Genome-scale metabolic models, with the assumption of steady state for the internal metabolites, allow the definition of a region of feasible metabolic flux distributions. The sequencing of full genomes and the development of high-throughput analysis technologies have made available both genome-scale metabolic networks and simultaneous transcription data for all the genes of an organism. 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. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. 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). 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. 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. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |