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Can single knockouts accurately single out gene functions?

David Deutscher14, Isaac Meilijson2, Stefan Schuster3 and Eytan Ruppin4*

Author Affiliations

1 Current address : Google Haifa, Haifa, Israel 31905

2 School of Mathematical Sciences, Tel Aviv University, Tel-Aviv, 69978, Israel

3 Faculty of Biology and Pharmaceutics, Section of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany

4 School of Computer Sciences and School of Medicine, Tel Aviv University, Tel-Aviv, 69978, Israel

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BMC Systems Biology 2008, 2:50  doi:10.1186/1752-0509-2-50

Published: 18 June 2008



When analyzing complex biological systems, a major objective is localization of function – assessing how much each element contributes to the execution of specific tasks. To establish causal relationships, knockout and perturbation studies are commonly executed. The vast majority of studies perturb a single element at a time, yet one may hypothesize that in non-trivial biological systems single-perturbations will fail to reveal the functional organization of the system, owing to interactions and redundancies.


We address this fundamental gap between theory and practice by quantifying how misleading the picture arising from classical single-perturbation analysis is, compared with the full multiple-perturbations picture. To this end we use a combination of a novel approach for quantitative, rigorous multiple-knockouts analysis based on the Shapley value from game theory, with an established in-silico model of Saccharomyces cerevisiae metabolism. We find that single-perturbations analysis misses at least 33% of the genes that contribute significantly to the growth potential of this organism, though the essential genes it does find are responsible for most of the growth potential. But when assigning gene contributions for individual metabolic functions, the picture arising from single-perturbations is severely lacking and a multiple-perturbations approach turns out to be essential.


The multiple-perturbations investigation yields a significantly richer and more biologically plausible functional annotation of the genes comprising the metabolic network of the yeast.