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Robust flux balance analysis of multiscale biochemical reaction networks

Yuekai Sun1*, Ronan MT Fleming23, Ines Thiele23 and Michael A Saunders4

Author Affiliations

1 Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA

2 Center for Systems Biology, University of Iceland, Reykjavik, Iceland

3 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg

4 Department of Management Science and Engineering, Stanford University, Stanford, USA

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BMC Bioinformatics 2013, 14:240  doi:10.1186/1471-2105-14-240

Published: 30 July 2013



Biological processes such as metabolism, signaling, and macromolecular synthesis can be modeled as large networks of biochemical reactions. Large and comprehensive networks, like integrated networks that represent metabolism and macromolecular synthesis, are inherently multiscale because reaction rates can vary over many orders of magnitude. They require special methods for accurate analysis because naive use of standard optimization systems can produce inaccurate or erroneously infeasible results.


We describe techniques enabling off-the-shelf optimization software to compute accurate solutions to the poorly scaled optimization problems arising from flux balance analysis of multiscale biochemical reaction networks. We implement lifting techniques for flux balance analysis within the openCOBRA toolbox and demonstrate our techniques using the first integrated reconstruction of metabolism and macromolecular synthesis for E. coli.


Our techniques enable accurate flux balance analysis of multiscale networks using off-the-shelf optimization software. Although we describe lifting techniques in the context of flux balance analysis, our methods can be used to handle a variety of optimization problems arising from analysis of multiscale network reconstructions.