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

Jan Schellenberger, Richard Que, Ronan M.T. Fleming, Ines Thiele, Jeffrey D. Orth, Adam M. Feist, Daniel C. Zielinski, Aarash Bordbar, Nathan E. Lewis, Sorena Rahmanian, Joseph Kang, Daniel R. Hyduke*, Bernhard Palsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

968 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1290-1307
Number of pages18
JournalNature Protocols
Issue number9
Publication statusPublished - Sept 2011

Bibliographical note

Funding Information:
acKnowleDGMents 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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