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Latendresse M, Krummenacker M, Trupp M, et al. Construction and completion of flux balance models from pathway databases. Bioinformatics. 2012;28(3):388-96doi: 10.1093/bioinformatics/btr681.
Latendresse, M., Krummenacker, M., Trupp, M., & Karp, P. D. (2012). Construction and completion of flux balance models from pathway databases. Bioinformatics (Oxford, England), 28(3), 388-96. https://doi.org/10.1093/bioinformatics/btr681
Latendresse, Mario, et al. "Construction and completion of flux balance models from pathway databases." Bioinformatics (Oxford, England) vol. 28,3 (2012): 388-96. doi: https://doi.org/10.1093/bioinformatics/btr681
Latendresse M, Krummenacker M, Trupp M, Karp PD. Construction and completion of flux balance models from pathway databases. Bioinformatics. 2012 Feb 01;28(3):388-96. doi: 10.1093/bioinformatics/btr681. Epub 2012 Jan 18. PMID: 22262672; PMCID: PMC3268246.
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Bioinformatics. 2012 Feb 01;28(3):388-96. doi: 10.1093/bioinformatics/btr681. Epub 2012 Jan 18.

Construction and completion of flux balance models from pathway databases.

Bioinformatics (Oxford, England)

Mario Latendresse, Markus Krummenacker, Miles Trupp, Peter D Karp

Affiliations

  1. Bioinformatics Research Group/Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA. [email protected]

PMID: 22262672 PMCID: PMC3268246 DOI: 10.1093/bioinformatics/btr681

Abstract

MOTIVATION: Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models can be time consuming and tedious because of the difficulty in assembling completely accurate descriptions of these sets, and in identifying errors in the composition of these sets. For example, the presence of a single non-producible metabolite in the biomass will make the entire model infeasible. Other difficulties in FBA modeling are that model distributions, and predicted fluxes, can be cryptic and difficult to understand.

RESULTS: We present a multiple gap-filling method to accelerate the development of FBA models using a new tool, called MetaFlux, based on mixed integer linear programming (MILP). The method suggests corrections to the sets of reactions, biomass metabolites, nutrients and secretions. The method generates FBA models directly from Pathway/Genome Databases. Thus, FBA models developed in this framework are easily queried and visualized using the Pathway Tools software. Predicted fluxes are more easily comprehended by visualizing them on diagrams of individual metabolic pathways or of metabolic maps. MetaFlux can also remove redundant high-flux loops, solve FBA models once they are generated and model the effects of gene knockouts. MetaFlux has been validated through construction of FBA models for Escherichia coli and Homo sapiens.

AVAILABILITY: Pathway Tools with MetaFlux is freely available to academic users, and for a fee to commercial users. Download from: biocyc.org/download.shtml.

CONTACT: [email protected]

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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