An integrated approach to characterize genetic interaction networks in yeast metabolism

Balázs Papp and colleagues construct a genetic interaction map of yeast metabolism and use a genome-scale systems biology model to examine the structure of the metabolic network. They use an automated machine-learning method to reconcile differences between the experimental and computational genetic interaction maps. In contrast to previous studies, they do not find evidence for prevalent positive interactions in essential metabolic genes. Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae , ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.