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

By Caroline Colijn, A. Brandes, Jeremy D. Zucker, Desmond S. Lun, B. Weiner, Maha R. Farhat, T. Cheng, D. Moody, Megan R. Murray, James E. Galagan. PLoS Comput. Biol., 2009

425 citations recorded by the source corpus.

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.

Subfields: Infectious Diseases, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ff65fcef6fc0b2341fe83e0d2ed512cd77d1e321

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Citations in the corpus

Constraint-based models predict metabolic and associated cellular functions (2014, background): “…reaction bounds on the basis of mRNA and protein expression data, either by setting the bounds to zero for reactions that correspond to absent transcripts and proteins 34,35 (see the figure, part a ) or by linearly adjusting the bounds on the basis of transcript and protein abundances 103 .”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “These include constraints imposed by metabolite dilution 51 , changes in transcript level 52,53 and economy in enzyme use 43,54–56 .”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “The rules for mapping gene expression to flux bounds were implemented as described in the original publication [23].”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, methodology): “More recently, transcriptome data have been used to reduce the size of the set of computed feasible states [14-17].”
Integrative approaches for finding modular structure in biological networks (2013, methodology): “The identification of active modules has also been applied to metabolic networks 50,90–92 .”