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Model driven analysis of Escherichia coli metabolism

  • Author(s): Reed, Jennifer Leanne
  • et al.
Abstract

Diverse datasets, including genomic, transcriptomic, proteomic, and metabolomic data are becoming readily available, and there is a need to integrate these datasets within a modeling framework. Constraint-based metabolic and regulatory models of Escherichia coli have been developed and used to study the bacterium's metabolism and phenotypic behavior as well as analyze high-throughput data. This work describes an updated genome-scale metabolic model of E. coli (iJR904), which includes 904 genes, 625 metabolites, and 931 unique elementally and charge balanced biochemical reactions. Analysis of network gaps led to putative assignments for 55 ORFs. Subsequent calculation of sets of alternate optimal growth solutions for iJR904 found that: 1) only a small subset of reactions in the network have variable fluxes across alternate optima for a given environment, 2) sets of correlated reactions showed moderate agreement with the currently known transcriptional regulatory structure in E. coli and available expression data, and 3) reaction usage under different environmental conditions can provide clues about network regulatory needs. Calculation of suboptimal flux distributions identified reactions which are used significantly more suboptimally than optimally. An isotopomer model containing a significant number of the metabolic reactions in iJR904 was also generated, representing the largest such model to date. Calculation of flux distributions from experimental GC-MS measurements showed that the calculated flux distributions were highly dependent on the metabolic reconstruction used to generate isotopomer models. Theoretical GC-MS data was calculated by the isotopomer models to evaluate experimental conditions and to identify correlated measurements. An iterative model building algorithm was also developed to identify missing metabolic and transport activities from a reconstruction based on the analysis of positive growth environments from high-throughput phenotyping data. When applied to a genome-scale reconstruction of E. coli metabolism, metabolic and transport activities were identified that if added would reconcile growth on 25 different carbon and nitrogen sources. A portion of these missing activities were investigated experimentally, which led to the identification of three new transport activities, one metabolic activity, and their associated genes. Together this work provides detailed computational analysis of E. coli metabolic behavior and comparisons of model results with experimental data

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