An integrated approach to characterize genetic interaction networks in yeast metabolism

• Published in: Nature genetics Vol. 43; no. 7; pp. 656 - 662
• Main Authors: Papp, Balázs, Szappanos, Balázs, Kovács, Károly, Szamecz, Béla, Honti, Frantisek, Costanzo, Michael, Baryshnikova, Anastasia, Gelius-Dietrich, Gabriel, Lercher, Martin J, Jelasity, Márk, Myers, Chad L, Andrews, Brenda J, Boone, Charles, Oliver, Stephen G, Pál, Csaba
• Format: Journal Article
• Language: English
• Published: New York, NY Nature Publishing Group 01.07.2011
• Subjects: Artificial Intelligence; Automation; Biological and medical sciences; Cell metabolism; Computational Biology; Fundamental and applied biological sciences. Psychology; Gene expression; Gene Expression Regulation, Fungal; Gene Regulatory Networks; Genetic aspects; Genetics of eukaryotes. Biological and molecular evolution; Models, Genetic; Mutation; Physiological aspects; Protein Interaction Mapping; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; Software; Studies; Yeast fungi; Canada; United Kingdom; United States; Germany; Gene interaction; Metabolism; Yeast
• ISSN: 1061-4036; 1546-1718; 1546-1718
• DOI: 10.1038/ng.846

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.