Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions. | Research Open Access Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions Evan S Snitkin Aimée M Dudley Daniel M Janse Kaisheen WongA George M Church and Daniel Segrè Addresses Bioinformatics graduate Program Boston University Boston MA 02215 USA. Institute for Systems Biology Seattle WA 98103 USA. McKinsey Company London SW1Y 4UH UK. Department of Genetics Harvard Medical School Boston MA 02115 USA. Departments of Biology and Biomedical Engineering Boston University Boston MA 02215 USA. Correspondence Daniel Segrè. Email dsegre@ Deceased Published 22 September 2008 Genome Biology 2008 9 RI40 doi gb-2008-9-9-rI40 The electronic version of this article is the complete one and can be found online at http 2008 9 9 RI40 Received 27 June 2008 Revised I September 2008 Accepted 22 September 2008 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 licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract 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 .
