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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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Flux variability analysis of the E. coli Metabolic-Expression model. Minimum and maximum flux for iAF1260 (which only accounts for metabolic reactions) versus the minimum and maximum flux for the Metabolic-Expression model. Each colored box corresponds to a different reaction in metabolism. The boxes are always longer on the axis for the metabolic model (iAF1260) than on the axis for the Metabolic-Expression model. This demonstrates that increasing the comprehensiveness of the model toward whole cell modeling leads to a substantial shrinkage of the steady state solution space. (Fluxes are plotted in ).
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Figure 2: Flux variability analysis of the E. coli Metabolic-Expression model. Minimum and maximum flux for iAF1260 (which only accounts for metabolic reactions) versus the minimum and maximum flux for the Metabolic-Expression model. Each colored box corresponds to a different reaction in metabolism. The boxes are always longer on the axis for the metabolic model (iAF1260) than on the axis for the Metabolic-Expression model. This demonstrates that increasing the comprehensiveness of the model toward whole cell modeling leads to a substantial shrinkage of the steady state solution space. (Fluxes are plotted in ).

Mentions: We also used lifting to conduct flux variability analysis (FVA) [14] for the ME76664 model and obtained biologically consistent results (see FigureĀ 2). We compared the flux span of each metabolic reaction in ME76664 with the flux span of the corresponding reaction in the E. coli metabolic model (iAF1260) [15]. The chief difference between these two models is that in ME76664 the metabolic building blocks (e.g., amino acids) are used to synthesize the metabolic enzymes, which in turn catalyze the metabolic reactions, while in iAF1260 the building blocks are collected in a static biomass reaction. Artifacts with FBA on metabolic models, such as thermodynamically infeasible flux around stoichiometrically balanced reaction cycles, are eliminated for all enzyme-catalyzed reactions in ME76664, as the coupling constraints penalize high flux rates. These constraints also restrict the maximum possible flux rates through enzyme catalyzed reactions due to the demand-supply challenge for the building blocks, thus limiting the set of possible transcriptomes and proteomes of the model. Overall, the feasible steady state solution space is substantially reduced in ME76664 compared to the metabolic model alone.


Robust flux balance analysis of multiscale biochemical reaction networks.

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

Flux variability analysis of the E. coli Metabolic-Expression model. Minimum and maximum flux for iAF1260 (which only accounts for metabolic reactions) versus the minimum and maximum flux for the Metabolic-Expression model. Each colored box corresponds to a different reaction in metabolism. The boxes are always longer on the axis for the metabolic model (iAF1260) than on the axis for the Metabolic-Expression model. This demonstrates that increasing the comprehensiveness of the model toward whole cell modeling leads to a substantial shrinkage of the steady state solution space. (Fluxes are plotted in ).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Flux variability analysis of the E. coli Metabolic-Expression model. Minimum and maximum flux for iAF1260 (which only accounts for metabolic reactions) versus the minimum and maximum flux for the Metabolic-Expression model. Each colored box corresponds to a different reaction in metabolism. The boxes are always longer on the axis for the metabolic model (iAF1260) than on the axis for the Metabolic-Expression model. This demonstrates that increasing the comprehensiveness of the model toward whole cell modeling leads to a substantial shrinkage of the steady state solution space. (Fluxes are plotted in ).
Mentions: We also used lifting to conduct flux variability analysis (FVA) [14] for the ME76664 model and obtained biologically consistent results (see FigureĀ 2). We compared the flux span of each metabolic reaction in ME76664 with the flux span of the corresponding reaction in the E. coli metabolic model (iAF1260) [15]. The chief difference between these two models is that in ME76664 the metabolic building blocks (e.g., amino acids) are used to synthesize the metabolic enzymes, which in turn catalyze the metabolic reactions, while in iAF1260 the building blocks are collected in a static biomass reaction. Artifacts with FBA on metabolic models, such as thermodynamically infeasible flux around stoichiometrically balanced reaction cycles, are eliminated for all enzyme-catalyzed reactions in ME76664, as the coupling constraints penalize high flux rates. These constraints also restrict the maximum possible flux rates through enzyme catalyzed reactions due to the demand-supply challenge for the building blocks, thus limiting the set of possible transcriptomes and proteomes of the model. Overall, the feasible steady state solution space is substantially reduced in ME76664 compared to the metabolic model alone.

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
Related in: MedlinePlus