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 Title: Stochastic many-body problems in ecology, evolution, neuroscience, and systems biology Author(s): Butler, Thomas C. Director of Research: Goldenfeld, Nigel D. Doctoral Committee Chair(s): Dahmen, Karin A. Doctoral Committee Member(s): Goldenfeld, Nigel D.; Thaler, Jonathan J.; Chemla, Yann R. Department / Program: Physics Discipline: Physics Degree Granting Institution: University of Illinois at Urbana-Champaign Degree: Ph.D. Genre: Dissertation Subject(s): Physics Statistical Mechanics Ecology Neuroscience Evolution Genetic Code Systems Biology Condensed Matter Physics Stochastic Processes Pattern Formation Abstract: Using the tools of many-body theory, I analyze problems in four different areas of biology dominated by strong fluctuations: The evolutionary history of the genetic code, spatiotemporal pattern formation in ecology, spatiotemporal pattern formation in neuroscience and the robustness of a model circadian rhythm circuit in systems biology. In the first two research chapters, I demonstrate that the genetic code is extremely optimal (in the sense that it manages the effects of point mutations or mistranslations efficiently), more than an order of magnitude beyond what was previously thought. I further show that the structure of the genetic code implies that early proteins were probably only loosely defined. Both the nature of early proteins and the extreme optimality of the genetic code are interpreted in light of recent theory as evidence that the evolution of the genetic code was driven by evolutionary dynamics that were dominated by horizontal gene transfer. I then explore the optimality of a proposed precursor to the genetic code. The results show that the precursor code has only limited optimality, which is interpreted as evidence that the precursor emerged prior to translation, or else never existed. In the next part of the dissertation, I introduce a many-body formalism for reaction-diffusion systems described at the mesoscopic scale with master equations. I first apply this formalism to spatially-extended predator-prey ecosystems, resulting in the prediction that many-body correlations and fluctuations drive population cycles in time, called quasi-cycles. Most of these results were previously known, but were derived using the system size expansion. I next apply the analytical techniques developed in the study of quasi-cycles to a simple model of Turing patterns in a predator-prey ecosystem. This analysis shows that fluctuations drive the formation of a new kind of spatiotemporal pattern formation that I name quasi-patterns." These quasi-patterns exist over a much larger range of physically accessible parameters than the patterns predicted in mean field theory and therefore account for the apparent observations in ecology of patterns in regimes where Turing patterns do not occur. I further show that quasi-patterns have statistical properties that allow them to be distinguished empirically from mean field Turing patterns. I next analyze a model of visual cortex in the brain that has striking similarities to the activator-inhibitor model of ecosystem quasi-pattern formation. Through analysis of the resulting phase diagram, I show that the architecture of the neural network in the visual cortex is configured to make the visual cortex robust to unwanted internally generated spatial structure that interferes with normal visual function. I also predict that some geometric visual hallucinations are quasi-patterns and that the visual cortex supports a new phase of spatially scale invariant behavior present far from criticality. In the final chapter, I explore the effects of fluctuations on cycles in systems biology, specifically the pervasive phenomenon of circadian rhythms. By exploring the behavior of a generic stochastic model of circadian rhythms, I show that the circadian rhythm circuit exploits leaky mRNA production to safeguard the cycle from failure. I also show that this safeguard mechanism is highly robust to changes in the rate of leaky mRNA production. Finally, I explore the failure of the deterministic model in two different contexts, one where the deterministic model predicts cycles where they do not exist, and another context in which cycles are not predicted by the deterministic model. Issue Date: 2011-01-14 URI: http://hdl.handle.net/2142/18436 Rights Information: Copyright 2010 Thomas C. Butler Date Available in IDEALS: 2011-01-14 Date Deposited: December 2
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