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Computational Thermodynamic and Biosynthetic Analysis of Genome-scale Metabolic Models

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The use of processes based on microorganisms to produce fuels, monomers, and pharmaceuticals from renewable feedstocks is increasing today due to their environmental and economic advantages over petrochemical-based processes. In order to unlock the full potential of microorganisms to produce a sustainable industry centered on biosynthesis, an improved understanding of the complex and highly coupled biochemical processes taking place within metabolism is required. The ability to design and optimize new biosynthetic pathways to expand on the biosynthetic capabilities of microorganisms must also be improved. This thesis work focused on the development and application of computational methods for improving the understanding of cell metabolism and utilizing that understanding toward the design of new biosynthetic pathways. First, thermodynamics was applied to predict ranges for intracellular fluxes, metabolite activities, and reaction driving force (ΔrG') using a new form of constraints-based modeling called thermodynamics-based metabolic flux analysis (TMFA). TMFA was applied to study the iJR904 metabolic model of E. coli, and a group contribution method was utilized to estimate the standard Gibbs free energy change (ΔrG'°) for the reactions in the iJR904 model to impose the thermodynamic constraints of TMFA. To improve estimations of ΔrG'°, a new group contribution method was also developed that has enhanced accuracy and wider applicability compared to previous methods. In the final portion of this thesis work, the Biochemical Network Integrated Computational Explorer (BNICE) was applied to generate thousands of novel pathways from pyruvate to 3-hydroxypropoate. Pathway evaluation methods based on TMFA were applied to identify the four most promising pathways to 3HP, (three of which are novel pathways), based on pathway length, yield of 3HP, and maximum 3HP concentration. All pathways were generated using 86 generalized reaction rules developed for BNICE based on the types of chemical transformations known to be catalyzed by enzymes. These reaction rules were demonstrated to be capable of reproducing 47% of the reactions in the iJR904 model and 50% of the reactions in the KEGG database, indicating the breadth of biochemistry covered by these diverse reaction rules

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  • 06/01/2018
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