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

Journal name:
Nature Protocols
Volume:
6,
Pages:
1290–1307
Year published:
DOI:
doi:10.1038/nprot.2011.308
Published online

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.

At a glance

Figures

  1. The philosophy of COBRA.
    Figure 1: The philosophy of COBRA.

    (a) COBRA of biological networks involves the creation of network models from a variety of biological data sources. The capabilities of the model are then assessed in the context of physical, chemical, regulatory and omics constraints (reproduced from Becker et al.17 with permission). (b) COBRA models are often derived from BiGG knowledgebases, which are essentially 2D annotations of the genome that relate metabolic activity to genomic loci. (Left inset) In E.coli, the GAPD activity can be provided by two isozymes (GapA or GapC); GapC is a heteromeric protein that requires genes from two genomic loci. The contents of a BiGG knowledgebase can be converted to a map (right) to facilitate visual interpretation, or to a mathematical modeling formalism to develop and explore hypotheses, such as a stoichiometric matrix (bottom) that can be used to explore mass flow through the network (reproduced with permission from Reed et al.33, with modifications).

  2. Overview of the COBRA Toolbox.
    Figure 2: Overview of the COBRA Toolbox.

    (a) Seven categories of COBRA methods contained within version 2.0 of the COBRA Toolbox. (b) The COBRA Toolbox contains solver interface functions for linear, quadratic, mixed integer linear and quadratic, and nonlinear programming problems. Functions to read and write models in several formats are available. A test suite is included to validate installation and provide an example for implementation of many methods.

  3. Flux balance analysis of E. coli core model.
    Figure 3: Flux balance analysis of E. coli core model.

    (Left) Full E. coli core map. (Right) Zoom in on the optimal flux distribution map of the citric acid cycle. (Bottom) Zoom in on the flux color scale. Reactions are colored according to a scale of cyan (flux of 15 mmol per gDW per h or greater in the reverse direction) to magenta (flux of 15 mmol per gDW per h) or greater in the forward direction). Reactions carrying zero flux have their corresponding arrows narrowed.

  4. Flux variability analysis of E. coli.
    Figure 4: Flux variability analysis of E. coli.

    (Right) Reaction map of E. coli core model. (Left) Flux variability analysis of part of glycolysis and pentose phosphate pathway in the E. coli core model when growth rate is constrained to 90% of optimal. Bidirectional reversible reactions are colored green. Unidirectional reversible reactions that carry flux in the forward direction are colored magenta. Unidirectional reversible reactions that carry flux only in the reverse direction are colored cyan. Irreversible fluxes are colored blue. Unidirectional fluxes have enlarged arrowheads in the direction of the flux.

  5. Sampling histogram of glycolysis, using the E. coli core model under aerobic and anaerobic glucose minimal medium conditions.
    Figure 5: Sampling histogram of glycolysis, using the E. coli core model under aerobic and anaerobic glucose minimal medium conditions.

    For growth in aerobic (blue) versus anaerobic (green) medium, there is a large shift in the probable flux through many of the reactions. In general, the range of flux probabilities for each reaction became more constrained. Phosphoglucose isomerase (PGI) switched from being able to carry flux in either direction with aerobic conditions to only carrying flux in the forward direction with anaerobic conditions.

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Author information

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

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.

Competing financial interests

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

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