title: Inferring carbon sources from gene expression profiles using metabolic flux models.
abstract: BACKGROUND
  Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism's metabolic network.
  
  
  RESULTS
  For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not.
  
  
  CONCLUSIONS
  Inferences about a microorganism's nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism's alteration of gene expression is adaptive to its nutrient environment.
authors.affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
authors.forename: Aaron
authors.initials: A
authors.lastname: Brandes
authors.forename: Desmond S
authors.initials: DS
authors.lastname: Lun
authors.forename: Kuhn
authors.initials: K
authors.lastname: Ip
authors.forename: Jeremy
authors.initials: J
authors.lastname: Zucker
authors.forename: Caroline
authors.initials: C
authors.lastname: Colijn
authors.forename: Brian
authors.initials: B
authors.lastname: Weiner
authors.forename: James E
authors.initials: JE
authors.lastname: Galagan
mesh_terms: D000465:Algorithms; D018533:Biomass; D002244:Carbon / Q000378:metabolism*; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics* / Q000254:growth & development / Q000378:metabolism*; D020869:Gene Expression Profiling; D016680:Genome, Bacterial; D053858:Metabolic Networks and Pathways; D008954:Models, Biological
chemical_list: D002244:Carbon