Quantitative Prediction of Cellular Metabolism with Constraint-based Models: The COBRA Toolbox

Department of Bioengineering, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92093-0412, USA.
Nature Protocol (Impact Factor: 9.67). 02/2007; 2(3):727-38. DOI: 10.1038/nprot.2007.99
Source: PubMed

ABSTRACT 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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    • "and C x the biomass concentration . It was assumed that the rates remain constant during each 0 . 5 h integration step . The solution to this equation was fitted to the experimental data . All simulations were per - formed using MATLAB and the COBRA Toolbox software packages with Gurobi TM Optimizer ( Gurobi Optimization , Inc . , Houston , TX ) ( Becker et al . , 2007 ; Schellenberger et al . , 2011 ) ."
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    • "Calculations were made with MATLAB software version 7.12.0 (Mathworks Inc., MA) utilizing the SBML [25] and COBRA [26] toolboxes. The GNU linear programming kit (GLPK) package was used to solve LP problems. "
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