Reverse engineering homeostasis in molecular biological systems
Quo, Chang Feng
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This dissertation is an initial study of how modern engineering control may be applied to reverse engineer homeostasis in metabolic pathways using high-throughput biological data. This attempt to reconcile differences between engineering control and biological homeostasis from an interdisciplinary perspective is 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 achieving a true meeting of minds between engineers and biologists. To do this, a comparator model is developed and applied to model the effect of single-gene (SPT) overexpression on C16:0 sphingolipid de novo biosynthesis in vitro, specifically to simulate and predict potential homeostatic pathway interactions between the sphingolipid metabolites. Sphingolipid de novo biosynthesis is highly regulated because its pathway intermediates are highly 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. To complete the modeling paradigm, wild type cells are defi ned as the reference that exhibits the "desired" pathway dynamics that the treated cells approach. Key model results show that the proposed modern engineering control approach using a comparator to reverse engineer homeostasis in metabolic systems is: (a) eff ective in capturing observed pathway dynamics from experimental data, with no signifi cant di fference 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) biologically relevant in terms of predicting steady-state feedback as a result of homeostasis that is verifi ed in literature and with additional independent data from drug dosage experiments.