Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli.

ABSTRACT: A widely studied problem in systems biology is to predict bacterial phenotype from growth conditions, using mechanistic models such as flux balance analysis (FBA). However, the inverse prediction of growth conditions from phenotype is rarely considered. Here we develop a computational framework to carry out this inverse prediction on a computational model of bacterial metabolism. We use FBA to calculate bacterial phenotypes from growth conditions in E. coli, and then we assess how accurately we can predict the original growth conditions from the phenotypes. Prediction is carried out via regularized multinomial regression. Our analysis provides several important physiological and statistical insights. First, we show that by analyzing metabolic end products we can consistently predict growth

SUBMITTER: Sridhara V 

PROVIDER: S-EPMC4264753 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature