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PLoS Comput Biol DOI:10.1371/journal.pcbi.1000489

Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

Publication TypeJournal Article
Year of Publication2009
AuthorsColijn, C, Brandes, A, Zucker, J, Lun, DS, Weiner, B, Farhat, MR, Cheng, T-Y, D Moody, B, Murray, M, Galagan, JE
JournalPLoS Comput Biol
Volume5
Issue8
Pagese1000489
Date Published2009 Aug
ISSN1553-7358
KeywordsAlgorithms, Cluster Analysis, Computational Biology, Fatty Acids, Gene Expression Profiling, Gene Expression Regulation, Gene Expression Regulation, Bacterial, Genome, Bacterial, Metabolic Networks and Pathways, Models, Biological, Models, Statistical, Mycobacterium tuberculosis, Mycolic Acids, Reproducibility of Results, Software
Abstract

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.

URLhttp://dx.plos.org/10.1371/journal.pcbi.1000489
DOI10.1371/journal.pcbi.1000489
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/19714220?dopt=Abstract

Alternate JournalPLoS Comput. Biol.
PubMed ID19714220
PubMed Central IDPMC2726785
Grant List1U19AI076217 / AI / NIAID NIH HHS / United States
R01 071155 / / PHS HHS / United States
U19 AI076217 / AI / NIAID NIH HHS / United States
HHSN266200400001C / AO / NIAID NIH HHS / United States
HHSN 26620040000IC / / PHS HHS / United States