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Quantifying the Organization and Dynamics of Excitable Signaling Networks

dc.contributor.advisorLosert, Wolfgangen_US
dc.contributor.authorCampanello, Leonard Josephen_US
dc.date.accessioned2020-10-12T05:32:10Z
dc.date.available2020-10-12T05:32:10Z
dc.date.issued2020en_US
dc.identifierhttps://doi.org/10.13016/o9ng-5js4
dc.identifier.urihttp://hdl.handle.net/1903/26663
dc.description.abstractThe transmission of extracellular information through intracellular signaling networks is ubiquitous in biology---from single-celled organisms to complex multicellular systems. Via signal-transduction machinery, cells of all types can detect and respond to biological, chemical, and physical stimuli. Although studies of signaling mechanisms and pathways traditionally involve arrays of biochemical assays, detailed quantification of physical information is becoming an increasingly important tool for understanding the complexities of signaling. With the rich datasets currently being collected in biological experiments, understanding the mechanisms that govern intracellular signaling networks is becoming a multidisciplinary problem at the intersection of biology, computer science, physics, and applied mathematics. In this dissertation, I focus on understanding and characterizing the physical behavior of signaling networks. Through analysis of experimental data, statistical modeling, and computational simulations, I explore a characteristic of signaling networks called excitability, and show that an excitable-systems framework is broadly applicable for explaining the connection between intracellular behaviors and cell functions. One way to connect the physical behavior of signaling networks to cell function is through the structural and spatial analyses of signaling proteins. In the first part of this dissertation, I employ an adaptive-immune-cell model with a key activation step that is both promoted and inhibited by a microns-long, filamentous protein complex. I introduce a novel image-based bootstrap-like resampling method and demonstrate that the spatial organization of signaling proteins is an important contributor to immune-cell self regulation. Furthermore, I use the bootstrap-like resampling to demonstrate that the location of contact points between signaling proteins can provide mechanistic insight into how signal regulation is accomplished on the single-cell level. Finally, I probe the excitable dynamics of the system with a Monte Carlo simulation of nucleation-limited growth and degradation. Using the simulations, I show that careful balance between simulation parameters can elicit a tunable response dynamic. The spatiotemporal dynamics of signaling components are also important mediators of cell function. One key readout of the connection between signaling dynamics and cell function is the behavior of the cytoskeleton. In the second part of this dissertation, I use innate-immune-cell and epithelial-cell models to understand how a key cytoskeletal component, actin, is influenced by topographical features in the extracellular environment. Engineered nanotopographic substrates similar in size to typical extracellular-matrix structures have been shown to bias the flow of actin, a concept known as esotaxis. To measure this bias, I introduce a generalizable optical-flow-based-analysis suite that can robustly and systematically quantify the spatiotemporal dynamics of actin in both model systems. Interestingly, despite having wildly different migratory phenotypes and physiological functions, both cell types exhibit quantitatively similar topography-guidance dynamics which suggests that sensing and responding to extracellular textures is an evolutionarily-conserved phenomena. The signaling mechanisms that enable actin responses to the physical environment are poorly understood. Despite experimental evidence for the enhancement of actin-nucleation-promoting factors (NPFs) on extracellular features, connecting texture-induced signaling to overall cell behavior is an ongoing challenge. In the third part of this dissertation, I study the topography-induced guidance of actin in amoeboid cells on nanotopographic textures of different spacings. Using optical-flow analysis and statistical modeling, I demonstrate that topography-induced guidance is strongest when the features are similar in size to typical actin-rich protrusions. To probe this mechanism further, I employ a dendritic-growth simulation of filament assembly and disassembly with realistic biochemical rates, NPFs, and filament-network-severing dynamics. These simulations demonstrate that topography-induced guidance is more likely the result of a redistribution, rather than an enhancement, of NPF components. Overall, this dissertation introduces quantitative tools for the analysis, modeling, and simulations of excitable systems. I use these tools to demonstrate that an excitable-systems framework can provide deep, phenomenological insights into the character, organization, and dynamics of a variety of biological systems.en_US
dc.language.isoenen_US
dc.titleQuantifying the Organization and Dynamics of Excitable Signaling Networksen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentPhysicsen_US
dc.subject.pqcontrolledPhysicsen_US
dc.subject.pqcontrolledBiophysicsen_US
dc.subject.pqcontrolledBiologyen_US


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