Bioinformatics Advance Access originally published online on January 25, 2005
Bioinformatics 2005 21(9):2008-2016; doi:10.1093/bioinformatics/bti245
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Investigating metabolite essentiality through genome-scale analysis of Escherichia coli production capabilities
ski 1,*
lin Belta 3
1Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine Philadelphia, PA, 19104, USA
2Department of Medicine, University of Pennsylvania School of Medicine Philadelphia, PA, 19104, USA
3Department of Mechanical Engineering, Drexel University Philadelphia, PA, 19104, USA
4General Robotics, Automation, Sensing and Perception Laboratory, University of Pennsylvania Philadelphia, PA, 19104, USA
*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 Escherichia coli metabolism with in vivo survival data, we uncover possible essential roles for several cell membranes, cell walls, 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.
Contact: imielns{at}mail.med.upenn.edu
Supplementary information: http://www.cis.upenn.edu/biocomp/manuscripts/bioinformatics_bti245/supp-info.html
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