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Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Nature Protocols volume 6, pages 1290–1307 (2011)Cite this article

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Abstract

Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) 13C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language–formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.

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Figure 1: The philosophy of COBRA.
Figure 2: Overview of the COBRA Toolbox.
Figure 3: Flux balance analysis of E. coli core model.
Figure 4: Flux variability analysis of E. coli.
Figure 5: Sampling histogram of glycolysis, using the E. coli core model under aerobic and anaerobic glucose minimal medium conditions.

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Acknowledgements

We thank individuals who have contributed to or tested COBRA 2.0: S. Gudmundsson, T. Conrad, N. Jamshidi, R. Notebaart and J. Feala. Funding was provided in part by the National Institute of Allergy and Infectious Diseases NIH/DHHS through interagency agreement Y1-AI-8401-01, NIH grants GM68837-05A1, DE-PS02-08ER08-01, GM057089-12 and GM057089-11S1, and the CalIT2 Summer scholars program. R.F. and I.T. were funded by 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 DE-SC0002009. N.L. was funded through a National Science Foundation Integrative Graduate Education and Research Traineeship (IGERT) Program Plant Systems Biology training grant (# DGE-0504645).

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Authors and Affiliations

  1. Bioinformatics Program, University of California San Diego, La Jolla, California, USA

    Jan Schellenberger

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

    Richard Que, Jeffrey D Orth, Adam M Feist, Daniel C Zielinski, Aarash Bordbar, Nathan E Lewis, Sorena Rahmanian, Joseph Kang, Daniel R Hyduke & Bernhard Ø Palsson

  3. Science Institute & Center for Systems Biology, University of Iceland, Reykjavik, Iceland

    Ronan M T Fleming

  4. Faculty of Industrial Engineering, Mechanical Engineering & Computer Science & Center for Systems Biology, University of Iceland, Reykjavik, Iceland

    Ines Thiele

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Contributions

J.S., R.Q., R.M.T.F., I.T., J.D.O., A.M.F., D.C.Z., A.B., N.E.L., S.R., J.K. and D.R.H. contributed modules to the COBRA Toolbox v2.0. D.R.H., J.S., R.Q., A.B., J.D.O., N.E.L. and B.Ø.P. wrote the manuscript.

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Correspondence to Daniel R Hyduke or Bernhard Ø Palsson.

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Bernhard Ø. Palsson serves on the scientific advisory board of Genomatica.

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Schellenberger, J., Que, R., Fleming, R. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6, 1290–1307 (2011). https://doi.org/10.1038/nprot.2011.308

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