SCALABLE MODELING APPROACHES IN SYSTEMS IMMUNOLOGY

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dc.contributor.advisorLevy, Doronen_US
dc.contributor.advisorTsang, John Sen_US
dc.contributor.authorPark, Kyemyungen_US
dc.contributor.departmentBiophysics (BIPH)en_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-10-12T05:31:25Z
dc.date.available2020-10-12T05:31:25Z
dc.date.issued2020en_US
dc.description.abstractSystems biology seeks to build quantitative predictive models of biological system behavior. Biological systems, such as the mammalian immune system, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus, mechanistic, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with the high-dimensional parameter space arising when building reasonably detailed models. Another challenge is to devise integrated frameworks incorporating behavioral characteristics manifested at various organizational levels seamlessly. In this dissertation, I present two research projects addressing problems in immunological, or biological systems in general, using quantitative mechanistic models and machine learning, touching on the aforementioned challenges in scalable modeling. First, I aimed to understand how cell-to-cell heterogeneities are regulated through gene expression variations and their propagation at the single-cell level. To better understand detailed gene regulatory circuit models with many parameters without analytical solutions, I developed a framework called MAchine learning of Parameter-Phenotype Analysis (MAPPA). MAPPA combines machine learning approaches and stochastic simulation methods to dissect the mapping between high- dimensional parameters and phenotypes. MAPPA elucidated regulatory features of stochastic gene-gene correlation phenotypes. Next, I sought to quantitatively dissect immune homeostasis conferring tolerance to self-antigens and responsiveness to foreign antigens. Towards this goal, I built a series of models spanning from intracellular to organismal levels to describe the recurrent reciprocal relationships between self-reactive T cells and regulatory T cells in collaboration with an experimentalist. This effort elucidated critical immune parameters regulating the circuitry enabling the robust suppression of self-reactive T cells, followed by experimental validation. Moreover, by bridging these models across organizational scales, I derived a framework describing immune homeostasis as a dynamical equilibrium between self-activated T cells and regulatory T cells, typically operating well below thresholds that could result in clonal expansion and subsequent autoimmune diseases. I start with an introduction with a perspective linking seemingly contradictory behaviors of the immune system at different scales: microscopic “noise” and macroscopic deterministic outcomes. By connecting these aspects in the adaptive immune system analogously with an ansatz from statistical physics, I introduced a view on how robust immune homeostasis ensues.en_US
dc.identifierhttps://doi.org/10.13016/7hok-aaxq
dc.identifier.urihttp://hdl.handle.net/1903/26658
dc.language.isoenen_US
dc.subject.pqcontrolledImmunologyen_US
dc.subject.pqcontrolledBiophysicsen_US
dc.subject.pqcontrolledApplied mathematicsen_US
dc.subject.pquncontrolledComputational immunologyen_US
dc.subject.pquncontrolledImmune homeostasisen_US
dc.subject.pquncontrolledMachine learningen_US
dc.subject.pquncontrolledScalable modelingen_US
dc.subject.pquncontrolledSingle-cell heterogeneityen_US
dc.subject.pquncontrolledSystems biologyen_US
dc.titleSCALABLE MODELING APPROACHES IN SYSTEMS IMMUNOLOGYen_US
dc.typeDissertationen_US

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