PNNL

Abstract

After hundreds of generations of mid log phase growth, Escherichia coli acquires a higher growth rate as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). FBA solutions contain hundreds of variables that can be examined using omics methods. We report that 99% 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 resulting evolved strains reinforce the optimal growth predictions. Specifically, through constraint-based analysis of the proteomic and transcriptomic data, we find: 1) selective pressure for the predicted optimal growth states and a minimization of network flux; 2) suppression of genes outside of the optimal growth solutions; and 3) a trend towards usage of more efficient metabolic pathways. For processes not in GEMs, we find 4) an increase in the transcription/translation machinery and stringent response suppression, and 5) that established regulons are significantly down-regulated. Thus, differential expression supports observed growth phenotype changes, and observed expression in evolved strains is consistent with GEM computed optimal growth states.