Novel systems biology methods for the analysis, optimization, and visualization of kinetic and genome-scale stoichiometric models
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Date
2022-01-01
Type of Work
Department
Biological Sciences
Program
Biological Sciences
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan through a local library, pending author/copyright holder's permission.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Abstract
Genetic and metabolic engineering has made it possible to use microbial organisms as systems that can be engineered for various applications such as bio-product generation and bioremediation. Being able to reverse-engineer key components of these microbial organisms can pave the way for multiple applications in science and engineering. However, understanding the dynamics and regulation of metabolic networks is a current challenge due to their size and complexity.Integrative systems biology uses a bottom-up approach for reverse-engineering critical biological components and their interactions, with the goal to explain why and how a cell or whole organism exhibits a phenotype. This approach can create mechanistic comprehensive models that have the ability to recapitulate cellular growth and metabolic fluxes essential for formulating quantitative predictions. Extracting such metabolic models however is currently limited to small datasets typically restricted to growth in a single substrate, no genetic manipulations, and no dynamic regulation. In addition,
visualizing, merging, and comparing large metabolic models, especially those at the genome scale, is another current challenge in the field.
This dissertations developed novel systems biology methods for the analysis, optimization, and visualization of kinetic and genome-scale stoichiometric models. For kinetic modeling, we proposed novel mathematical formalisms to describe metabolic reactions, enzyme expression, and cell biomass to create comprehensive models that can account for the growth dynamics on multiple substrates as well as under genetic perturbations. Furthermore, we developed automatic optimization methods that successfully reverse-engineered the unknown kinetic parameters to fit the model to experimental multiomic datasets. For stoichiometric modeling, we developed novel interactive and user-friendly approaches based on flux analysis to solve the topological problem of visualizing and understanding metabolic networks comprising all the reactions (genome-scale) occurring within a cell or organism. In addition, we proposed a methodology and developed a tool to merge and compare such models, solving the current challenge of reconciling incompatible identifier systems.
We applied these novel approaches to reverse-engineer and optimize metabolic pathways towards biofuel and bio-product production from renewable sources. Although plants are one of the largest sources of sugars, the physical configuration of the saccharide polymers makes the sugar monomers difficult to retrieve. Cellvibrio japonicus is a Gram-negative saprophytic bacterium able to overcome the polymeric recalcitrance of plant polysaccharides by synergistically employing a diverse set of enzymes, the mechanism and regulation of which are not completely understood. Using the tools developed in this thesis, we discovered both a kinetic model able to correctly predict non-diauxic growth dynamics
and a stochiometric model accounting for a diverse set of carbon sources, demonstrating the applicability of our proposed novel methods.