Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production

• DOI: 10.1371/journal.pcbi.1000489
• Publication Date: 2009-08-27T22:10:15Z
• Authors: Caroline Colijn; Aaron Brandes; Jeremy Zucker; Desmond S. Lun; Brian Weiner; Maha R. Farhat; Tan-Yun Cheng; D. Branch Moody; Megan Murray; James E. Galagan
• Keywords: Mycolic acid; Flux Balance Analysis; Compendium; Metabolic network; Metabolic pathway

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