An integrated approach to characterize genetic interaction networks in yeast metabolism

Balázs Szappanos, Károly Kovács, Béla Szamecz, Frantisek Honti, Michael Costanzo, Anastasia Baryshnikova, Gabriel Gelius-Dietrich, Martin J. Lercher, Márk Jelasity, Chad L. Myers, Brenda J. Andrews, Charles Boone, Stephen G. Oliver, Csaba Pál, Balázs Papp

Research output: Contribution to journalArticlepeer-review

167 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)656-662
Number of pages7
JournalNature Genetics
Issue number7
StatePublished - Jul 2011

Bibliographical note

Funding Information:
This work was supported by grants from The International Human Frontier Science Program Organization, the Hungarian Scientific Research Fund (OTKA PD 75261) and the ‘Lendület Program’ of the Hungarian Academy of Sciences

Funding Information:
(B.P.), European Research Council (202591), Wellcome Trust and Hungarian Scientific Research Fund (C.P.), FEBS Long-Term Fellowship (B. Szamecz), Biotechnology & Biological Sciences Research Council (Grant BB/C505140/1) and the UNICELLSYS Collaborative Project (No. 201142) of the European Commission (S.G.O.), the US National Institutes of Health (1R01HG005084-01A1) and a seed grant from the University of Minnesota Biomedical Informatics and Computational Biology program (C.L.M.), the Canadian Institutes of Health Research (MOP-102629) (C.B. and B.J.A.) and the US National Institutes of Health (1R01HG005853-01) (C.B., B.J.A. and C.L.M.).


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