JB Accepts, published online ahead of print on 29 September 2006

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J. Bacteriol. doi:10.1128/JB.00740-06
Copyright (c) 2006, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.

Experimental and computational assessment of conditionally essential genes in E. coli

Andrew R. Joyce, Jennifer L. Reed, Aprilfawn White, Robert Edwards, Andrei Osterman, Tomoya Baba, Hirotada Mori, Scott A. Lesely, Bernhard . Palsson^*, and Sanjay Agarwalla^*

Program in Bioinformatics, University of California, San Diego, La Jolla, CA 92093; Department of Bioengineering, University of California, La Jolla, CA 92093; The Joint Center for Structural Genomics; The Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121; Center for Microbial Sciences, San Diego State University, San Diego, CA 92182; Fellowship for the Interpretation of Genomes, Burr Ridge, IL 60527; Burnham Institute for Medical Research, La Jolla, CA 92037; Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), Ikoma, Nara, Japan; Advanced Institute of Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan

^* To whom correspondence should be addressed. Email: palsson{at}ucsd.edu, sagarwalla{at}gnf.org.

Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these datasets have yet to be analyzed in the context of a genome-scale model. Here, we present an integrative model driven analyis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome-scale metabolic and transcriptional regulatory model of E. coli, finding that the model made the correct prediction in 91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements can be made or where current literature lacks an explanation for the observed phenotypes. The identified set of essential genes, and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing this dataset in terms of metabolic subsystems across multiple genomes we can project which metabolic pathways are likely to play an equally important role in other organisms. Overall, this work establishes a paradigm that will drive model enhancement, while simultaneously generating hypotheses that ultimately lead to a better understanding of the organism.

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