Quantitative Biosciences Thesis Proposal
Sarah Sundius
School of Mathematics
Advisors:
Dr. Rachel Kuske (School of Mathematics)
Dr. Sam Brown (School of Biological Sciences)
Open to the Community
Novel quantitative approaches for understanding microbial dynamics and interactions in
heterogeneous environments
Monday, October 5th, 2020, 1:00pm
Occurring online via BlueJeans (URL: https://bluejeans.com/395423643)
Committee Members:
Dr. Leonid Bunimovich, School of Mathematics; Georgia Institute of Technology
Dr. Marvin Whiteley, School of Biological Sciences; Georgia Institute of Technology
Abstract:
Microbes are key players in human health and disease; however, there is much debate over the nature, consequences, and importance of interactions among lineages. Commonly used pairwise mathematical models, such as the generalized Lotka-Volterra framework, are partially successful in understanding community assembly, fluctuations, and persistence. However, these models have limitations related to key factors, as they rely on a single positive (faciliatory) or negative (inhibitory) parameter to describe all of the complexities in the impact of one species on another. They also assume environmental homogeneity, despite the fact that biological systems are stochastic and rarely well-mixed spatially, and pattern formation is regularly observed. In this thesis, I focus on simple two lineage systems, in the context of human infection, with the goal of providing an improved theoretical foundation for the study of infection microbiomes—polymicrobial communities resulting in chronic infections—, using chronic lung infections in people with Cystic fibrosis as model. Using a range of mathematical approaches, integrating forward models and data-driven methods, I aim to understand how heterogeneity in microbial systems impacts ecological outcomes, and to inform sampling, inference, and modeling methods to efficiently capture molecular and regulatory complexities of interactions. In the study of human infection, this work will provide a critical baseline toolkit to develop improved treatment methods for polymicrobial infections and improve predictability of treatment outcomes.
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