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) (13)C 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.
%0 Journal Article
%1 Schellenberger2011Quantitative
%A Schellenberger, Jan
%A Que, Richard
%A Fleming, Ronan M. T.
%A Thiele, Ines
%A Orth, Jeffrey D.
%A Feist, Adam M.
%A Zielinski, Daniel C.
%A Bordbar, Aarash
%A Lewis, Nathan E.
%A Rahmanian, Sorena
%A Kang, Joseph
%A Hyduke, Daniel R.
%A Palsson, Bernhard O.
%D 2011
%I Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.
%J Nat. Protocols
%K flux-analysis software
%N 9
%P 1290--1307
%R 10.1038/nprot.2011.308
%T Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
%U http://dx.doi.org/10.1038/nprot.2011.308
%V 6
%X 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) (13)C 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.
@article{Schellenberger2011Quantitative,
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) ({13)C} 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.
},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Schellenberger, Jan and Que, Richard and Fleming, Ronan M. T. and Thiele, Ines and Orth, Jeffrey D. and Feist, Adam M. and Zielinski, Daniel C. and Bordbar, Aarash and Lewis, Nathan E. and Rahmanian, Sorena and Kang, Joseph and Hyduke, Daniel R. and Palsson, Bernhard O.},
biburl = {https://www.bibsonomy.org/bibtex/2428681e61e7e2ed567adeaa4b607bf66/karthikraman},
citeulike-article-id = {9730074},
citeulike-linkout-0 = {http://dx.doi.org/10.1038/nprot.2011.308},
citeulike-linkout-1 = {http://dx.doi.org/10.1038/nprot.2011.308},
citeulike-linkout-2 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3319681/},
citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/21886097},
citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=21886097},
day = 4,
doi = {10.1038/nprot.2011.308},
interhash = {0655cbb44ad871ebbf2234cbbce8eab1},
intrahash = {428681e61e7e2ed567adeaa4b607bf66},
issn = {1750-2799},
journal = {Nat. Protocols},
keywords = {flux-analysis software},
month = sep,
number = 9,
pages = {1290--1307},
pmcid = {PMC3319681},
pmid = {21886097},
posted-at = {2011-09-14 06:58:29},
priority = {2},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Quantitative prediction of cellular metabolism with constraint-based models: the {COBRA} Toolbox v2.0},
url = {http://dx.doi.org/10.1038/nprot.2011.308},
volume = 6,
year = 2011
}