Integrating gene expression data into a genome-scale metabolic model to identify reprogramming during adaptive evolution.

ABSTRACT: The development of a method for identifying latent reprogramming in gene expression data resulting from adaptive laboratory evolution (ALE) in response to genetic or environmental perturbations 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 maximization of an adaptation score as a criterion indicating a pattern of using metabolism with maximum utilization of gene expression resources is defined as an objective function. Then, genes with complete expression usage and significant expression differences between wild-type and evolved strains were selected as

SUBMITTER: Yazdanpanah S 

PROVIDER: S-EPMC10547208 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature