Computational design of microbial strains for nongrowth-associated production of antibiotics and oleochemicals

Abstract

Over the past decade, a number of genome-scale models of metabolism (GEMs) and computational strain optimization methods (CSOMs) have been developed to guide metabolic engineering of microbial strains for the production of valuable chemicals. In this thesis, the approach has been extended to be applicable for computational design of microbial strains for nongrowth-associated production. Production of antibiotics in Streptomyces coelicolor and oleochemicals in Yarrowia lipolytica has been investigated using the developed computational tools. First, antibiotics production in S. coelicolor has been studied by reconstructing a high-quality GEM for S. coelicolor, designated iMK1208. It has been verified that iMK1208 can be used for designing an antibiotic overproducing strain using an existing CSOM named flux scanning based on enforced objective flux. To more precisely design antibiotic overproducers by considering regulatory constraints which governing the production of antibiotics, two new CSOMs, transcriptomics-based strain optimization tool (tSOT) and beneficial regulator targeting (BeReTa), have been devised. tSOT identifies metabolic gene overexpression targets by considering regulatory states through the integration of transcriptomic data into a GEM. On the other hand, BeReTa prioritizes transcriptional regulator manipulations for target chemical production by using transcriptional regulatory network model together with a GEM. Finally, tSOT and BeReTa have been successfully applied for designing antibiotic overproducing strains of S. coelicolor using iMK1208. Second, to examine oleochemicals production in Y. lipolytica, environmental version of minimization of metabolic adjustment (eMOMA) method has been developed. After confirming that the eMOMA method can be used for predicting lipid accumulation as well as metabolic fluxes of Y. lipolytica, the method has been further applied to find metabolic engineering strategies for the improved lipid production. Using the eMOMA method, several known targets as well as novel non-intuitive targets for lipid overproduction have been successfully identified. In short, the GEM and CSOMs presented herein would be powerful tools for guiding the development of production hosts for nongrowth-associated products.