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The application of flux balance analysis in systems biology

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Abstract An increasing number of genome‐scale reconstructions of intracellular biochemical networks are being generated. Coupled with these stoichiometric models, several systems‐based approaches for probing these reconstructions in silico have been developed. One such approach, called flux balance analysis (FBA), has been effective at predicting systemic phenotypes in the form of fluxes through a reaction network. FBA employs a linear programming (LP) strategy to generate a flux distribution that is optimized toward a particular ‘objective,’ subject to a set of underlying physicochemical and thermodynamic constraints. Although classical FBA assumes steady‐state conditions, several extensions have been proposed in recent years to constrain the allowable flux distributions and enable characterization of dynamic profiles even with minimal kinetic information. Furthermore, FBA coupled with techniques for measuring fluxes in vivo has facilitated integration of computational and experimental approaches, and is allowing pursuit of rational hypothesis‐driven research. Ultimately, as we will describe in this review, studying intracellular reaction fluxes allows us to understand network structure and function and has broad applications ranging from metabolic engineering to drug discovery. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Analytical and Computational Methods > Computational Methods

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The classical flux balance analysis (FBA) approach. Panel (a) illustrates the glycolytic pathway as a representative biochemical network. The reactions that convert glucose to pyruvate are labeled v1 through v12. In panel (b), the process of generating a stoichiometric matrix, S, for a given reaction is demonstrated. Specifically, the conversion of 1 mole of fructose‐6‐phosphate (F6P) to 1 mole of fructose‐1,6‐bisphosphate (FDP) by the enzyme phosphofructokinase 1 (PFK) (reaction v3 in panel (a)) is represented as a column within S. The rows of S describe the components of the network, while the columns represent the underlying interactions. The stoichiometric coefficient at the intersection of a row and column quantitatively captures the precise interaction, with inputs delineated with negative coefficients (e.g., ‘–1′ F6P) and outputs delineated with positive coefficients (e.g., ‘+ 1′ FDP). Panel (c) depicts the classical FBA optimization framework, including the objective function and flux bounds that may be applied to the glycolytic network. Note that exchange reactions are excluded from this rendering for simplicity. Finally, the FBA‐derived flux distribution through the network is shown in panel (d).

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Stoichiometric network reconstruction and analysis with FBA. A stoichiometric network reconstruction is assembled piece‐by‐piece, with gene‐protein‐reaction (GPR) relationships assimilated from experimental data available in published literature and online databases. FBA is then used to predict a steady‐state flux distribution through the network, given certain underlying physicochemical and thermodynamic constraints. This prediction can be validated with independent experiments, and inconsistencies can lead to model refinement. In addition, with the stoichiometric network reconstruction and FBA, the network can be perturbed in different ways, ranging from differences in environmental stimuli to mutations in network structure (e.g., single‐ or double‐gene knockouts) to assess properties of the system such as robustness.

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