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Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia coli

Andrew R. Joyce,^1,2, Jennifer L. Reed,^2, Aprilfawn White,^3,4 Robert Edwards,^5,6 Andrei Osterman,^6,7 Tomoya Baba,⁸ Hirotada Mori,^8,9 Scott A. Lesely,^3,4 Bernhard Ø. Palsson,^2* and Sanjay Agarwalla^3,4*,

Program in Bioinformatics, University of California, San Diego, La Jolla, California 92093,¹ Department of Bioengineering, University of California, La Jolla, California 92093,² The Joint Center for Structural Genomics,³ The Genomics Institute of the Novartis Research Foundation, San Diego, California 92121,⁴ Center for Microbial Sciences, San Diego State University, San Diego, California 92182,⁵ Fellowship for the Interpretation of Genomes, Burr Ridge, Illinois 60527,⁶ Burnham Institute for Medical Research, La Jolla, California 92037;,⁷ Graduate School of Biological Sciences, Nara Institute of Science and Technology (NAIST), Ikoma, Nara, Japan,⁸ Advanced Institute of Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan⁹

Received 22 May 2006/ Accepted 9 September 2006

Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis 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, and it was found 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 could be made or where the 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 the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.

Published ahead of print on 29 September 2006.

Andrew R. Joyce, Jennifer L. Reed, and Sanjay Agarwalla contributed equally to this work.

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