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MathWorks Inc openflux
Workflow of <t>OpenFLUX</t> . The software consists of 2 components (shaded box) – a JAVA-based metabolic model parser and a set of MATLAB-based algorithms for parameter estimation and sensitivity analysis. The user defines the reaction network in the model definition file, which contains the metabolic model and the experimental data input. The parser reads the metabolic model, and subsequently generates a metabolite balance model (stoichiometric matrix), and an isotopomer balance model using the EMU framework. The MATLAB-based computational algorithms use these models, in conjunction with the experimental data, to perform least-square parameter estimation and parameter sensitivity analysis. The optimized flux distributions and confidence intervals are obtained as results output.
Openflux, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Article Title: OpenFLUX: efficient modelling software for 13 C-based metabolic flux analysis

Journal: Microbial Cell Factories

doi: 10.1186/1475-2859-8-25

Workflow of OpenFLUX . The software consists of 2 components (shaded box) – a JAVA-based metabolic model parser and a set of MATLAB-based algorithms for parameter estimation and sensitivity analysis. The user defines the reaction network in the model definition file, which contains the metabolic model and the experimental data input. The parser reads the metabolic model, and subsequently generates a metabolite balance model (stoichiometric matrix), and an isotopomer balance model using the EMU framework. The MATLAB-based computational algorithms use these models, in conjunction with the experimental data, to perform least-square parameter estimation and parameter sensitivity analysis. The optimized flux distributions and confidence intervals are obtained as results output.
Figure Legend Snippet: Workflow of OpenFLUX . The software consists of 2 components (shaded box) – a JAVA-based metabolic model parser and a set of MATLAB-based algorithms for parameter estimation and sensitivity analysis. The user defines the reaction network in the model definition file, which contains the metabolic model and the experimental data input. The parser reads the metabolic model, and subsequently generates a metabolite balance model (stoichiometric matrix), and an isotopomer balance model using the EMU framework. The MATLAB-based computational algorithms use these models, in conjunction with the experimental data, to perform least-square parameter estimation and parameter sensitivity analysis. The optimized flux distributions and confidence intervals are obtained as results output.

Techniques Used: Software

Algorithm for weighted least-square parameter estimation . OpenFLUX uses MATLAB's FMINCON to minimize the residual error ( ) between the experimental MIDs ( ) and the simulated MIDs ( ), by optimizing the free fluxes ( ), which are subjected to lower and upper boundary value constraints. is weighted by the variance matrix ( D ). The inverse compactification function is used to transform reverse flux from a numerical parameter ( v ←, [0,1] ) into a physical flux parameter ( v ← ). The null-space matrix ( Ns ) is then used to calculate the flux vector ( ) from the free fluxes ( ). Finally, is calculated using and known MIDs of the input substrates ( ). FMINCON terminates when a local minimum is found, revealing the optimum free fluxes.
Figure Legend Snippet: Algorithm for weighted least-square parameter estimation . OpenFLUX uses MATLAB's FMINCON to minimize the residual error ( ) between the experimental MIDs ( ) and the simulated MIDs ( ), by optimizing the free fluxes ( ), which are subjected to lower and upper boundary value constraints. is weighted by the variance matrix ( D ). The inverse compactification function is used to transform reverse flux from a numerical parameter ( v ←, [0,1] ) into a physical flux parameter ( v ← ). The null-space matrix ( Ns ) is then used to calculate the flux vector ( ) from the free fluxes ( ). Finally, is calculated using and known MIDs of the input substrates ( ). FMINCON terminates when a local minimum is found, revealing the optimum free fluxes.

Techniques Used: Plasmid Preparation

Flux distributions for a simplified TCA cycle model as described in the text and Appendix . Fluxes were calculated using OpenFLUX (top values) and the cumomer package 13C-FLUX (bottom values). The absolute forward fluxes are displayed in the circles. The free fluxes (R2, R8, R9) and their 95% CI (generated by OpenFLUX) are displayed in the rectangular boxes. Full arrowhead: forward flux. Line arrowhead: reverse flux. All reactions are unidirectional except for the SUC↔OAA reaction. Suffix: _B, biomass drain; _EX, exo-metabolites. Metabolites: PYR, pyruvate; ACCOA, acetyl-CoA; CIT, citrate/isocitrate; AKG, α-ketoglutarate; SUC, succinate; OAA, malate/oxaloacetate; GLU, glutamate. Reactions: R1, pyruvate uptake; R2, glutamate take; R3, pyruvate dehydrogenase; R4, citrate synthase; R5, iso-citrate dehydrogase; R6, α-ketoglutarate dehydrogenase & succinyl-CoA hydrolase; R7/R8; fumarate hydratase & malate dehydrogenase; R9, malic enzyme; R10, pyruvate carboxylase; R11/R12, oxidative phosphorylation; R13, oxygen uptake; R14, CO2 evolution; R15, ATP maintenance; R16, pyruvate biomass drain; R17, α-ketoglutarate biomass drain; R18, oxaloacetate biomass drain.
Figure Legend Snippet: Flux distributions for a simplified TCA cycle model as described in the text and Appendix . Fluxes were calculated using OpenFLUX (top values) and the cumomer package 13C-FLUX (bottom values). The absolute forward fluxes are displayed in the circles. The free fluxes (R2, R8, R9) and their 95% CI (generated by OpenFLUX) are displayed in the rectangular boxes. Full arrowhead: forward flux. Line arrowhead: reverse flux. All reactions are unidirectional except for the SUC↔OAA reaction. Suffix: _B, biomass drain; _EX, exo-metabolites. Metabolites: PYR, pyruvate; ACCOA, acetyl-CoA; CIT, citrate/isocitrate; AKG, α-ketoglutarate; SUC, succinate; OAA, malate/oxaloacetate; GLU, glutamate. Reactions: R1, pyruvate uptake; R2, glutamate take; R3, pyruvate dehydrogenase; R4, citrate synthase; R5, iso-citrate dehydrogase; R6, α-ketoglutarate dehydrogenase & succinyl-CoA hydrolase; R7/R8; fumarate hydratase & malate dehydrogenase; R9, malic enzyme; R10, pyruvate carboxylase; R11/R12, oxidative phosphorylation; R13, oxygen uptake; R14, CO2 evolution; R15, ATP maintenance; R16, pyruvate biomass drain; R17, α-ketoglutarate biomass drain; R18, oxaloacetate biomass drain.

Techniques Used: Generated



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Workflow of OpenFLUX . The software consists of 2 components (shaded box) – a JAVA-based metabolic model parser and a set of MATLAB-based algorithms for parameter estimation and sensitivity analysis. The user defines the reaction network in the model definition file, which contains the metabolic model and the experimental data input. The parser reads the metabolic model, and subsequently generates a metabolite balance model (stoichiometric matrix), and an isotopomer balance model using the EMU framework. The MATLAB-based computational algorithms use these models, in conjunction with the experimental data, to perform least-square parameter estimation and parameter sensitivity analysis. The optimized flux distributions and confidence intervals are obtained as results output.

Journal: Microbial Cell Factories

Article Title: OpenFLUX: efficient modelling software for 13 C-based metabolic flux analysis

doi: 10.1186/1475-2859-8-25

Figure Lengend Snippet: Workflow of OpenFLUX . The software consists of 2 components (shaded box) – a JAVA-based metabolic model parser and a set of MATLAB-based algorithms for parameter estimation and sensitivity analysis. The user defines the reaction network in the model definition file, which contains the metabolic model and the experimental data input. The parser reads the metabolic model, and subsequently generates a metabolite balance model (stoichiometric matrix), and an isotopomer balance model using the EMU framework. The MATLAB-based computational algorithms use these models, in conjunction with the experimental data, to perform least-square parameter estimation and parameter sensitivity analysis. The optimized flux distributions and confidence intervals are obtained as results output.

Article Snippet: OpenFLUX is optimized for MATLAB 7.3 (R2006b) (The MathWorks, Natick, MA, USA) and Java 6 (Sun Microsystems, Santa Clara, CA, USA) on a Microsoft Windows XP platform.

Techniques: Software

Algorithm for weighted least-square parameter estimation . OpenFLUX uses MATLAB's FMINCON to minimize the residual error ( ) between the experimental MIDs ( ) and the simulated MIDs ( ), by optimizing the free fluxes ( ), which are subjected to lower and upper boundary value constraints. is weighted by the variance matrix ( D ). The inverse compactification function is used to transform reverse flux from a numerical parameter ( v ←, [0,1] ) into a physical flux parameter ( v ← ). The null-space matrix ( Ns ) is then used to calculate the flux vector ( ) from the free fluxes ( ). Finally, is calculated using and known MIDs of the input substrates ( ). FMINCON terminates when a local minimum is found, revealing the optimum free fluxes.

Journal: Microbial Cell Factories

Article Title: OpenFLUX: efficient modelling software for 13 C-based metabolic flux analysis

doi: 10.1186/1475-2859-8-25

Figure Lengend Snippet: Algorithm for weighted least-square parameter estimation . OpenFLUX uses MATLAB's FMINCON to minimize the residual error ( ) between the experimental MIDs ( ) and the simulated MIDs ( ), by optimizing the free fluxes ( ), which are subjected to lower and upper boundary value constraints. is weighted by the variance matrix ( D ). The inverse compactification function is used to transform reverse flux from a numerical parameter ( v ←, [0,1] ) into a physical flux parameter ( v ← ). The null-space matrix ( Ns ) is then used to calculate the flux vector ( ) from the free fluxes ( ). Finally, is calculated using and known MIDs of the input substrates ( ). FMINCON terminates when a local minimum is found, revealing the optimum free fluxes.

Article Snippet: OpenFLUX is optimized for MATLAB 7.3 (R2006b) (The MathWorks, Natick, MA, USA) and Java 6 (Sun Microsystems, Santa Clara, CA, USA) on a Microsoft Windows XP platform.

Techniques: Plasmid Preparation

Flux distributions for a simplified TCA cycle model as described in the text and Appendix . Fluxes were calculated using OpenFLUX (top values) and the cumomer package 13C-FLUX (bottom values). The absolute forward fluxes are displayed in the circles. The free fluxes (R2, R8, R9) and their 95% CI (generated by OpenFLUX) are displayed in the rectangular boxes. Full arrowhead: forward flux. Line arrowhead: reverse flux. All reactions are unidirectional except for the SUC↔OAA reaction. Suffix: _B, biomass drain; _EX, exo-metabolites. Metabolites: PYR, pyruvate; ACCOA, acetyl-CoA; CIT, citrate/isocitrate; AKG, α-ketoglutarate; SUC, succinate; OAA, malate/oxaloacetate; GLU, glutamate. Reactions: R1, pyruvate uptake; R2, glutamate take; R3, pyruvate dehydrogenase; R4, citrate synthase; R5, iso-citrate dehydrogase; R6, α-ketoglutarate dehydrogenase & succinyl-CoA hydrolase; R7/R8; fumarate hydratase & malate dehydrogenase; R9, malic enzyme; R10, pyruvate carboxylase; R11/R12, oxidative phosphorylation; R13, oxygen uptake; R14, CO2 evolution; R15, ATP maintenance; R16, pyruvate biomass drain; R17, α-ketoglutarate biomass drain; R18, oxaloacetate biomass drain.

Journal: Microbial Cell Factories

Article Title: OpenFLUX: efficient modelling software for 13 C-based metabolic flux analysis

doi: 10.1186/1475-2859-8-25

Figure Lengend Snippet: Flux distributions for a simplified TCA cycle model as described in the text and Appendix . Fluxes were calculated using OpenFLUX (top values) and the cumomer package 13C-FLUX (bottom values). The absolute forward fluxes are displayed in the circles. The free fluxes (R2, R8, R9) and their 95% CI (generated by OpenFLUX) are displayed in the rectangular boxes. Full arrowhead: forward flux. Line arrowhead: reverse flux. All reactions are unidirectional except for the SUC↔OAA reaction. Suffix: _B, biomass drain; _EX, exo-metabolites. Metabolites: PYR, pyruvate; ACCOA, acetyl-CoA; CIT, citrate/isocitrate; AKG, α-ketoglutarate; SUC, succinate; OAA, malate/oxaloacetate; GLU, glutamate. Reactions: R1, pyruvate uptake; R2, glutamate take; R3, pyruvate dehydrogenase; R4, citrate synthase; R5, iso-citrate dehydrogase; R6, α-ketoglutarate dehydrogenase & succinyl-CoA hydrolase; R7/R8; fumarate hydratase & malate dehydrogenase; R9, malic enzyme; R10, pyruvate carboxylase; R11/R12, oxidative phosphorylation; R13, oxygen uptake; R14, CO2 evolution; R15, ATP maintenance; R16, pyruvate biomass drain; R17, α-ketoglutarate biomass drain; R18, oxaloacetate biomass drain.

Article Snippet: OpenFLUX is optimized for MATLAB 7.3 (R2006b) (The MathWorks, Natick, MA, USA) and Java 6 (Sun Microsystems, Santa Clara, CA, USA) on a Microsoft Windows XP platform.

Techniques: Generated

Comparison of iMS2Flux and other available MS correction tools

Journal: BMC Bioinformatics

Article Title: iMS2Flux – a high–throughput processing tool for stable isotope labeled mass spectrometric data used for metabolic flux analysis

doi: 10.1186/1471-2105-13-295

Figure Lengend Snippet: Comparison of iMS2Flux and other available MS correction tools

Article Snippet: Similar to MSCorr , the OpenFLUX correction is provided as a function in MATLAB (corrMatGen) which requires the user to enter the chemical formula and other specifics about each compound individually.

Techniques: Labeling, Software