Context-specific metabolic networks are consistent with experiments.

TitleContext-specific metabolic networks are consistent with experiments.
Publication TypeJournal Article
Year of Publication2008
AuthorsBecker SA, Palsson BØ
JournalPLoS computational biology
Volume4
Issue5
Paginatione1000082
PubMed Date2008 May
ISSN1553-7358
KeywordsAlgorithms, Computer Simulation, Gene Expression Profiling, Models, Biological, Proteome, Research, Signal Transduction
Abstract

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

Alternate JournalPLoS Comput. Biol.
PubMed ID18483554

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