Model Driven Mechanistic Analysis of Adaptive RNAP Mutations

Fitness increase by adaptive RNA polymerase mutations have been reported for different environments. The effects of these mutations in molecular phenotype have been detailed but the mechanisms that underlie the network reprogramming have not been decoded. In this work two RNAP mutations obtained from a glucose minimal media Adaptive Laboratory Evolution experiment were reintroduced in wild type Escherichia coli. Using genome wide data across different conditions and a genome scale model of metabolism and gene expression it was possible to unravel the mechanism of network reprogramming by a single amino acid change in the transcription machinery. Our results show that suboptimal phenotype is caused by the up regulation of the non-growth functions, specifically during growth on batch minimal media. The down regulation of non-growth functions by the introduced mutations reduces the proteome and the maintenance energy needs enabling higher cell yield and growth rate. Structural and regulatory insights of mechanism of global network reprogramming are presented and their physiological implications for metabolic engineering highlighted.