Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models

After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from…

• NS
  National Science Foundation

  Award: 0504645
• UD
  U.S. Department of Energy

  Award: Y1-A1-8401
• WI
  Weizmann Institute of Science
• HÍ
  Háskóli Íslands
• NI
  National Institutes of Health

  Award: Y1-A1-8401
• UO
  University of California, San Diego

  Award: 0504645
• NI
  National Institute of Allergy and Infectious Diseases
• NC
  National Center for Research Resources

  Award: RR18522
• BA
  Biological and Environmental Research
• PN
  Pacific Northwest National Laboratory