title: Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses.
abstract: Flux balance analysis (FBA) of a genome-scale metabolic model allows calculation of intracellular fluxes by optimizing an objective function, such as maximization of cell growth, under given constraints, and has found numerous applications in the field of systems biology and biotechnology. Due to the underdetermined nature of the system, however, it has limitations such as inaccurate prediction of fluxes and existence of multiple solutions for an optimal objective value. Here, we report a strategy for accurate prediction of metabolic fluxes by FBA combined with systematic and condition-independent constraints that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses. Analyses of three types of genomic contexts, conserved genomic neighborhood, gene fusion events, and co-occurrence of genes across multiple organisms, were performed to suggest a group of fluxes that are likely on or off simultaneously. The flux ranges of these grouped reactions were constrained by flux-converging pattern analysis. FBA of the Escherichia coli genome-scale metabolic model was carried out under several different genotypic (pykF, zwf, ppc, and sucA mutants) and environmental (altered carbon source) conditions by applying these constraints, which resulted in flux values that were in good agreement with the experimentally measured (13)C-based fluxes. Thus, this strategy will be useful for accurately predicting the intracellular fluxes of large metabolic networks when their experimental determination is difficult.
authors.affiliation: Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST, Daejeon 305-701, Republic of Korea.
authors.forename: Jong Myoung
authors.initials: JM
authors.lastname: Park
authors.forename: Tae Yong
authors.initials: TY
authors.lastname: Kim
authors.forename: Sang Yup
authors.initials: SY
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D002244:Carbon / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000235:genetics / Q000378:metabolism; D016680:Genome, Bacterial / Q000235:genetics; D023281:Genomics / Q000379:methods; D053858:Metabolic Networks and Pathways / Q000235:genetics / Q000502:physiology*; D055442:Metabolome / Q000235:genetics / Q000502:physiology; D055432:Metabolomics / Q000379:methods*; D008954:Models, Biological; D009154:Mutation; D049490:Systems Biology / Q000379:methods
chemical_list: D029968:Escherichia coli Proteins; D002244:Carbon