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

Nature Protocols volume 14, pages 639–702 (2019)Cite this article

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Abstract

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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Fig. 1: Overview of key constraint-based reconstruction and analysis concepts.
Fig. 2: Continuous integration of newly developed code is performed on a dedicated server running Jenkins.
Fig. 3: Unsteady-state flux balance analysis.
Fig. 4: Solution spaces from steady-state fluxes are anisotropic, that is, long in some directions and short in others.
Fig. 5: In the OptForce procedure, the MUST sets are determined by contrasting the flux ranges obtained using flux variability analysis (FVA) of a wild-type (blue bars) and an overproducing strain (red bars).
Fig. 6: Development branching model of the COBRA Toolbox.
Fig. 7
Fig. 8: An energy-generating stoichiometrically balanced cycle.
Fig. 9: The interventions predicted by the OptForce method for succinate overproduction in E. coli (AntCore model) under aerobic conditions.
Fig. 10: Qualitatively forward, quantitatively reverse reactions in a multi-compartmental, genome-scale model.
Fig. 11: Human metabolic network visualization.
Fig. 12: Selective scope visualization of the E. coli core model by Paint4Net.

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Acknowledgements

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).

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Author notes

  1. These authors contributed equally: Laurent Heirendt, Sylvain Arreckx.

Authors and Affiliations

  1. Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg

    Laurent Heirendt, Sylvain Arreckx, Almut Heinken, Hulda S. Haraldsdóttir, Jacek Wachowiak, Vanja Vlasov, Stefania Magnusdóttir, German Preciat, Alise Žagare, Maike K. Aurich, Catherine M. Clancy, Jennifer Modamio, Alberto Noronha, Diana C. El Assal, Susan Ghaderi, Masoud Ahookhosh, Marouen Ben Guebila, Hoai M. Le, Miguel A. P. Oliveira, Phan T. Vuong, Lemmer P. El Assal, Ines Thiele & Ronan M. T. Fleming

  2. Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg

    Thomas Pfau & Thomas Sauter

  3. Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile

    Sebastián N. Mendoza & Alejandro Maass

  4. Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile

    Sebastián N. Mendoza & Alejandro Maass

  5. Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA

    Anne Richelle & Nathan E. Lewis

  6. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK

    Sarah M. Keating

  7. Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA

    Chiam Yu Ng, Siu H. J. Chan, Lin Wang & Costas D. Maranas

  8. Department of Physics, and Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA

    John T. Sauls

  9. Sinopia Biosciences, San Diego, CA, USA

    Aarash Bordbar

  10. Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA

    Benjamin Cousins & Santosh Vempala

  11. Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain

    Luis V. Valcarcel, Iñigo Apaolaza & Francisco J. Planes

  12. Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia

    Andrejs Kostromins & Egils Stalidzans

  13. Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France

    Nicolas Sompairac, Inna Kuperstein & Andrei Zinovyev

  14. Department of Management Science and Engineering, Stanford University, Stanford, CA, USA

    Ding Ma & Michael A. Saunders

  15. Department of Statistics, University of Michigan, Ann Arbor, MI, USA

    Yuekai Sun

  16. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    James T. Yurkovich & Bernhard Ø. Palsson

  17. Utah State University Research Foundation, North Logan, UT, USA

    H. Scott Hinton

  18. Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, UK

    William A. Bryant

  19. Department of Mathematics, University of Alicante, Alicante, Spain

    Francisco J. Aragón Artacho

  20. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Michael Hucka

  21. Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA

    Nathan E. Lewis

  22. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark

    Bernhard Ø. Palsson

  23. Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands

    Ronan M. T. Fleming

Authors
  1. Laurent Heirendt
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  2. Sylvain Arreckx
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Contributions

S.A.: continuous integration, code review, opencobra.github.io/cobratoolbox, Jenkins, Documenter.py, changeCobraSolver, pull request support, tutorials, tests, coordination, manuscript, and initCobraToolbox. L.H.: continuous integration, code review, fastFVA (new version, test, and integration), MATLAB.devTools, opencobra.github.io, 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.

Corresponding author

Correspondence to Ronan M. T. Fleming.

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Related links

Key references using this protocol

Schellenberger, J. et al. Nat. Protocols 6, 1290–1307 (2011): https://www.nature.com/articles/nprot.2011.308

Becker, S. A. et al. Nat. Protocols 2, 727–738 (2007): https://www.nature.com/articles/nprot.2007.99

This protocol is an update to Nat. Protoc. 2, 727–738 (2007): https://doi.org/10.1038/nprot.2007.99 and Nat. Protoc. 6, 1290–1307 (2011): https://doi.org/10.1038/protex.2011.234

Supplementary information

Supplementary Data 1

Supplementary Manual 1

MATLAB basics

Supplementary Manual 2

Shell or Terminal basics

Supplementary Manual 3

Contributing to the COBRA Toolbox using git

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Heirendt, L., Arreckx, S., Pfau, T. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 14, 639–702 (2019). https://doi.org/10.1038/s41596-018-0098-2

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