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Title: A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.
 [1] ;  [1]
  1. The Pennsylvania State Univ., University Park, PA (United States)
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2041-1723
Nature Publishing Group
Research Org:
Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
60 APPLIED LIFE SCIENCES; bacteria; computer modelling; metabolic engineering
OSTI Identifier: