Development of strategies based on genome-scale simulations for strain improvement

Abstract

For the development of industrial strains, random mutagenesis and selection approaches used traditionally, which might cause unknown genotypic/phenotypic changes and undesired mutations in the cells and difficulties for the reproduction of desired physiologies in the cells. Consequently, rational metabolic engineering has emerged as a standard strategy for strain development over the last couple of decades, which allows purposeful modification of metabolic and cellular characteristics by using recombinant DNA and other molecular biological techniques. However, these approaches also attained to some limitations in order to improve cellular performances dramatically in the extent of strain improvement because the scope of engineering the cell is often local, which manipulate only handful genes encoding enzymes and regulatory proteins, rather than system-wide. As systems biology advances as a new paradigm of research thanks to the development of genome-scale computational tools and high-throughput experimental technologies, metabolic engineering powered by the systems-level and large-scale genome wide analyses and computational tools, termed systems metabolic engineering, is now providing new rational and systematic ways for designing and developing strains having improved performances beyond traditional metabolic engineering. The advent of in silico genome-scale metabolic model has brought about the development of various algorithms to simulate the metabolic status of the cell as a whole. In silico genome-scale metabolic model and its simulation play increasingly important role in providing systematic strategies for metabolic engineering. In this thesis, I developed systems metabolic engineering strategies for the re-design of a cell (i.e., accurately predicting metabolic fluxes in genome-scale simulation, in silico gene deletion strategy, in silico gene amplification strategy, and regulating flux ratios at branch points in metabolic pathway) along with their applications based on genome-scale simulations. Also, I developed genome-scale metabolic model and genetic manipulation methods as metabolic tools for applications of the developed strategies. Chapter 1 is an overview of several in silico algorithms with genome-scale metabolic networks for systems metabolic engineering. Chapter 2 reports a strategy for accurate prediction of metabolic fluxes by combining genome-scale model with systematic and condition-independent constraints (i.e., grouping reaction constraints) that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses. Chapter 3 reports the development of an in silico object-oriented algorithm (i.e., FSCOF) to identify gene deletion targets efficiently for strain improvement and its applications of homo-organic acid production. Chapter 4 reports the development of in silico gene amplification strategy (i.e., FVSEOF with grouping reaction constraints) by genome-scale simulation. Chapter 5 reports the strategy for the redirection of the metabolic fluxes by regulating the metabolic flux ratio at branch points in metabolic pathway. Chapter 6 describes the reconstruction of genome-scale metabolic networks of industrially important microorganism, Ralstonia eutropha H16 for applications of developed strategies. Finally, Chapter 7 reports the re-design of a cell based on the developed systems metabolic engineering strategies. Appendix A and B describe the development of metabolic tools for applications of the developed strategies, which are genetic manipulation methods using sacB suicide vector and mobile group II intron. Results and methodologies presented in this thesis will facilitate the pipeline for systems metabolic engineering and improve our insights into biological systems.