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Robust flux balance analysis of multiscale biochemical reaction networks.

Sun Y, Fleming RM, Thiele I, Saunders MA - BMC Bioinformatics (2013)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA. yuekai@stanford.edu

ABSTRACT

Background: 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.

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.

Conclusion: 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.

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E. coli Metabolic-Expression matrix before and after lifting. Spy plot of the E. coli Metabolic-Expression matrix before and after lifting. The red areas were added by the lifting procedure and are very sparse.
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Figure 1: E. coli Metabolic-Expression matrix before and after lifting. Spy plot of the E. coli Metabolic-Expression matrix before and after lifting. The red areas were added by the lifting procedure and are very sparse.

Mentions: Unlike traditional scaling, the above lifting techniques transform poorly scaled constraints without affecting other constraints. The linear program does become larger (more constraints and variables), but the added constraints are extremely sparse and should have little impact on the performance of a typical large-scale solver (see Figure 1). Indeed, the time per iteration for the simplex method could well decrease because smaller “large” entries in the basis matrices typically lead to sparser basis factorizations.


Robust flux balance analysis of multiscale biochemical reaction networks.

Sun Y, Fleming RM, Thiele I, Saunders MA - BMC Bioinformatics (2013)

E. coli Metabolic-Expression matrix before and after lifting. Spy plot of the E. coli Metabolic-Expression matrix before and after lifting. The red areas were added by the lifting procedure and are very sparse.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750362&req=5

Figure 1: E. coli Metabolic-Expression matrix before and after lifting. Spy plot of the E. coli Metabolic-Expression matrix before and after lifting. The red areas were added by the lifting procedure and are very sparse.
Mentions: Unlike traditional scaling, the above lifting techniques transform poorly scaled constraints without affecting other constraints. The linear program does become larger (more constraints and variables), but the added constraints are extremely sparse and should have little impact on the performance of a typical large-scale solver (see Figure 1). Indeed, the time per iteration for the simplex method could well decrease because smaller “large” entries in the basis matrices typically lead to sparser basis factorizations.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA. yuekai@stanford.edu

ABSTRACT

Background: 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.

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.

Conclusion: 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.

Show MeSH