Interactive direct volume rendering of curvilinear and unstructured data

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 gene-nutrient 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-acid metabolism system of yeast reveals complex interdependencies between amino-acid biosynthesis 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.