Integrated Probabilistic Regulatory and Metabolic Network Analysis of Saccharomyces Cerevisiae for Biochemical Production

Wednesday, November 10, 2010: 1:10 PM
255 F Room (Salt Palace Convention Center)
Amit Ghosh1, Huimin Zhao2 and Nathan D. Price1, (1)Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois Urbana Champaign, Urbana, IL, (2)Chemical and Biomolecular Engineering, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL

In recent years the bio-based production of chemicals, fuels and materials has been receiving great attention. The microorganisms are isolated based on their capability to produce biochemicals that match our interest. The new field of system-based model guided metabolic engineering has emerged to generate new strains with improved productivity of biochemicals. Using metabolic networks, various gene deletion strategies are employed for improving the performance of the strain. Currently available computational methods for metabolic engineering are based on gene deletions and do not generally include the transcriptional regulatory networks that control metabolism. Thus, computational designs based on gene deletions can sometimes result in significant error in prediction. Using our new method Probabilistic Regulation of Metabolism (PROM), we constructed the genome-scale integrated metabolic-regulatory model for S. cerevisiae. The one to one mapping between transcription factor genes and metabolic genes are based on interaction probability obtained from high-throughput data. The integrated regulatory and metabolic model predicts the growth phenotypes of high-throughput knockout in good agreement with experimental data. The integrated model has been used for ethanol production in S. cerevisiae, and optimization strategies can be applied for improvement of other biochemicals based on genetic modifications of metabolic or regulatory genes.

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