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Model-Driven Analysis of Experimentally Determined Growth Phenotypes for 465 Yeast Gene Deletion Mutants Under 16 Different Conditions

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dc.contributor.author Snitkin, Evan S en_US
dc.contributor.author Dudley, Aimée M en_US
dc.contributor.author Janse, Daniel M en_US
dc.contributor.author Wong, Kaisheen en_US
dc.contributor.author Church, George M en_US
dc.contributor.author Segrè, Daniel en_US
dc.date.accessioned 2012-01-09T14:44:45Z
dc.date.available 2012-01-09T14:44:45Z
dc.date.copyright 2008 en_US
dc.date.issued 2008-09-22 en_US
dc.identifier.citation Snitkin, Evan S, Aimée M Dudley, Daniel M Janse, Kaisheen Wong, George M Church, Daniel Segrè. "Model-Driven Analysis of Experimentally Determined Growth Phenotypes for 465 Yeast Gene Deletion Mutants Under 16 Different Conditions" Genome Biology 9(9):R140. (2008) en_US
dc.identifier.issn 1465-6914 en_US
dc.identifier.uri http://hdl.handle.net/2144/2796
dc.description.abstract An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results. BACKGROUND. Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored. RESULTS. In this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes. CONCLUSIONS. Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources. en_US
dc.description.sponsorship National Human Genome Institute, National Institute of Health (K22 HG002908); United States Department of Energy; National Institutes of Health, NASA Astrobiology Institute en_US
dc.language.iso en en_US
dc.publisher BioMed Central en_US
dc.rights Copyright 2008 Snitkin et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.rights.uri http://creativecommons.org/licenses/by/2.0 en_US
dc.title Model-Driven Analysis of Experimentally Determined Growth Phenotypes for 465 Yeast Gene Deletion Mutants Under 16 Different Conditions en_US
dc.type article en_US
dc.identifier.doi 10.1186/gb-2008-9-9-r140 en_US
dc.identifier.pubmedid 18808699 en_US
dc.identifier.pmcid 2592718 en_US


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Copyright 2008 Snitkin et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Except where otherwise noted, this item's license is described as Copyright 2008 Snitkin et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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