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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor:
Mark P. Styczynski, Ph.D. (ChBE, Georgia Institute of Technology)
Committee Members:
Fani Boukouvala, Ph.D. (ChBE, Georgia Institute of Technology)
Melissa Kemp, Ph.D. (BME, Georgia Institute of Technology)
Andrew Medford, Ph.D. (ChBE, Georgia Institute of Technology)
Eberhard Voit, Ph.D. (BME, Georgia Institute of Technology)
Computational modeling of metabolic pathways toward predicting dynamic phenotypes
Metabolic systems are important to a wide variety of applications, including therapeutic development, agricultural crop production, and manufacturing of industrial chemicals. Developing metabolic models is one of the best approaches to study metabolism, as computational experiments are generally cheaper and faster to perform than experiments in a laboratory. While there are computational frameworks that can model large metabolic systems at steady state or the metabolite dynamics of a small number of key metabolic pathways, it is substantially more difficult to model the dynamics of metabolism at the genome scale. In this thesis dissertation, I present three computational platforms that address several of the challenges in developing dynamic genome-scale metabolic models. First, I devised a stepwise machine learning strategy for identifying the regulatory topology within metabolic systems, which can be used to construct more accurate metabolic models. I then developed a framework for inferring absolute concentrations from relative abundances in metabolomics data, which will allow metabolomics (the systems-scale study of metabolites) to be more easily used with metabolic modeling tools. Finally, I implemented new constraints within a linear programming dynamic modeling framework that increase its ability to model a wider variety of metabolic systems. Together, these three platforms create a cohesive workflow for modeling the dynamics of metabolism at any scale.
