First Thesis Advisor
David Paydarfar, MD
Biological Models, Physiological Adaptation, Theoretical Models, Statistical Models, Signal Transduction, Algorithms, Physiological Feedback
Dissertations, UMMS; Models, Biological; Adaptation, Physiological; Models, Theoretical; Models, Statistical; Signal Transduction; Algorithms; Feedback, Physiological
Switches play an important regulatory role at all levels of biology, from molecular switches triggering signaling cascades to cellular switches regulating cell maturation and apoptosis. Medical therapies are often designed to toggle a system from one state to another, achieving a specified health outcome. For instance, small doses of subpathologic viruses activate the immune system’s production of antibodies. Electrical stimulation revert cardiac arrhythmias back to normal sinus rhythm. In all of these examples, a major challenge is finding the optimal stimulus waveform necessary to cause the switch to flip. This thesis develops, validates, and applies a novel model-independent stochastic algorithm, the Extrema Distortion Algorithm (EDA), towards finding the optimal stimulus. We validate the EDA’s performance for the Hodgkin-Huxley model (an empirically validated ionic model of neuronal excitability), the FitzHugh-Nagumo model (an abstract model applied to a wide range of biological systems that that exhibit an oscillatory state and a quiescent state), and the genetic toggle switch (a model of bistable gene expression). We show that the EDA is able to not only find the optimal solution, but also in some cases excel beyond the traditional analytic approaches. Finally, we have computed novel optimal stimulus waveforms for aborting epileptic seizures using the EDA in cellular and network models of epilepsy. This work represents a first step in developing a new class of adaptive algorithms and devices that flip biological switches, revealing basic mechanistic insights and therapeutic applications for a broad range of disorders.
Chang JT. (2015). Flipping Biological Switches: Solving for Optimal Control: A Dissertation. GSBS Dissertations and Theses. https://doi.org/10.13028/M2N01D. Retrieved from https://escholarship.umassmed.edu/gsbs_diss/763
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