Journal
MOLECULAR SYSTEMS BIOLOGY
Volume 6, Issue -, Pages -Publisher
WILEY
DOI: 10.1038/msb.2010.47
Keywords
Escherichia coli; genome-scale models; microarray; optimality; proteomics
Categories
Funding
- NIH National Center for Research Resources [RR18522]
- U S Department of Energy Office of Biological and Environmental Research (DOE/BER)
- Fulbright fellowship
- NSF IGERT [DGE-0504645]
- NIH [R01 GM062791, R01 GM57089]
- NIAID [IAA Y1-A1-8401]
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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 dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states. Molecular Systems Biology 6: 390; published online 27 July 2010; doi:10.1038/msb.2010.47 Subject Categories: functional genomics; simulation and data analysis
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