Development of in silico metabolic flux analysis methods for metabolic engineering

Abstract

Environmental problems such as oil depletion and climate change cause tremendous issues in the petrochemical industry, which led to increasing attention of systems metabolic engineering that uses renewable biomass and produce chemicals through biology. Systems metabolic engineering is a field of study where metabolic networks of microorganisms are manipulated to produce biofuels, polymers and pharmaceuticals. With the growth of this discipline, in silico computational methods has also been developed as well, which provide metabolic strategies to increase production of target chemicals based on simulation of thousands of reactions at systems-level. In this thesis, novel in silico strategies are developed and confirmed to increase production of target chemicals. In Chapter 1, basics of the three in silico strategies (i.e., elementary mode analysis, constraint-based analysis of genome-scale model and $^{13}C$-metabolic flux analysis) will be explained and genome-scale metabolic models will be introduced as well. In addition, Chapter 2 will describe increased production of succinic acid in Mannheimia succiniciproducens through elementary mode analysis with clustering method. Combined elementary mode analysis and clustering method were experimentally demonstrated through metabolic engineering of M. succiniciproducens. Chapter 3, iBridge analysis based on metabolite-centric analysis method was adopted rather than reaction-centric method, which is originally used in constraint-based flux analysis, to offer novel engineering strategies to improve production of 298 commercial chemicals in Escherichia coli. Among the 298 chemicals, overproduction of D-panthenol and putrescine was experimentally demonstrated in E. coli. iBridge should be useful for exploring a wide range of effective metabolic reactions. Chapter 4 deals with the in silico analysis based on omics data. Methodology was constructed to analyze $^{13}C$-metabolic flux analysis and was applied to metabolic engineering. Such results are summarized together in Chapter 5 as a conclusion.