Issue 12, 2009

A network-based method for predicting gene–nutrient interactions and its application to yeast amino-acidmetabolism

Abstract

Cellular metabolism is highly dependent on environmental factors, such as nutrients, toxins and drugs, genetic factors, and interactions between the two. Previous experimental and computational studies of how environmental factors affect cellular metabolism were limited to the analysis of only a small set of growth media. In this study, we present a new computational method for predicting metabolic genenutrient interactions (GNI) that uncovers the dependence of gene essentiality on the presence or absence of nutrients in the growth medium. The method is based on constraint-based modeling, permitting the systematic exploration of a large putative growth media ‘space’. Applying this method to predict GNIs in the amino-acidmetabolism system of yeast reveals complex interdependencies between amino-acidbiosynthesis pathways. The predicted GNIs also enable the ‘reverse-prediction’ of growth media composition, based on gene essentiality data. These results suggest that our approach may be applied to learn about the host environment in which a microorganism is embedded given data pertaining to gene lethality, providing a means for the identification of a species’ natural habitat.

Graphical abstract: A network-based method for predicting gene–nutrient interactions and its application to yeast amino-acidmetabolism

Supplementary files

Article information

Article type
Paper
Submitted
24 Dec 2008
Accepted
07 May 2009
First published
18 Jun 2009

Mol. BioSyst., 2009,5, 1732-1739

A network-based method for predicting genenutrient interactions and its application to yeast amino-acidmetabolism

I. Diamant, Y. C. Eldar, O. Rokhlenko, E. Ruppin and T. Shlomi, Mol. BioSyst., 2009, 5, 1732 DOI: 10.1039/B823287N

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