Antimicrobial resistance (AMR) is a growing global threat to public health expected to impact 10 million people by 2050, with a disproportionate effect on low- and middle- income countries, that is further exacerbated in communities living in urban informal settlements and refugee camps. As a result, there is a heightened urgency to understand how current antibiotic use is driving the spread of drug resistance in communities with high population density and those that are in proximity to wastewater settings and environmentally contaminated surroundings. Currently, there is a limited quantitative and mechanistic understanding of the evolution and spread of multidrug resistant (MDR) pathogens in these complex settings where there are a multitude of antibiotic residues and bacterial species present. Computational and experimental work in this area can lead to predictive outcomes and more effective strategies to prevent outbreaks of resistant pathogens. The goal of this thesis was to develop and test an integrated mathematical modeling and high-throughput experimental approach to quantitatively analyze AMR evolution in complex environments. The mathematical model captures predicted behavior for systems with multiple antibiotic residues and metal ions, incorporating the effects of both antibiotic-antibiotic interactions and metal-antibiotic interactions. This model is rooted in fundamental principles of biological systems modeling and was continuously integrated with a novel experimental workflow utilizing the eVOLVER for rapid iterative model development and validation. This work has resulted in the development of a robust method of understanding and predicting the development and spread of MDR bacteria in complex environments and has the potential to provide robust strategies to protect the health of vulnerable populations in these environments.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46658 |
| Date | 30 August 2023 |
| Creators | Sutradhar, Indorica |
| Contributors | Zaman, Muhammad H. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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