Linking Molecular Mechanisms to Population-level Behavior using Mathematical Models of Pseudomonas aeruginosa Quorum Sensing
Author(s)
Thomas, Stephen
Advisor(s)
Brown, Sam P.
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Abstract
Summary
Bacterial cells of many species communicate with each other by exchanging diffusible signal molecules. This mechanism, known as quorum sensing, has been well-studied at the level of specific molecular interactions. We have a rich understanding of the molecular mechanisms underlying the processes of signal production and signal response. What is less clear is how quorum sensing operates at the population level in defined environmental contexts. How can we characterize population-level quorum sensing responses to defined environmental variables? What types of responses are possible? What are the important constraints on those responses?
The three chapters that follow contribute answers to some of those questions. They focus on characterizing and modeling the overall population response to environmental variation mediated by quorum sensing, and how heterogenous individual cells contribute systematically to that response. Chapter 2 demonstrates that both populations and individual cells are not limited to the on or off reaction implied by simple quorum sensing narratives. Rather, cells and populations can fine-tune their behavior to changing environments, giving them a richer repertoire of responses. We show that increasing stationary phase density leads to increasing bimodality in single-cell expression of QS controlled protease (lasB ), which generates a linear functional response to increasing density on the population scale. Chapter 3 shows how multiple quorum sensing systems within a species can interact in distinct ways, and how the specific form of those interactions can significantly affect their behavior. In the case of P. aeruginosa we demonstrate that the las and rhl systems do not follow the canonical hierarchical arrangement and instead form a biased reciprocal network. We go on to show that this architecture enhances sensitivity to changes in density and increases robustness to changes in mass transfer. Chapter 4 tracks the expression of multiple QS genes with single cell resolution and reveals temporal diversity in patterns of gene expression across cells. We evaluate potential ways this cellular heterogeneity could provide fitness benefits via bet-hedging and division of labor. Finally, Chapter 5 discusses key elements common to all of the chapters, and it outlines a path for future research that can extend their findings.
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Date
2023-01-10
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Dissertation