title: Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes.
abstract: Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. Once the region of feasible flux distributions has been defined, a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated. These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes. The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change (transcriptional regulation) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations (metabolic regulation). The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. This has not been previously reported. The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases.
authors.affiliation: Systems Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
authors.forename: Sergio
authors.initials: S
authors.lastname: Bordel
authors.forename: Rasmus
authors.initials: R
authors.lastname: Agren
authors.forename: Jens
authors.initials: J
authors.lastname: Nielsen
mesh_terms: D000332:Aerobiosis; D000465:Algorithms; D000693:Anaerobiosis; D004798:Enzymes / Q000096:biosynthesis / Q000235:genetics* / Q000378:metabolism; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D016678:Genome; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D009154:Mutation; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism / Q000502:physiology; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*; D014157:Transcription Factors
chemical_list: D004798:Enzymes; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors