Sarah Sundius, QBioS Thesis Defense

In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Quantitative Biosciences
in the School of Mathematics

Sarah Sundius

Will defend her dissertation
NOVEL COMPUTATIONAL MODELS FOR BACTERIAL DYNAMICS IN COMMUNITY AND TREATMENT CONTEXTS

Friday, December 8th, 2023
1:00pm
U.A. Whitaker Building, Room 1232
https://gatech.zoom.us/j/94862841926?pwd=S2JsWnFtcytieGNuaDhzeUJ6U3dVQT09

Thesis Advisors:
Dr. Rachel Kuske, School of Mathematics, Georgia Institute of Technology
Dr. Sam Brown, School of Biological Sciences, Georgia Institute of Technology

Committee Members:
Dr. Leonid Bunimovich, School of Mathematics, Georgia Institute of Technology
Dr. Sung Ha Kang, 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 between bacteria and their environments on the population scale. Interactions in bacterial communities and infection environments are complex and present challenges for modeling, measurement, and inference. However, rising interest in microbiomes (multi-species microbial communities), increasing antimicrobial resistance, and the quest for novel therapeutic strategies to combat human bacterial infection, all center around being able to answer common questions: how do bacteria grow and interact with each other and their environments on the population level? How do they respond to external perturbation from antibiotic exposure or bacteriophage? Using a range of mathematical approaches, we address these questions by integrating forward models and data-driven methods to assess the impacts of underlying mechanisms, abiotic and biotic perturbations, and spatio-temporal heterogeneity as they relate to microbial dynamics in human infections.

Throughout this dissertation, we employ mathematical modeling as a tool to bridge gaps between theoretical and empirical microbiology, highlighting that many standard models and inference methods fail to capture qualitative and quantitative features of microbial dynamics. First, we challenge the received wisdom that antibiotic resistance genes always worsen treatment outcomes and should be strictly minimized. We mathematically explore the effects of ecological interactions on antibiotic treatment in a two lineage system of a pathogen and commensal, proposing an optimization approach to antibiotic resistance management. We define conditions for competitive release and “beneficial” commensal resistance---namely, when commensals inhibit pathogens---and demonstrate generality to resource explicit and spatially extended models. These results are conserved in a four-species experimental community with phage, showing that the addition of phage, targeting the dominant competitor in the community, leads to extinction of the dominant species, competitive release of the next strongest competitor, and maintenance of community diversity. Next, we present an iterative approach for understanding antibiotic and inoculum effects on bacterial growth and yield. Using fine-scale experimental data and a menu of standard population models, we conclude that both growth rate and yield are modified by antibiotic exposure and that populations exhibit distinct regimes of dynamical behavior given distinct exposure conditions. Finally, we expand our modeling into two-dimensional space, building an agent-based simulation of bacterial cells and aggregates to explore physical and socio-microbiology mechanisms underlying relationships between bacterial growth rate and aggregate size.

This work has important implications for both theoretical and empirical studies of microbial systems---evaluating and informing methods for sampling, inference, and modeling to efficiently capture underlying complexities of interactions between bacteria and their environments. In the study of human infection, we provide a baseline toolkit to develop improved treatment strategies for acute and chronic infections and to increase predictability of treatment outcomes.