particle molecular dynamics"}, - {240, "implicit method type"}, - {375, "delay differential equation problem"}, - {342, "level set method"}, - {610, "Composite-rejection stochastic simulation algorithm"}, - {88, "LSODA"}, - {508, "evolutionary programming"}, - {286, "Euler-Maruyama method"}, + {652, "concentration rate"}, + {653, "particle number rate"}, + {654, "amount rate"}, + {655, "rate"}, {656, "use adaptive time steps"}, - {481, "interpolate solution"}, - {623, "flux balance problem"}, - {410, "simlified Newton method"}, - {578, "nested algorithm"}, - {290, "Merson method"}, - {512, "praxis"}, - {545, "include sensitivity variables in error control mechanism"}, - {417, "hierarchical cluster-based partial least squares regression method"}, - {314, "S-System power-law canonical differential equations solver"}, - {582, "Spatiocyte method"}, - {826, "standard deviation"}, - {488, "seed"}, - {258, "neighbour distance"}, - {327, "maximum discrete number"}, + {657, "sequential logical simulation method"}, + {658, "logical model analysis method"}, + {659, "Naldi MDD logical model stable state search method"}, + {660, "logical model stable state search method"}, + {661, "logical model trap space identification method"}, + {662, "Klarner ASP logical model trap space identification method"}, + {663, "BDD logical model trap space identification method"}, + {664, "Second order backward implicit product Euler scheme"}, + {665, "maximum number of iterations for root finding"}, + {666, "Jacobian epsilon"}, + {667, "memory size"}, + {668, "Numerical Recipes in C "stiff\" Rosenbrock method"}, + {669, "Resource Balance Analysis"}, + {670, "use multiple steps"}, + {671, "use stiff method"}, + {672, "Numerical Recipes in C "quality-controlled Runge-Kutta\" method"}, + {673, "skip reactions that produce negative species amounts"}, + {674, "presimulate"}, + {675, "Broyden method"}, + {676, "degree of linearity"}, + {677, "maximum number of steps for presimulation"}, + {678, "maximum number of steps for approximation"}, + {679, "maximum time for approximation"}, + {680, "duration"}, + {681, "maximum time"}, + {682, "allow approximation"}, + {683, "relative tolerance for approximation"}, + {684, "number of steps per output"}, + {685, "biological state optimization method"}, + {686, "Enzyme Cost Minimization"}, + {687, "Max-min Driving Force method"}, + {688, "type of system described"}, + {689, "mathematical system"}, + {690, "biological system"}, + {691, "metabolic system"}, + {692, "cellular system"}, + {693, "biochemical system"}, + {694, "ODE solver"}, + {695, "parameters for"}, + {800, "systems property"}, + {801, "concentration control coefficient matrix (unscaled)"}, + {802, "control coefficient (scaled)"}, + {803, "control coefficient (unscaled)"}, + {804, "elasticity matrix (unscaled)"}, + {805, "elasticity coefficient (unscaled)"}, + {806, "elasticity matrix (scaled)"}, + {807, "elasticity coefficient (scaled)"}, + {808, "reduced stoichiometry matrix"}, + {809, "reduced Jacobian matrix"}, + {810, "reduced eigenvalue matrix"}, + {811, "stoichiometry matrix"}, + {812, "Jacobian matrix"}, + {813, "Eigenvalue matrix"}, + {814, "flux control coefficient matrix (unscaled)"}, + {815, "flux control coefficient matrix (scaled)"}, + {816, "link matrix"}, + {817, "kernel matrix"}, + {818, "L0 matrix"}, + {819, "Nr matrix"}, + {820, "model and simulation property characteristic"}, + {821, "intensive property"}, + {822, "extensive property"}, + {824, "aggregation function"}, + {825, "mean ignoring NaN"}, + {826, "standard deviation ignoring NaN"}, + {827, "standard error ignoring NaN"}, + {828, "maximum ignoring NaN"}, + {829, "minimum ignoring NaN"}, + {830, "maximum"}, + {831, "model and simulation property"}, + {832, "time"}, + {834, "rate of change"}, + {835, "concentration control coefficient matrix (scaled)"}, + {836, "amount"}, + {837, "particle number"}, + {838, "concentration"}, + {839, "temperature"}, + {840, "minimum"}, + {841, "mean"}, + {842, "standard deviation"}, + {843, "standard error"}, + {844, "sum ignoring NaN"}, + {845, "sum"}, + {846, "product ignoring NaN"}, + {847, "product"}, + {848, "cumulative sum ignoring NaN"}, + {849, "cumulative sum"}, + {850, "cumulative product ignoring NaN"}, + {851, "cumulative product"}, + {852, "count ignoring NaN"}, + {853, "count"}, + {854, "length ignoring NaN"}, + {855, "length"}, + {856, "median ignoring NaN"}, + {857, "median"}, + {858, "variance ignoring NaN"}, + {859, "variance"}, }; - - -LIBSEDML_CPP_NAMESPACE_END From f62efee13edc47c9fcd952987b27883ed13c9b69 Mon Sep 17 00:00:00 2001 From: Lucian Smith Date: Wed, 24 May 2023 14:05:00 -0700 Subject: [PATCH 02/16] Fix names with quotes in 'em. --- src/kisao/transform_kisao.py | 3 +++ src/sedml/kisaomap.cpp | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/kisao/transform_kisao.py b/src/kisao/transform_kisao.py index 0e69e81a..2c7e6b91 100644 --- a/src/kisao/transform_kisao.py +++ b/src/kisao/transform_kisao.py @@ -65,6 +65,9 @@ continue k_id = int(row[0].split("_")[-1]) k_name = row[1] + if '"' in k_name: + k_name = k_name.replace('"', "'") + k_name = k_name.replace("\\", "") allterms.append((k_id, k_name)) allterms.sort() diff --git a/src/sedml/kisaomap.cpp b/src/sedml/kisaomap.cpp index da6aba56..8ace820f 100644 --- a/src/sedml/kisaomap.cpp +++ b/src/sedml/kisaomap.cpp @@ -477,11 +477,11 @@ std::map g_kisaomap = { {665, "maximum number of iterations for root finding"}, {666, "Jacobian epsilon"}, {667, "memory size"}, - {668, "Numerical Recipes in C "stiff\" Rosenbrock method"}, + {668, "Numerical Recipes in C 'stiff' Rosenbrock method"}, {669, "Resource Balance Analysis"}, {670, "use multiple steps"}, {671, "use stiff method"}, - {672, "Numerical Recipes in C "quality-controlled Runge-Kutta\" method"}, + {672, "Numerical Recipes in C 'quality-controlled Runge-Kutta' method"}, {673, "skip reactions that produce negative species amounts"}, {674, "presimulate"}, {675, "Broyden method"}, From 5386e7ec04e5dcb019c9f432a2a18161e4e79dd9 Mon Sep 17 00:00:00 2001 From: Lucian Smith Date: Wed, 24 May 2023 14:50:02 -0700 Subject: [PATCH 03/16] Put kisaomap in sedml namespace. --- src/kisao/transform_kisao.py | 5 ++++- src/sedml/kisaomap.cpp | 5 +++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/src/kisao/transform_kisao.py b/src/kisao/transform_kisao.py index 2c7e6b91..34ee28d1 100644 --- a/src/kisao/transform_kisao.py +++ b/src/kisao/transform_kisao.py @@ -53,6 +53,9 @@ #include #include +#include "sedml/common/libsedml-version.h" + +LIBSEDML_CPP_NAMESPACE_BEGIN std::map g_kisaomap = { """) @@ -75,5 +78,5 @@ kcpp.write(' {' + str(k_id) + ', "' + k_name + '"},\n') print(k_id, k_name) -kcpp.write("};\n") +kcpp.write("};\nLIBSEDML_CPP_NAMESPACE_END\n\n") kcpp.close() diff --git a/src/sedml/kisaomap.cpp b/src/sedml/kisaomap.cpp index 8ace820f..919bd2b2 100644 --- a/src/sedml/kisaomap.cpp +++ b/src/sedml/kisaomap.cpp @@ -40,6 +40,9 @@ #include #include +#include "sedml/common/libsedml-version.h" + +LIBSEDML_CPP_NAMESPACE_BEGIN std::map g_kisaomap = { {0, "modelling and simulation algorithm"}, @@ -564,3 +567,5 @@ std::map g_kisaomap = { {858, "variance ignoring NaN"}, {859, "variance"}, }; +LIBSEDML_CPP_NAMESPACE_END + From 202ea2d771b8bc657bcf381ce319deec31e06b61 Mon Sep 17 00:00:00 2001 From: Lucian Smith Date: Wed, 24 May 2023 15:10:17 -0700 Subject: [PATCH 04/16] Maybe java wants SWIGEXPORT defined like this now? --- src/bindings/java/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/bindings/java/CMakeLists.txt b/src/bindings/java/CMakeLists.txt index 2525711b..5cbc638a 100755 --- a/src/bindings/java/CMakeLists.txt +++ b/src/bindings/java/CMakeLists.txt @@ -77,7 +77,7 @@ if (LIBSEDML_REMOVE_WRAPPERS) endif(LIBSEDML_REMOVE_WRAPPERS) -set(SWIG_EXTRA_FLAGS -DSWIGEXPORT +set(SWIG_EXTRA_FLAGS -DSWIGEXPORT= -DLIBSBML_CPP_NAMESPACE_BEGIN= -DLIBSBML_CPP_NAMESPACE_END= -DLIBSBML_CPP_NAMESPACE_QUALIFIER= -DLIBSBML_CPP_NAMESPACE_USE= -DLIBNUML_CPP_NAMESPACE_BEGIN= -DLIBNUML_CPP_NAMESPACE_END= -DLIBNUML_CPP_NAMESPACE_QUALIFIER= -DLIBNUML_CPP_NAMESPACE_USE= ) From ed5fb73e19f578c54c2b1de8265c5f8b5998c023 Mon Sep 17 00:00:00 2001 From: Frank Bergmann Date: Fri, 4 Aug 2023 09:13:54 +0200 Subject: [PATCH 05/16] #174: add basic instructions on how to get API documentation --- .gitignore | 4 + README.md | 13 +- libSEDML.doxyfile | 2460 +++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 2476 insertions(+), 1 deletion(-) create mode 100644 libSEDML.doxyfile diff --git a/.gitignore b/.gitignore index e3f1a269..49b2efcf 100644 --- a/.gitignore +++ b/.gitignore @@ -173,3 +173,7 @@ libsedml/_* examples/python/*.xml dev/temp_deviser_file.xml setup.py + +# documentation +/doc/html/ +Doxyfile \ No newline at end of file diff --git a/README.md b/README.md index 5edd8990..9d2520e6 100644 --- a/README.md +++ b/README.md @@ -39,12 +39,23 @@ note the semicolon denoting the listing of several libraries. Of course you coul for linking against `expat` and indicating, that libSBML was compiled without compression. +## Documentation +API documentation is something to be added and pull requests are happily accepted to improve them. For now a basic doxygen file +is provided and documentation can be generated after checkout like so: + +```bash +PROJECT_NUMBER=2.0.32 doxygen -x libSEDML.doxyfile > Doxyfile && doxygen +``` + +This expands the version number in the doxygen file, creates a temporary `Doxyfile` and runs doxygen with it. After that the +generated documentation is available in the `./doc/html` folder. + ## License This project is open source and freely available under the [Simplified BSD](http://opensource.org/licenses/BSD-2-Clause) license. Should that license not meet your needs, please contact me. -Copyright (c) 2013-2021, Frank T. Bergmann +Copyright (c) 2013-2023, Frank T. Bergmann All rights reserved. Redistribution and use in source and binary forms, with or without diff --git a/libSEDML.doxyfile b/libSEDML.doxyfile new file mode 100644 index 00000000..c36c5015 --- /dev/null +++ b/libSEDML.doxyfile @@ -0,0 +1,2460 @@ +# Doxyfile 1.8.14 + +# This file describes the settings to be used by the documentation system +# doxygen (www.doxygen.org) for a project. +# +# All text after a double hash (##) is considered a comment and is placed in +# front of the TAG it is preceding. +# +# All text after a single hash (#) is considered a comment and will be ignored. +# The format is: +# TAG = value [value, ...] +# For lists, items can also be appended using: +# TAG += value [value, ...] +# Values that contain spaces should be placed between quotes (\" \"). + +#--------------------------------------------------------------------------- +# Project related configuration options +#--------------------------------------------------------------------------- + +# This tag specifies the encoding used for all characters in the config file +# that follow. The default is UTF-8 which is also the encoding used for all text +# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv +# built into libc) for the transcoding. See +# https://www.gnu.org/software/libiconv/ for the list of possible encodings. +# The default value is: UTF-8. + +DOXYFILE_ENCODING = UTF-8 + +# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by +# double-quotes, unless you are using Doxywizard) that should identify the +# project for which the documentation is generated. This name is used in the +# title of most generated pages and in a few other places. +# The default value is: My Project. + +PROJECT_NAME = "LIBSEDML API" + +# The PROJECT_NUMBER tag can be used to enter a project or revision number. This +# could be handy for archiving the generated documentation or if some version +# control system is used. + +PROJECT_NUMBER = $(PROJECT_NUMBER) + +# Using the PROJECT_BRIEF tag one can provide an optional one line description +# for a project that appears at the top of each page and should give viewer a +# quick idea about the purpose of the project. Keep the description short. + +PROJECT_BRIEF = + +# With the PROJECT_LOGO tag one can specify a logo or an icon that is included +# in the documentation. The maximum height of the logo should not exceed 55 +# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy +# the logo to the output directory. + +PROJECT_LOGO = + +# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path +# into which the generated documentation will be written. If a relative path is +# entered, it will be relative to the location where doxygen was started. If +# left blank the current directory will be used. + +OUTPUT_DIRECTORY = ./doc + +# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub- +# directories (in 2 levels) under the output directory of each output format and +# will distribute the generated files over these directories. Enabling this +# option can be useful when feeding doxygen a huge amount of source files, where +# putting all generated files in the same directory would otherwise causes +# performance problems for the file system. +# The default value is: NO. + +CREATE_SUBDIRS = YES + +# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII +# characters to appear in the names of generated files. If set to NO, non-ASCII +# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode +# U+3044. +# The default value is: NO. + +ALLOW_UNICODE_NAMES = NO + +# The OUTPUT_LANGUAGE tag is used to specify the language in which all +# documentation generated by doxygen is written. Doxygen will use this +# information to generate all constant output in the proper language. +# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese, +# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States), +# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian, +# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages), +# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian, +# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian, +# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish, +# Ukrainian and Vietnamese. +# The default value is: English. + +OUTPUT_LANGUAGE = English + +# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member +# descriptions after the members that are listed in the file and class +# documentation (similar to Javadoc). Set to NO to disable this. +# The default value is: YES. + +BRIEF_MEMBER_DESC = YES + +# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief +# description of a member or function before the detailed description +# +# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the +# brief descriptions will be completely suppressed. +# The default value is: YES. + +REPEAT_BRIEF = YES + +# This tag implements a quasi-intelligent brief description abbreviator that is +# used to form the text in various listings. Each string in this list, if found +# as the leading text of the brief description, will be stripped from the text +# and the result, after processing the whole list, is used as the annotated +# text. Otherwise, the brief description is used as-is. If left blank, the +# following values are used ($name is automatically replaced with the name of +# the entity):The $name class, The $name widget, The $name file, is, provides, +# specifies, contains, represents, a, an and the. + +ABBREVIATE_BRIEF = + +# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then +# doxygen will generate a detailed section even if there is only a brief +# description. +# The default value is: NO. + +ALWAYS_DETAILED_SEC = NO + +# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all +# inherited members of a class in the documentation of that class as if those +# members were ordinary class members. Constructors, destructors and assignment +# operators of the base classes will not be shown. +# The default value is: NO. + +INLINE_INHERITED_MEMB = NO + +# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path +# before files name in the file list and in the header files. If set to NO the +# shortest path that makes the file name unique will be used +# The default value is: YES. + +FULL_PATH_NAMES = NO + +# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path. +# Stripping is only done if one of the specified strings matches the left-hand +# part of the path. The tag can be used to show relative paths in the file list. +# If left blank the directory from which doxygen is run is used as the path to +# strip. +# +# Note that you can specify absolute paths here, but also relative paths, which +# will be relative from the directory where doxygen is started. +# This tag requires that the tag FULL_PATH_NAMES is set to YES. + +STRIP_FROM_PATH = + +# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the +# path mentioned in the documentation of a class, which tells the reader which +# header file to include in order to use a class. If left blank only the name of +# the header file containing the class definition is used. Otherwise one should +# specify the list of include paths that are normally passed to the compiler +# using the -I flag. + +STRIP_FROM_INC_PATH = + +# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but +# less readable) file names. This can be useful is your file systems doesn't +# support long names like on DOS, Mac, or CD-ROM. +# The default value is: NO. + +SHORT_NAMES = NO + +# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the +# first line (until the first dot) of a Javadoc-style comment as the brief +# description. If set to NO, the Javadoc-style will behave just like regular Qt- +# style comments (thus requiring an explicit @brief command for a brief +# description.) +# The default value is: NO. + +JAVADOC_AUTOBRIEF = NO + +# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first +# line (until the first dot) of a Qt-style comment as the brief description. If +# set to NO, the Qt-style will behave just like regular Qt-style comments (thus +# requiring an explicit \brief command for a brief description.) +# The default value is: NO. + +QT_AUTOBRIEF = NO + +# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a +# multi-line C++ special comment block (i.e. a block of //! or /// comments) as +# a brief description. This used to be the default behavior. The new default is +# to treat a multi-line C++ comment block as a detailed description. Set this +# tag to YES if you prefer the old behavior instead. +# +# Note that setting this tag to YES also means that rational rose comments are +# not recognized any more. +# The default value is: NO. + +MULTILINE_CPP_IS_BRIEF = NO + +# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the +# documentation from any documented member that it re-implements. +# The default value is: YES. + +INHERIT_DOCS = YES + +# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new +# page for each member. If set to NO, the documentation of a member will be part +# of the file/class/namespace that contains it. +# The default value is: NO. + +SEPARATE_MEMBER_PAGES = NO + +# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen +# uses this value to replace tabs by spaces in code fragments. +# Minimum value: 1, maximum value: 16, default value: 4. + +TAB_SIZE = 2 + +# This tag can be used to specify a number of aliases that act as commands in +# the documentation. An alias has the form: +# name=value +# For example adding +# "sideeffect=@par Side Effects:\n" +# will allow you to put the command \sideeffect (or @sideeffect) in the +# documentation, which will result in a user-defined paragraph with heading +# "Side Effects:". You can put \n's in the value part of an alias to insert +# newlines (in the resulting output). You can put ^^ in the value part of an +# alias to insert a newline as if a physical newline was in the original file. + +ALIASES = + +# This tag can be used to specify a number of word-keyword mappings (TCL only). +# A mapping has the form "name=value". For example adding "class=itcl::class" +# will allow you to use the command class in the itcl::class meaning. + +TCL_SUBST = + +# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources +# only. Doxygen will then generate output that is more tailored for C. For +# instance, some of the names that are used will be different. The list of all +# members will be omitted, etc. +# The default value is: NO. + +OPTIMIZE_OUTPUT_FOR_C = NO + +# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or +# Python sources only. Doxygen will then generate output that is more tailored +# for that language. For instance, namespaces will be presented as packages, +# qualified scopes will look different, etc. +# The default value is: NO. + +OPTIMIZE_OUTPUT_JAVA = NO + +# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran +# sources. Doxygen will then generate output that is tailored for Fortran. +# The default value is: NO. + +OPTIMIZE_FOR_FORTRAN = NO + +# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL +# sources. Doxygen will then generate output that is tailored for VHDL. +# The default value is: NO. + +OPTIMIZE_OUTPUT_VHDL = NO + +# Doxygen selects the parser to use depending on the extension of the files it +# parses. With this tag you can assign which parser to use for a given +# extension. Doxygen has a built-in mapping, but you can override or extend it +# using this tag. The format is ext=language, where ext is a file extension, and +# language is one of the parsers supported by doxygen: IDL, Java, Javascript, +# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran: +# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran: +# Fortran. In the later case the parser tries to guess whether the code is fixed +# or free formatted code, this is the default for Fortran type files), VHDL. For +# instance to make doxygen treat .inc files as Fortran files (default is PHP), +# and .f files as C (default is Fortran), use: inc=Fortran f=C. +# +# Note: For files without extension you can use no_extension as a placeholder. +# +# Note that for custom extensions you also need to set FILE_PATTERNS otherwise +# the files are not read by doxygen. + +EXTENSION_MAPPING = + +# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments +# according to the Markdown format, which allows for more readable +# documentation. See http://daringfireball.net/projects/markdown/ for details. +# The output of markdown processing is further processed by doxygen, so you can +# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in +# case of backward compatibilities issues. +# The default value is: YES. + +MARKDOWN_SUPPORT = YES + +# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up +# to that level are automatically included in the table of contents, even if +# they do not have an id attribute. +# Note: This feature currently applies only to Markdown headings. +# Minimum value: 0, maximum value: 99, default value: 0. +# This tag requires that the tag MARKDOWN_SUPPORT is set to YES. + +TOC_INCLUDE_HEADINGS = 0 + +# When enabled doxygen tries to link words that correspond to documented +# classes, or namespaces to their corresponding documentation. Such a link can +# be prevented in individual cases by putting a % sign in front of the word or +# globally by setting AUTOLINK_SUPPORT to NO. +# The default value is: YES. + +AUTOLINK_SUPPORT = YES + +# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want +# to include (a tag file for) the STL sources as input, then you should set this +# tag to YES in order to let doxygen match functions declarations and +# definitions whose arguments contain STL classes (e.g. func(std::string); +# versus func(std::string) {}). This also make the inheritance and collaboration +# diagrams that involve STL classes more complete and accurate. +# The default value is: NO. + +BUILTIN_STL_SUPPORT = YES + +# If you use Microsoft's C++/CLI language, you should set this option to YES to +# enable parsing support. +# The default value is: NO. + +CPP_CLI_SUPPORT = NO + +# Set the SIP_SUPPORT tag to YES if your project consists of sip (see: +# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen +# will parse them like normal C++ but will assume all classes use public instead +# of private inheritance when no explicit protection keyword is present. +# The default value is: NO. + +SIP_SUPPORT = NO + +# For Microsoft's IDL there are propget and propput attributes to indicate +# getter and setter methods for a property. Setting this option to YES will make +# doxygen to replace the get and set methods by a property in the documentation. +# This will only work if the methods are indeed getting or setting a simple +# type. If this is not the case, or you want to show the methods anyway, you +# should set this option to NO. +# The default value is: YES. + +IDL_PROPERTY_SUPPORT = YES + +# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC +# tag is set to YES then doxygen will reuse the documentation of the first +# member in the group (if any) for the other members of the group. By default +# all members of a group must be documented explicitly. +# The default value is: NO. + +DISTRIBUTE_GROUP_DOC = NO + +# If one adds a struct or class to a group and this option is enabled, then also +# any nested class or struct is added to the same group. By default this option +# is disabled and one has to add nested compounds explicitly via \ingroup. +# The default value is: NO. + +GROUP_NESTED_COMPOUNDS = NO + +# Set the SUBGROUPING tag to YES to allow class member groups of the same type +# (for instance a group of public functions) to be put as a subgroup of that +# type (e.g. under the Public Functions section). Set it to NO to prevent +# subgrouping. Alternatively, this can be done per class using the +# \nosubgrouping command. +# The default value is: YES. + +SUBGROUPING = YES + +# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions +# are shown inside the group in which they are included (e.g. using \ingroup) +# instead of on a separate page (for HTML and Man pages) or section (for LaTeX +# and RTF). +# +# Note that this feature does not work in combination with +# SEPARATE_MEMBER_PAGES. +# The default value is: NO. + +INLINE_GROUPED_CLASSES = NO + +# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions +# with only public data fields or simple typedef fields will be shown inline in +# the documentation of the scope in which they are defined (i.e. file, +# namespace, or group documentation), provided this scope is documented. If set +# to NO, structs, classes, and unions are shown on a separate page (for HTML and +# Man pages) or section (for LaTeX and RTF). +# The default value is: NO. + +INLINE_SIMPLE_STRUCTS = NO + +# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or +# enum is documented as struct, union, or enum with the name of the typedef. So +# typedef struct TypeS {} TypeT, will appear in the documentation as a struct +# with name TypeT. When disabled the typedef will appear as a member of a file, +# namespace, or class. And the struct will be named TypeS. This can typically be +# useful for C code in case the coding convention dictates that all compound +# types are typedef'ed and only the typedef is referenced, never the tag name. +# The default value is: NO. + +TYPEDEF_HIDES_STRUCT = NO + +# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This +# cache is used to resolve symbols given their name and scope. Since this can be +# an expensive process and often the same symbol appears multiple times in the +# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small +# doxygen will become slower. If the cache is too large, memory is wasted. The +# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range +# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536 +# symbols. At the end of a run doxygen will report the cache usage and suggest +# the optimal cache size from a speed point of view. +# Minimum value: 0, maximum value: 9, default value: 0. + +LOOKUP_CACHE_SIZE = 1 + +#--------------------------------------------------------------------------- +# Build related configuration options +#--------------------------------------------------------------------------- + +# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in +# documentation are documented, even if no documentation was available. Private +# class members and static file members will be hidden unless the +# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES. +# Note: This will also disable the warnings about undocumented members that are +# normally produced when WARNINGS is set to YES. +# The default value is: NO. + +EXTRACT_ALL = YES + +# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will +# be included in the documentation. +# The default value is: NO. + +EXTRACT_PRIVATE = YES + +# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal +# scope will be included in the documentation. +# The default value is: NO. + +EXTRACT_PACKAGE = NO + +# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be +# included in the documentation. +# The default value is: NO. + +EXTRACT_STATIC = NO + +# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined +# locally in source files will be included in the documentation. If set to NO, +# only classes defined in header files are included. Does not have any effect +# for Java sources. +# The default value is: YES. + +EXTRACT_LOCAL_CLASSES = YES + +# This flag is only useful for Objective-C code. If set to YES, local methods, +# which are defined in the implementation section but not in the interface are +# included in the documentation. If set to NO, only methods in the interface are +# included. +# The default value is: NO. + +EXTRACT_LOCAL_METHODS = NO + +# If this flag is set to YES, the members of anonymous namespaces will be +# extracted and appear in the documentation as a namespace called +# 'anonymous_namespace{file}', where file will be replaced with the base name of +# the file that contains the anonymous namespace. By default anonymous namespace +# are hidden. +# The default value is: NO. + +EXTRACT_ANON_NSPACES = NO + +# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all +# undocumented members inside documented classes or files. If set to NO these +# members will be included in the various overviews, but no documentation +# section is generated. This option has no effect if EXTRACT_ALL is enabled. +# The default value is: NO. + +HIDE_UNDOC_MEMBERS = NO + +# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all +# undocumented classes that are normally visible in the class hierarchy. If set +# to NO, these classes will be included in the various overviews. This option +# has no effect if EXTRACT_ALL is enabled. +# The default value is: NO. + +HIDE_UNDOC_CLASSES = NO + +# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend +# (class|struct|union) declarations. If set to NO, these declarations will be +# included in the documentation. +# The default value is: NO. + +HIDE_FRIEND_COMPOUNDS = NO + +# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any +# documentation blocks found inside the body of a function. If set to NO, these +# blocks will be appended to the function's detailed documentation block. +# The default value is: NO. + +HIDE_IN_BODY_DOCS = NO + +# The INTERNAL_DOCS tag determines if documentation that is typed after a +# \internal command is included. If the tag is set to NO then the documentation +# will be excluded. Set it to YES to include the internal documentation. +# The default value is: NO. + +INTERNAL_DOCS = NO + +# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file +# names in lower-case letters. If set to YES, upper-case letters are also +# allowed. This is useful if you have classes or files whose names only differ +# in case and if your file system supports case sensitive file names. Windows +# and Mac users are advised to set this option to NO. +# The default value is: system dependent. + +CASE_SENSE_NAMES = YES + +# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with +# their full class and namespace scopes in the documentation. If set to YES, the +# scope will be hidden. +# The default value is: NO. + +HIDE_SCOPE_NAMES = NO + +# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will +# append additional text to a page's title, such as Class Reference. If set to +# YES the compound reference will be hidden. +# The default value is: NO. + +HIDE_COMPOUND_REFERENCE= NO + +# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of +# the files that are included by a file in the documentation of that file. +# The default value is: YES. + +SHOW_INCLUDE_FILES = YES + +# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each +# grouped member an include statement to the documentation, telling the reader +# which file to include in order to use the member. +# The default value is: NO. + +SHOW_GROUPED_MEMB_INC = NO + +# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include +# files with double quotes in the documentation rather than with sharp brackets. +# The default value is: NO. + +FORCE_LOCAL_INCLUDES = NO + +# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the +# documentation for inline members. +# The default value is: YES. + +INLINE_INFO = YES + +# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the +# (detailed) documentation of file and class members alphabetically by member +# name. If set to NO, the members will appear in declaration order. +# The default value is: YES. + +SORT_MEMBER_DOCS = YES + +# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief +# descriptions of file, namespace and class members alphabetically by member +# name. If set to NO, the members will appear in declaration order. Note that +# this will also influence the order of the classes in the class list. +# The default value is: NO. + +SORT_BRIEF_DOCS = YES + +# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the +# (brief and detailed) documentation of class members so that constructors and +# destructors are listed first. If set to NO the constructors will appear in the +# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS. +# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief +# member documentation. +# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting +# detailed member documentation. +# The default value is: NO. + +SORT_MEMBERS_CTORS_1ST = NO + +# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy +# of group names into alphabetical order. If set to NO the group names will +# appear in their defined order. +# The default value is: NO. + +SORT_GROUP_NAMES = NO + +# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by +# fully-qualified names, including namespaces. If set to NO, the class list will +# be sorted only by class name, not including the namespace part. +# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. +# Note: This option applies only to the class list, not to the alphabetical +# list. +# The default value is: NO. + +SORT_BY_SCOPE_NAME = NO + +# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper +# type resolution of all parameters of a function it will reject a match between +# the prototype and the implementation of a member function even if there is +# only one candidate or it is obvious which candidate to choose by doing a +# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still +# accept a match between prototype and implementation in such cases. +# The default value is: NO. + +STRICT_PROTO_MATCHING = NO + +# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo +# list. This list is created by putting \todo commands in the documentation. +# The default value is: YES. + +GENERATE_TODOLIST = NO + +# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test +# list. This list is created by putting \test commands in the documentation. +# The default value is: YES. + +GENERATE_TESTLIST = NO + +# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug +# list. This list is created by putting \bug commands in the documentation. +# The default value is: YES. + +GENERATE_BUGLIST = NO + +# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO) +# the deprecated list. This list is created by putting \deprecated commands in +# the documentation. +# The default value is: YES. + +GENERATE_DEPRECATEDLIST= YES + +# The ENABLED_SECTIONS tag can be used to enable conditional documentation +# sections, marked by \if ... \endif and \cond +# ... \endcond blocks. + +ENABLED_SECTIONS = + +# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the +# initial value of a variable or macro / define can have for it to appear in the +# documentation. If the initializer consists of more lines than specified here +# it will be hidden. Use a value of 0 to hide initializers completely. The +# appearance of the value of individual variables and macros / defines can be +# controlled using \showinitializer or \hideinitializer command in the +# documentation regardless of this setting. +# Minimum value: 0, maximum value: 10000, default value: 30. + +MAX_INITIALIZER_LINES = 30 + +# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at +# the bottom of the documentation of classes and structs. If set to YES, the +# list will mention the files that were used to generate the documentation. +# The default value is: YES. + +SHOW_USED_FILES = YES + +# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This +# will remove the Files entry from the Quick Index and from the Folder Tree View +# (if specified). +# The default value is: YES. + +SHOW_FILES = YES + +# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces +# page. This will remove the Namespaces entry from the Quick Index and from the +# Folder Tree View (if specified). +# The default value is: YES. + +SHOW_NAMESPACES = NO + +# The FILE_VERSION_FILTER tag can be used to specify a program or script that +# doxygen should invoke to get the current version for each file (typically from +# the version control system). Doxygen will invoke the program by executing (via +# popen()) the command command input-file, where command is the value of the +# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided +# by doxygen. Whatever the program writes to standard output is used as the file +# version. For an example see the documentation. + +FILE_VERSION_FILTER = + +# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed +# by doxygen. The layout file controls the global structure of the generated +# output files in an output format independent way. To create the layout file +# that represents doxygen's defaults, run doxygen with the -l option. You can +# optionally specify a file name after the option, if omitted DoxygenLayout.xml +# will be used as the name of the layout file. +# +# Note that if you run doxygen from a directory containing a file called +# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE +# tag is left empty. + +LAYOUT_FILE = + +# The CITE_BIB_FILES tag can be used to specify one or more bib files containing +# the reference definitions. This must be a list of .bib files. The .bib +# extension is automatically appended if omitted. This requires the bibtex tool +# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info. +# For LaTeX the style of the bibliography can be controlled using +# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the +# search path. See also \cite for info how to create references. + +CITE_BIB_FILES = + +#--------------------------------------------------------------------------- +# Configuration options related to warning and progress messages +#--------------------------------------------------------------------------- + +# The QUIET tag can be used to turn on/off the messages that are generated to +# standard output by doxygen. If QUIET is set to YES this implies that the +# messages are off. +# The default value is: NO. + +QUIET = NO + +# The WARNINGS tag can be used to turn on/off the warning messages that are +# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES +# this implies that the warnings are on. +# +# Tip: Turn warnings on while writing the documentation. +# The default value is: YES. + +WARNINGS = YES + +# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate +# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag +# will automatically be disabled. +# The default value is: YES. + +WARN_IF_UNDOCUMENTED = NO + +# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for +# potential errors in the documentation, such as not documenting some parameters +# in a documented function, or documenting parameters that don't exist or using +# markup commands wrongly. +# The default value is: YES. + +WARN_IF_DOC_ERROR = YES + +# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that +# are documented, but have no documentation for their parameters or return +# value. If set to NO, doxygen will only warn about wrong or incomplete +# parameter documentation, but not about the absence of documentation. +# The default value is: NO. + +WARN_NO_PARAMDOC = NO + +# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when +# a warning is encountered. +# The default value is: NO. + +WARN_AS_ERROR = NO + +# The WARN_FORMAT tag determines the format of the warning messages that doxygen +# can produce. The string should contain the $file, $line, and $text tags, which +# will be replaced by the file and line number from which the warning originated +# and the warning text. Optionally the format may contain $version, which will +# be replaced by the version of the file (if it could be obtained via +# FILE_VERSION_FILTER) +# The default value is: $file:$line: $text. + +WARN_FORMAT = "$file:$line: $text" + +# The WARN_LOGFILE tag can be used to specify a file to which warning and error +# messages should be written. If left blank the output is written to standard +# error (stderr). + +WARN_LOGFILE = + +#--------------------------------------------------------------------------- +# Configuration options related to the input files +#--------------------------------------------------------------------------- + +# The INPUT tag is used to specify the files and/or directories that contain +# documented source files. You may enter file names like myfile.cpp or +# directories like /usr/src/myproject. Separate the files or directories with +# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING +# Note: If this tag is empty the current directory is searched. + +INPUT = ./src/ README.md + +# This tag can be used to specify the character encoding of the source files +# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses +# libiconv (or the iconv built into libc) for the transcoding. See the libiconv +# documentation (see: https://www.gnu.org/software/libiconv/) for the list of +# possible encodings. +# The default value is: UTF-8. + +INPUT_ENCODING = UTF-8 + +# If the value of the INPUT tag contains directories, you can use the +# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and +# *.h) to filter out the source-files in the directories. +# +# Note that for custom extensions or not directly supported extensions you also +# need to set EXTENSION_MAPPING for the extension otherwise the files are not +# read by doxygen. +# +# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp, +# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, +# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, +# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, +# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf and *.qsf. + +FILE_PATTERNS = *.cpp \ + *.c \ + *.h \ + *.hh \ + *.H + +# The RECURSIVE tag can be used to specify whether or not subdirectories should +# be searched for input files as well. +# The default value is: NO. + +RECURSIVE = YES + +# The EXCLUDE tag can be used to specify files and/or directories that should be +# excluded from the INPUT source files. This way you can easily exclude a +# subdirectory from a directory tree whose root is specified with the INPUT tag. +# +# Note that relative paths are relative to the directory from which doxygen is +# run. + +EXCLUDE = ./copasi/ABiochem \ + ./copasi/bindings \ + ./copasi/commercial \ + ./copasi/test \ + ./copasi/test2 \ + ./copasi/sbml/unittests/ \ + ./copasi/miase \ + ./copasi/compareExpressions \ + ./copasi/ViewCurrentUI/ \ + ./copasi/compareExpressions/unittests + + +# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or +# directories that are symbolic links (a Unix file system feature) are excluded +# from the input. +# The default value is: NO. + +EXCLUDE_SYMLINKS = YES + +# If the value of the INPUT tag contains directories, you can use the +# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude +# certain files from those directories. +# +# Note that the wildcards are matched against the file with absolute path, so to +# exclude all test directories for example use the pattern */test/* + +EXCLUDE_PATTERNS = + +# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names +# (namespaces, classes, functions, etc.) that should be excluded from the +# output. The symbol name can be a fully qualified name, a word, or if the +# wildcard * is used, a substring. Examples: ANamespace, AClass, +# AClass::ANamespace, ANamespace::*Test +# +# Note that the wildcards are matched against the file with absolute path, so to +# exclude all test directories use the pattern */test/* + +EXCLUDE_SYMBOLS = + +# The EXAMPLE_PATH tag can be used to specify one or more files or directories +# that contain example code fragments that are included (see the \include +# command). + +EXAMPLE_PATH = + +# If the value of the EXAMPLE_PATH tag contains directories, you can use the +# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and +# *.h) to filter out the source-files in the directories. If left blank all +# files are included. + +EXAMPLE_PATTERNS = + +# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be +# searched for input files to be used with the \include or \dontinclude commands +# irrespective of the value of the RECURSIVE tag. +# The default value is: NO. + +EXAMPLE_RECURSIVE = NO + +# The IMAGE_PATH tag can be used to specify one or more files or directories +# that contain images that are to be included in the documentation (see the +# \image command). + +IMAGE_PATH = + +# The INPUT_FILTER tag can be used to specify a program that doxygen should +# invoke to filter for each input file. Doxygen will invoke the filter program +# by executing (via popen()) the command: +# +# +# +# where is the value of the INPUT_FILTER tag, and is the +# name of an input file. Doxygen will then use the output that the filter +# program writes to standard output. If FILTER_PATTERNS is specified, this tag +# will be ignored. +# +# Note that the filter must not add or remove lines; it is applied before the +# code is scanned, but not when the output code is generated. If lines are added +# or removed, the anchors will not be placed correctly. +# +# Note that for custom extensions or not directly supported extensions you also +# need to set EXTENSION_MAPPING for the extension otherwise the files are not +# properly processed by doxygen. + +INPUT_FILTER = "grep -v '@dia:'" + +# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern +# basis. Doxygen will compare the file name with each pattern and apply the +# filter if there is a match. The filters are a list of the form: pattern=filter +# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how +# filters are used. If the FILTER_PATTERNS tag is empty or if none of the +# patterns match the file name, INPUT_FILTER is applied. +# +# Note that for custom extensions or not directly supported extensions you also +# need to set EXTENSION_MAPPING for the extension otherwise the files are not +# properly processed by doxygen. + +FILTER_PATTERNS = + +# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using +# INPUT_FILTER) will also be used to filter the input files that are used for +# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES). +# The default value is: NO. + +FILTER_SOURCE_FILES = NO + +# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file +# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and +# it is also possible to disable source filtering for a specific pattern using +# *.ext= (so without naming a filter). +# This tag requires that the tag FILTER_SOURCE_FILES is set to YES. + +FILTER_SOURCE_PATTERNS = + +# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that +# is part of the input, its contents will be placed on the main page +# (index.html). This can be useful if you have a project on for instance GitHub +# and want to reuse the introduction page also for the doxygen output. + +USE_MDFILE_AS_MAINPAGE = README.md + +#--------------------------------------------------------------------------- +# Configuration options related to source browsing +#--------------------------------------------------------------------------- + +# If the SOURCE_BROWSER tag is set to YES then a list of source files will be +# generated. Documented entities will be cross-referenced with these sources. +# +# Note: To get rid of all source code in the generated output, make sure that +# also VERBATIM_HEADERS is set to NO. +# The default value is: NO. + +SOURCE_BROWSER = NO + +# Setting the INLINE_SOURCES tag to YES will include the body of functions, +# classes and enums directly into the documentation. +# The default value is: NO. + +INLINE_SOURCES = NO + +# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any +# special comment blocks from generated source code fragments. Normal C, C++ and +# Fortran comments will always remain visible. +# The default value is: YES. + +STRIP_CODE_COMMENTS = NO + +# If the REFERENCED_BY_RELATION tag is set to YES then for each documented +# function all documented functions referencing it will be listed. +# The default value is: NO. + +REFERENCED_BY_RELATION = YES + +# If the REFERENCES_RELATION tag is set to YES then for each documented function +# all documented entities called/used by that function will be listed. +# The default value is: NO. + +REFERENCES_RELATION = YES + +# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set +# to YES then the hyperlinks from functions in REFERENCES_RELATION and +# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will +# link to the documentation. +# The default value is: YES. + +REFERENCES_LINK_SOURCE = NO + +# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the +# source code will show a tooltip with additional information such as prototype, +# brief description and links to the definition and documentation. Since this +# will make the HTML file larger and loading of large files a bit slower, you +# can opt to disable this feature. +# The default value is: YES. +# This tag requires that the tag SOURCE_BROWSER is set to YES. + +SOURCE_TOOLTIPS = YES + +# If the USE_HTAGS tag is set to YES then the references to source code will +# point to the HTML generated by the htags(1) tool instead of doxygen built-in +# source browser. The htags tool is part of GNU's global source tagging system +# (see https://www.gnu.org/software/global/global.html). You will need version +# 4.8.6 or higher. +# +# To use it do the following: +# - Install the latest version of global +# - Enable SOURCE_BROWSER and USE_HTAGS in the config file +# - Make sure the INPUT points to the root of the source tree +# - Run doxygen as normal +# +# Doxygen will invoke htags (and that will in turn invoke gtags), so these +# tools must be available from the command line (i.e. in the search path). +# +# The result: instead of the source browser generated by doxygen, the links to +# source code will now point to the output of htags. +# The default value is: NO. +# This tag requires that the tag SOURCE_BROWSER is set to YES. + +USE_HTAGS = NO + +# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a +# verbatim copy of the header file for each class for which an include is +# specified. Set to NO to disable this. +# See also: Section \class. +# The default value is: YES. + +VERBATIM_HEADERS = NO + +#--------------------------------------------------------------------------- +# Configuration options related to the alphabetical class index +#--------------------------------------------------------------------------- + +# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all +# compounds will be generated. Enable this if the project contains a lot of +# classes, structs, unions or interfaces. +# The default value is: YES. + +ALPHABETICAL_INDEX = YES + +# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in +# which the alphabetical index list will be split. +# Minimum value: 1, maximum value: 20, default value: 5. +# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. + +COLS_IN_ALPHA_INDEX = 3 + +# In case all classes in a project start with a common prefix, all classes will +# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag +# can be used to specify a prefix (or a list of prefixes) that should be ignored +# while generating the index headers. +# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. + +IGNORE_PREFIX = CCopasi \ + C \ + CQ + +#--------------------------------------------------------------------------- +# Configuration options related to the HTML output +#--------------------------------------------------------------------------- + +# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output +# The default value is: YES. + +GENERATE_HTML = YES + +# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a +# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of +# it. +# The default directory is: html. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_OUTPUT = html + +# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each +# generated HTML page (for example: .htm, .php, .asp). +# The default value is: .html. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_FILE_EXTENSION = .html + +# The HTML_HEADER tag can be used to specify a user-defined HTML header file for +# each generated HTML page. If the tag is left blank doxygen will generate a +# standard header. +# +# To get valid HTML the header file that includes any scripts and style sheets +# that doxygen needs, which is dependent on the configuration options used (e.g. +# the setting GENERATE_TREEVIEW). It is highly recommended to start with a +# default header using +# doxygen -w html new_header.html new_footer.html new_stylesheet.css +# YourConfigFile +# and then modify the file new_header.html. See also section "Doxygen usage" +# for information on how to generate the default header that doxygen normally +# uses. +# Note: The header is subject to change so you typically have to regenerate the +# default header when upgrading to a newer version of doxygen. For a description +# of the possible markers and block names see the documentation. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_HEADER = + +# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each +# generated HTML page. If the tag is left blank doxygen will generate a standard +# footer. See HTML_HEADER for more information on how to generate a default +# footer and what special commands can be used inside the footer. See also +# section "Doxygen usage" for information on how to generate the default footer +# that doxygen normally uses. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_FOOTER = + +# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style +# sheet that is used by each HTML page. It can be used to fine-tune the look of +# the HTML output. If left blank doxygen will generate a default style sheet. +# See also section "Doxygen usage" for information on how to generate the style +# sheet that doxygen normally uses. +# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as +# it is more robust and this tag (HTML_STYLESHEET) will in the future become +# obsolete. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_STYLESHEET = + +# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined +# cascading style sheets that are included after the standard style sheets +# created by doxygen. Using this option one can overrule certain style aspects. +# This is preferred over using HTML_STYLESHEET since it does not replace the +# standard style sheet and is therefore more robust against future updates. +# Doxygen will copy the style sheet files to the output directory. +# Note: The order of the extra style sheet files is of importance (e.g. the last +# style sheet in the list overrules the setting of the previous ones in the +# list). For an example see the documentation. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_EXTRA_STYLESHEET = + +# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or +# other source files which should be copied to the HTML output directory. Note +# that these files will be copied to the base HTML output directory. Use the +# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these +# files. In the HTML_STYLESHEET file, use the file name only. Also note that the +# files will be copied as-is; there are no commands or markers available. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_EXTRA_FILES = + +# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen +# will adjust the colors in the style sheet and background images according to +# this color. Hue is specified as an angle on a colorwheel, see +# https://en.wikipedia.org/wiki/Hue for more information. For instance the value +# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300 +# purple, and 360 is red again. +# Minimum value: 0, maximum value: 359, default value: 220. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_COLORSTYLE_HUE = 220 + +# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors +# in the HTML output. For a value of 0 the output will use grayscales only. A +# value of 255 will produce the most vivid colors. +# Minimum value: 0, maximum value: 255, default value: 100. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_COLORSTYLE_SAT = 100 + +# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the +# luminance component of the colors in the HTML output. Values below 100 +# gradually make the output lighter, whereas values above 100 make the output +# darker. The value divided by 100 is the actual gamma applied, so 80 represents +# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not +# change the gamma. +# Minimum value: 40, maximum value: 240, default value: 80. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_COLORSTYLE_GAMMA = 80 + +# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML +# page will contain the date and time when the page was generated. Setting this +# to YES can help to show when doxygen was last run and thus if the +# documentation is up to date. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_TIMESTAMP = YES + +# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML +# documentation will contain a main index with vertical navigation menus that +# are dynamically created via Javascript. If disabled, the navigation index will +# consists of multiple levels of tabs that are statically embedded in every HTML +# page. Disable this option to support browsers that do not have Javascript, +# like the Qt help browser. +# The default value is: YES. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_DYNAMIC_MENUS = YES + +# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML +# documentation will contain sections that can be hidden and shown after the +# page has loaded. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_DYNAMIC_SECTIONS = NO + +# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries +# shown in the various tree structured indices initially; the user can expand +# and collapse entries dynamically later on. Doxygen will expand the tree to +# such a level that at most the specified number of entries are visible (unless +# a fully collapsed tree already exceeds this amount). So setting the number of +# entries 1 will produce a full collapsed tree by default. 0 is a special value +# representing an infinite number of entries and will result in a full expanded +# tree by default. +# Minimum value: 0, maximum value: 9999, default value: 100. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_INDEX_NUM_ENTRIES = 100 + +# If the GENERATE_DOCSET tag is set to YES, additional index files will be +# generated that can be used as input for Apple's Xcode 3 integrated development +# environment (see: https://developer.apple.com/tools/xcode/), introduced with +# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a +# Makefile in the HTML output directory. Running make will produce the docset in +# that directory and running make install will install the docset in +# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at +# startup. See https://developer.apple.com/tools/creatingdocsetswithdoxygen.html +# for more information. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_DOCSET = NO + +# This tag determines the name of the docset feed. A documentation feed provides +# an umbrella under which multiple documentation sets from a single provider +# (such as a company or product suite) can be grouped. +# The default value is: Doxygen generated docs. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_FEEDNAME = "Doxygen generated docs" + +# This tag specifies a string that should uniquely identify the documentation +# set bundle. This should be a reverse domain-name style string, e.g. +# com.mycompany.MyDocSet. Doxygen will append .docset to the name. +# The default value is: org.doxygen.Project. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_BUNDLE_ID = org.doxygen.Project + +# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify +# the documentation publisher. This should be a reverse domain-name style +# string, e.g. com.mycompany.MyDocSet.documentation. +# The default value is: org.doxygen.Publisher. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_PUBLISHER_ID = org.doxygen.Publisher + +# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher. +# The default value is: Publisher. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_PUBLISHER_NAME = Publisher + +# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three +# additional HTML index files: index.hhp, index.hhc, and index.hhk. The +# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop +# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on +# Windows. +# +# The HTML Help Workshop contains a compiler that can convert all HTML output +# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML +# files are now used as the Windows 98 help format, and will replace the old +# Windows help format (.hlp) on all Windows platforms in the future. Compressed +# HTML files also contain an index, a table of contents, and you can search for +# words in the documentation. The HTML workshop also contains a viewer for +# compressed HTML files. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_HTMLHELP = NO + +# The CHM_FILE tag can be used to specify the file name of the resulting .chm +# file. You can add a path in front of the file if the result should not be +# written to the html output directory. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +CHM_FILE = + +# The HHC_LOCATION tag can be used to specify the location (absolute path +# including file name) of the HTML help compiler (hhc.exe). If non-empty, +# doxygen will try to run the HTML help compiler on the generated index.hhp. +# The file has to be specified with full path. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +HHC_LOCATION = + +# The GENERATE_CHI flag controls if a separate .chi index file is generated +# (YES) or that it should be included in the master .chm file (NO). +# The default value is: NO. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +GENERATE_CHI = NO + +# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc) +# and project file content. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +CHM_INDEX_ENCODING = + +# The BINARY_TOC flag controls whether a binary table of contents is generated +# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it +# enables the Previous and Next buttons. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +BINARY_TOC = NO + +# The TOC_EXPAND flag can be set to YES to add extra items for group members to +# the table of contents of the HTML help documentation and to the tree view. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +TOC_EXPAND = NO + +# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and +# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that +# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help +# (.qch) of the generated HTML documentation. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_QHP = NO + +# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify +# the file name of the resulting .qch file. The path specified is relative to +# the HTML output folder. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QCH_FILE = + +# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help +# Project output. For more information please see Qt Help Project / Namespace +# (see: http://doc.qt.io/qt-4.8/qthelpproject.html#namespace). +# The default value is: org.doxygen.Project. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_NAMESPACE = org.doxygen.Project + +# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt +# Help Project output. For more information please see Qt Help Project / Virtual +# Folders (see: http://doc.qt.io/qt-4.8/qthelpproject.html#virtual-folders). +# The default value is: doc. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_VIRTUAL_FOLDER = doc + +# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom +# filter to add. For more information please see Qt Help Project / Custom +# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters). +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_CUST_FILTER_NAME = + +# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the +# custom filter to add. For more information please see Qt Help Project / Custom +# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters). +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_CUST_FILTER_ATTRS = + +# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this +# project's filter section matches. Qt Help Project / Filter Attributes (see: +# http://doc.qt.io/qt-4.8/qthelpproject.html#filter-attributes). +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_SECT_FILTER_ATTRS = + +# The QHG_LOCATION tag can be used to specify the location of Qt's +# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the +# generated .qhp file. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHG_LOCATION = + +# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be +# generated, together with the HTML files, they form an Eclipse help plugin. To +# install this plugin and make it available under the help contents menu in +# Eclipse, the contents of the directory containing the HTML and XML files needs +# to be copied into the plugins directory of eclipse. The name of the directory +# within the plugins directory should be the same as the ECLIPSE_DOC_ID value. +# After copying Eclipse needs to be restarted before the help appears. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_ECLIPSEHELP = NO + +# A unique identifier for the Eclipse help plugin. When installing the plugin +# the directory name containing the HTML and XML files should also have this +# name. Each documentation set should have its own identifier. +# The default value is: org.doxygen.Project. +# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES. + +ECLIPSE_DOC_ID = org.doxygen.Project + +# If you want full control over the layout of the generated HTML pages it might +# be necessary to disable the index and replace it with your own. The +# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top +# of each HTML page. A value of NO enables the index and the value YES disables +# it. Since the tabs in the index contain the same information as the navigation +# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +DISABLE_INDEX = NO + +# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index +# structure should be generated to display hierarchical information. If the tag +# value is set to YES, a side panel will be generated containing a tree-like +# index structure (just like the one that is generated for HTML Help). For this +# to work a browser that supports JavaScript, DHTML, CSS and frames is required +# (i.e. any modern browser). Windows users are probably better off using the +# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can +# further fine-tune the look of the index. As an example, the default style +# sheet generated by doxygen has an example that shows how to put an image at +# the root of the tree instead of the PROJECT_NAME. Since the tree basically has +# the same information as the tab index, you could consider setting +# DISABLE_INDEX to YES when enabling this option. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_TREEVIEW = YES + +# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that +# doxygen will group on one line in the generated HTML documentation. +# +# Note that a value of 0 will completely suppress the enum values from appearing +# in the overview section. +# Minimum value: 0, maximum value: 20, default value: 4. +# This tag requires that the tag GENERATE_HTML is set to YES. + +ENUM_VALUES_PER_LINE = 4 + +# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used +# to set the initial width (in pixels) of the frame in which the tree is shown. +# Minimum value: 0, maximum value: 1500, default value: 250. +# This tag requires that the tag GENERATE_HTML is set to YES. + +TREEVIEW_WIDTH = 250 + +# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to +# external symbols imported via tag files in a separate window. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +EXT_LINKS_IN_WINDOW = NO + +# Use this tag to change the font size of LaTeX formulas included as images in +# the HTML documentation. When you change the font size after a successful +# doxygen run you need to manually remove any form_*.png images from the HTML +# output directory to force them to be regenerated. +# Minimum value: 8, maximum value: 50, default value: 10. +# This tag requires that the tag GENERATE_HTML is set to YES. + +FORMULA_FONTSIZE = 10 + +# Use the FORMULA_TRANSPARENT tag to determine whether or not the images +# generated for formulas are transparent PNGs. Transparent PNGs are not +# supported properly for IE 6.0, but are supported on all modern browsers. +# +# Note that when changing this option you need to delete any form_*.png files in +# the HTML output directory before the changes have effect. +# The default value is: YES. +# This tag requires that the tag GENERATE_HTML is set to YES. + +FORMULA_TRANSPARENT = YES + +# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see +# https://www.mathjax.org) which uses client side Javascript for the rendering +# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX +# installed or if you want to formulas look prettier in the HTML output. When +# enabled you may also need to install MathJax separately and configure the path +# to it using the MATHJAX_RELPATH option. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +USE_MATHJAX = NO + +# When MathJax is enabled you can set the default output format to be used for +# the MathJax output. See the MathJax site (see: +# http://docs.mathjax.org/en/latest/output.html) for more details. +# Possible values are: HTML-CSS (which is slower, but has the best +# compatibility), NativeMML (i.e. MathML) and SVG. +# The default value is: HTML-CSS. +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_FORMAT = HTML-CSS + +# When MathJax is enabled you need to specify the location relative to the HTML +# output directory using the MATHJAX_RELPATH option. The destination directory +# should contain the MathJax.js script. For instance, if the mathjax directory +# is located at the same level as the HTML output directory, then +# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax +# Content Delivery Network so you can quickly see the result without installing +# MathJax. However, it is strongly recommended to install a local copy of +# MathJax from https://www.mathjax.org before deployment. +# The default value is: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/. +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_RELPATH = https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/ + +# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax +# extension names that should be enabled during MathJax rendering. For example +# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_EXTENSIONS = + +# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces +# of code that will be used on startup of the MathJax code. See the MathJax site +# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an +# example see the documentation. +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_CODEFILE = + +# When the SEARCHENGINE tag is enabled doxygen will generate a search box for +# the HTML output. The underlying search engine uses javascript and DHTML and +# should work on any modern browser. Note that when using HTML help +# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET) +# there is already a search function so this one should typically be disabled. +# For large projects the javascript based search engine can be slow, then +# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to +# search using the keyboard; to jump to the search box use + S +# (what the is depends on the OS and browser, but it is typically +# , /