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J. Biol. Chem., Vol. 282, Issue 39, 28791-28799, September 28, 2007

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Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data^*

You-Kwan Oh¹, Bernhard O. Palsson, Sung M. Park², Christophe H. Schilling, and Radhakrishnan Mahadevan³

From the Department of Bioengineering, University of California at San Diego, La Jolla, California 92093-0412 and Genomatica, Inc., San Diego, California 92121

In this report, a genome-scale reconstruction of Bacillus subtilis metabolism and its iterative development based on the combination of genomic, biochemical, and physiological information and high-throughput phenotyping experiments is presented. The initial reconstruction was converted into an in silico model and expanded in a four-step iterative fashion. First, network gap analysis was used to identify 48 missing reactions that are needed for growth but were not found in the genome annotation. Second, the computed growth rates under aerobic conditions were compared with high-throughput phenotypic screen data, and the initial in silico model could predict the outcomes qualitatively in 140 of 271 cases considered. Detailed analysis of the incorrect predictions resulted in the addition of 75 reactions to the initial reconstruction, and 200 of 271 cases were correctly computed. Third, in silico computations of the growth phenotypes of knock-out strains were found to be consistent with experimental observations in 720 of 766 cases evaluated. Fourth, the integrated analysis of the large-scale substrate utilization and gene essentiality data with the genome-scale metabolic model revealed the requirement of 80 specific enzymes (transport, 53; intracellular reactions, 27) that were not in the genome annotation. Subsequent sequence analysis resulted in the identification of genes that could be putatively assigned to 13 intracellular enzymes. The final reconstruction accounted for 844 open reading frames and consisted of 1020 metabolic reactions and 988 metabolites. Hence, the in silico model can be used to obtain experimentally verifiable hypothesis on the metabolic functions of various genes.

Received for publication, May 7, 2007

Some of the authors are stockholders of Genomatica, a company that develops and commercializes genome-scale metabolic models.

^* This work was supported in part by the Department of Energy and by the Small Business Innovation Research grant program (DE-FG02-05ER84280), which enabled the high-throughput phenotyping integration studies and overall model development. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

The on-line version of this article (available at http://www.jbc.org) contains supplemental Figs. S1 and S2, Tables S1-S7, and additional Excel and Word files.

¹ Supported by the Post-doctoral Fellowship Program of Korea Science & Engineering Foundation. Present address: Bioenergy Research Center, Korea Institute of Energy Research, 71-2, Jang-dong, Yuseong-gu, Daejeon 305-343, Korea.

² Present address: Novozymes North America Inc., Franklington, NC 27525.

³ To whom correspondence should be addressed: Present address: Dept. of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario M5S3E5, Canada. Tel.: 416-046-0096; Fax: 416-978-8605; E-mail: mahadevan{at}chem-eng.utoronto.ca.

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