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Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Heirendt, Laurent and Hucka, Michael (2019) Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nature Protocols, 14 (3). pp. 639-702. ISSN 1754-2189.

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Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

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Hucka, Michael0000-0001-9105-5960
Additional Information:© 2019 Springer Nature Publishing AG. Published 20 February 2019. The Reproducible Research Results (R3) team, in particular, C. Trefois and Y. Jarosz, of the Luxembourg Centre for Systems Biomedicine, is acknowledged for their help in setting up the virtual machine and the Jenkins server. This study was funded by the National Centre of Excellence in Research (NCER) on Parkinson’s disease, the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant no. DE-SC0010429. This project also received funding from the European Union’s HORIZON 2020 Research and Innovation Programme under grant agreement no. 668738 and the Luxembourg National Research Fund (FNR) ATTRACT program (FNR/A12/01) and OPEN (FNR/O16/11402054) grants. N.E.L. was supported by NIGMS (R35 GM119850) and the Novo Nordisk Foundation (NNF10CC1016517). M.A.P.O. was supported by the Luxembourg National Research Fund (FNR) grant AFR/6669348. A.R. was supported by the Lilly Innovation Fellows Award. F.J.P. was supported by the Minister of Economy and Competitiveness of Spain (BIO2016-77998-R) and the ELKARTEK Programme of the Basque Government (KK-2016/00026). I.A. was supported by a Basque Government predoctoral grant (PRE_2016_2_0044). B.Ø.P. was supported by the Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517). Author Contributions: S.A.: continuous integration, code review,, Jenkins,, changeCobraSolver, pull request support, tutorials, tests, coordination, manuscript, and initCobraToolbox. L.H.: continuous integration, code review, fastFVA (new version, test, and integration), MATLAB.devTools,, tutorials, tests, pull request support, coordination, manuscript, initCobraToolbox, and forum support. T.P.: input–output and transcriptomic integration, tutorials, tutorial reviews, input–output and transcriptomic integration sections of manuscript, forum support, pull request support, and code review. S.N.M.: development and update of strain design algorithms, GAMS and MATLAB integration, and tutorials. A.R.: transcriptomic data integration methods, tutorials, transcriptomic integration section of manuscript, RuMBA, pFBA, metabolic tasks, and tutorial review. A.H.: multispecies modeling code contribution, tutorial review, and testing. H.S. Haraldsdóttir: thermodynamics, conserved moiety, and sampling methods. J.W.: documentation. S.M.K.: SBML input–output support. V.V.: tutorials. S.M.: multispecies modeling, tutorial review, and testing. C.Y.N.: strain design code review, tutorial review, and manuscript (OptForce/biotech introduction). G.P.: tutorials and chemoinformatics for metabolite structures and atom mapping data. A.Ž.: metabolic cartography. S.H.J.C.: solution navigation, multispecies modeling code, and tutorial review. M.K.A.: metabolomic data integration. C.M.C.: tutorials and testing. J.M.: metabolic cartography and human metabolic network visualization tutorials. J.T.S.: modelBorgifier code and tutorial. A.N.: virtual metabolic human interoperability. A.B.: MinSpan method and tutorial, supervision on uFBA method and tutorial. B.C.: CHRR uniform sampling. D.C.E.A.: tutorials. L.V.V.: tutorials and genetic MCS implementation. I.A.: tutorials and genetic MCS implementation. S.G.: interoperability with CellNetAnalyzer. M.A.: adaptive Levenberg–Marquardt solver. M.B.G.: tutorial reviews. A.K.: Paint4Net code and tutorial. N.S.: development of metabolomic cartography tool and tutorial. H.M.L.: cardinality optimization solver. D.M.: quadruple-precision solvers. Y.S.: multiscale FBA reformulation. L.W.: strain design code review, tutorial review, and manuscript (OptForce). J.T.Y.: uFBA method and tutorial. M.A.P.O.: tutorial. P.T.V.: adaptive Levenberg–Marquardt solvers and boosted difference of convex optimization solver. L.P.E.A.: chemoinformatic data integration and documentation. I.K.: development of metabolomic cartography tool and tutorial. A.Z.: development of metabolomic cartography tool and tutorial. H.S. Hinton: E. coli core tutorials. W.A.B.: code refinement. F.J.A.A.: duplomonotone equation solver, boosted difference of convex optimization solver, and adaptive Levenberg–Marquardt solvers. F.J.P.: academic supervision, tutorials, and genetic MCS implementation. E.S.: academic supervision, Paint4Net, and tutorial. A.M.: academic supervision. S.V.: academic supervision and CHRR uniform sampling algorithm. M.H.: academic supervision and SBML input–output support. M.A.S.: academic supervision, quadruple-precision solvers, nullspace computation, and convex optimization. C.D.M.: academic supervision and strain design algorithms. N.E.L.: academic supervision and coding, and transcriptomic data integration, RuMBA, pFBA, metabolic tasks, and tutorial review. T.S.: academic supervision and FASTCORE algorithm. B.Ø.P.: academic supervision and openCOBRA stewardship. I.T.: academic supervision, tutorials, code contribution, and manuscript. R.M.T.F.: conceptualization, lead developer, academic supervision, software architecture, code review, sparse optimization, nullspace computation, thermodynamics, variational kinetics, fastGapFill, sampling, conserved moieties, network visualization, forum support, tutorials, and manuscript. The authors declare no competing interests.
Funding AgencyGrant Number
National Centre of Excellence in Research (NCER) on Parkinson’s diseaseUNSPECIFIED
Department of Energy (DOE)DE-SC0010429
European Research Council (ERC)668738
Luxembourg National Research Fund (FNR)FNR/A12/01
Luxembourg National Research Fund (FNR)FNR/O16/11402054
NIHR35 GM119850
Novo Nordisk FoundationNNF10CC1016517
Luxembourg National Research Fund (FNR)AFR/6669348
Lilly Innovation Fellows AwardUNSPECIFIED
Ministerio de Economía, Industria y Competitividad (MINECO)BIO2016-77998-R
Basque GovernmentKK-2016/00026
Basque GovernmentPRE_2016_2_0044
Novo Nordisk FoundationNNF10CC1016517
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Record Number:CaltechAUTHORS:20190225-082428626
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93197
Deposited By: Tony Diaz
Deposited On:25 Feb 2019 16:38
Last Modified:03 Oct 2019 20:51

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