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Quantitative Approaches for Studying the Effects of Stressors in the Growth of Living Organisms


Living organisms encounter multiple stressors that reduce their growth. These include physical stressors, like changes in temperature and pressure, and chemical stressors such as poisons or antibiotics. This dissertation presents various mathematical and computational approaches to the study of the effects of stressors in living organisms, with a focus on antibiotic and temperature interactions. The first chapter of this dissertation consists of introductory material presenting the background needed to understand the contents of the later chapters. Chapters 2 through 4 consist of projects done in collaboration with the Pamela Yeh lab at UCLA, where we focus on combining quantitative approaches with experimental data to explore the interactions between the effects of antibiotics and temperature in the growth of bacteria. In the second chapter, we find groups of antibiotics that damage bacteria in a similar way to either high or low temperatures through network clustering methods. In the third chapter, we develop a flexible mathematical model with biologically interpretable parameters for describing temperature response curves. In the fourth chapter, we then apply this model to study the temperature dependence of E. coli growth under the presence of antibiotics, applying a Bayesian approach to infer the model parameters. We find that heat-similar and cold-similar antibiotic groups tend to shift the optimal temperature for growth in opposite directions, suggesting a similar damage hypothesis, where growth is reduced more sharply at temperatures where the antibiotic and temperature-induced damage to the bacteria overlap. Finally, in the fifth chapter we present work on a mathematical model for the evolution of stress responses, and show results regarding the favorability of evolving stress responses to stressors that primarily affect either the growth rate or death rate of a living organism. The mathematical techniques relevant to this dissertation span network theory, Bayesian statistics, and mathematical modeling. The biological impact of this work lies in an increased understanding of how overlap in the physiological damage caused by different stressors influences their joint effects in the growth of living organisms and the emergence of cross-sensitivity and cross-resistance, as well as a theoretical framework to explore the tradeoffs in the evolution of stress responses.

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