Bioinformatics Advance Access published online on January 25, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti245
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1 Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
* To whom correspondence should be addressed.
Motivation: A phenotype mechanism is classically derived through the study of a set of mutants and comparison of their biochemical capabilities. One method of comparing mutant capabilities is to characterize producible and knocked out metabolites. However, such an effect is difficult to manually assess, especially for a large biochemical network and a complex media. Current algorithmic approaches towards analyzing metabolic networks either do not address this specific property or are computationally infeasible on the genome-scale. Results: We have developed a novel genome-scale computational approach that identifies the full set of biochemical species that are knocked out from the metabolome following a gene deletion. Results from this approach are combined with data from in vivo mutant screens to examine the essentiality of metabolite production for a phenotype. This approach can also be a useful tool for metabolic network annotation validation and refinement in newly sequenced organisms. Combining an in silico genome-scale model of E. coli metabolism with in vivo survival data, we uncover possible essential roles for several cell membrane, cell wall, and quinone species. We also identify specific biomass components whose production appears to be non-essential for survival, contrary to the assumptions of previous models. Availability: Programs are available upon request from the authors in the form of Matlab script files.
Received October 22, 2004
Revised December 8, 2004
Accepted December 19, 2004
Article
Investigating metabolite essentiality through genome scale analysis of E. coli production capabilities
ski 1*,
lin Belta 2,
2 Dept. of Mechanical Engineering, Drexel University, Philadelphia, PA, 19104
3 General Robotics, Automation, Sensing, and Perception Laboratory, University of Pennsylvania, Philadelphia, PA, 19104
4 Dept. of Infectious Disease, University of Pennsylvania School of Medicine, Philadelphia, PA, 19104
Marcin Imieliski, E-mail: imielns{at}mail.med.upenn.edu
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