MRI: an Algorithm to Identify Metabolic Reprogramming During Adaptive Laboratory Evolution Using Gene Expression Data

The development of a method for identifying latent reprogramming in gene expression data resulting from evolution has been a challenge. In this study, a method called Metabolic Reprogramming Identifier (MRI), based on the integration of expression data to a genome-scale metabolic model, has been developed. To identify key genes playing the main role in reprogramming, a MILP problem is presented and maximum utilization of gene expression resources is defined as an objective function. Then, genes with complete expression usage and significant expression difference between wild-type and evolved strains were selected as key genes for reprogramming. This score is also applied to evaluate compatibility of expression pattern with maximal use of key genes. The method was implemented to investigate the reprogramming of E. coli during adaptive evolution caused by changing carbon sources. The results indicate the importance of inner membrane in reprogramming of E. coli to adapt to the new environment. The method predicts no reprogramming occurs when switching from glucose to glycerol.