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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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Committee:
Dr. May D Wang, BME, Thesis advisor
Dr. Leonid Bunimovich, MATH
Dr. Melissa Kemp, BME
Dr. Alfred H Merrill, Jr., BIOL
Dr. Mark Prausnitz, ChBE
Motivated not only by the observation that robust behavior in metabolic pathways resembles stabilized dynamics in controlled systems, but also by the challenges forewarned in interdisciplinary research between engineering and biology, this dissertation is an initial study of how engineering control may be applied to model homeostasis in metabolic pathways.
In particular, a comparator model is developed and applied to model the effect of SPT overexpression on C16:0 sphingolipid de novo biosynthesis in vitro, specifically to simulate and predict potential homeostatic pathway interactions between the pathway metabolites, using the wild type pathway dynamics as a reference for the treated cells. Sphingolipid de novo biosynthesis is highly regulated because its pathway intermediates are bioactive. Alterations in sphingolipid synthesis, storage, and metabolism are implicated in human diseases. In addition, when variation in structure is considered, sphingolipids are one of the most diverse and complex families of biomolecules.
Key outcomes show that the proposed approach to model homeostasis in metabolic systems using a comparator is: (a) effective in capturing observed pathway dynamics from experimental data, with no significant difference in precision from existing models, (b) robust to potential errors in estimating state-space parameters as a result of sparse data, (c) generalizable to model other metabolic systems, as demonstrated by testing on a separate independent dataset, and (d) through verification from literature and additional experimental data, biologically relevant in terms of predicting steady-state feedback as a result of homeostasis.
