Pan-reactome analysis of Streptomyces strains through a high-throughput genome-scale metabolic modeling

Abstract

Bioproduction of novel natural products can contribute to sustainable development. Among various approaches of systems biology for producing them, genome-scale models (GEMs) have been found to be effective in predicting the comprehensive pattern of metabolism in organisms. In this thesis, GEMs of Streptomyces strains known for representative sources of secondary metabolites were reconstructed through our modeling program based on their genome data, and the pan-reactome of strains was analyzed. In addition, the utility of GEM was proved by applying it to the overproduction of pikromycin. This thesis is expected to provide helpful information for metabolic engineering to optimize production of secondary metabolites. For future works, modeling frameworks for more complex biological networks than metabolic network should be studied to predict biological processes more accurately.