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


The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

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Figure 1: COBRA.
Figure 2: Stoichiometric representation of metabolic networks.
Figure 3: The workflow for using the COBRA Toolbox.
Figure 4: Robustness analysis.
Figure 5: Dynamic FBA.
Figure 6: Single and double deletion predictions using S. cerevisiae iND750.
Figure 7: FVA.
Figure 8: Flux sampling of E. coli.
Figure 9: Correlated reaction sets in yeast.


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We acknowledge Nathan Price, Vasiliy Portnoy, Jan Schellenberger and Christian Barrett for help with the COBRA Toolbox development and testing. Support for this work was provided by the National Institutes of Health (RO1 GM071808, 2R01 GM062791-04A2) and National Science Foundation (BES-0331342).

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Correspondence to Markus J Herrgard.

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Competing interests

These studies were supported by grants from the NIH/NSF (RO1 GM071808, 2R01 GM062791-04A2, BES-0331342). The investigator(s) have a financial interest in Genomatica, Inc. Although this grant has been identified for conflict of interest management based on the overall scope of the project and its potential to benefit Genomatica, Inc, the research findings included in this publication may not necessarily directly relate to the interests of Genomatica, Inc.

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Becker, S., Feist, A., Mo, M. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2, 727–738 (2007).

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