Dec 17, 2014

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

PloS One
Viswanadham SridharaClaus O Wilke

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 conditions. Second, prediction is reliable even in the presence of small amounts of impurities. Third, flux through a relatively small number of reactions per growth source (∼10) is sufficient for accurate prediction. Fourth, combining the predictions from two separate models, one trained only on carbon sources and one only on nitrogen sources, performs better than models trained to perform joint prediction. Finally, that separate predictions...Continue Reading

  • References27
  • Citations2

Citations

Mentioned in this Paper

Metabolic Process, Cellular
Biochemical Pathway
Glycerin
Fructose
Metabolic Networks
Amino Acids, I.V. solution additive
Pyruvate Measurement
Uptake
Cytosine
Putrescine

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