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Spatial Drug Heterogeneity Reshapes Population Response of Bacterial Pathogens
Spatial Drug Heterogeneity Reshapes Population Response of Bacterial Pathogens
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105237
- ISBN
- 9798291568019
- DDC
- 574.191
- 저자명
- Hu, Zhijian.
- 서명/저자
- Spatial Drug Heterogeneity Reshapes Population Response of Bacterial Pathogens
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 276 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Veatch, Sarah;Wood, Kevin.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약Pathogens such as bacteria inhabit spatially heterogeneous environments inside the human body, where nutrients, oxygen, and drug concentrations vary across scales-from individual cells to whole organs. These spatial differences can lead to surprising and sometimes contradictory outcomes. Compared to uniform environments, spatial drug heterogeneity can either promote bacterial growth or trigger population decline. It can also accelerate or delay resistance evolution, influence which mutants dominate, and affect diversity within microbial communities.In this thesis, I use a combination of theoretical modeling and experimental validation to study how spatial drug heterogeneity shapes bacterial population responses. I analyze both short-term dynamics like growth or decline, and long-term evolutionary outcomes such as resistance emergence and diversity patterns. My work is grounded in a minimal, deterministic mathematical framework suitable for large populations, allowing general predictions across many spatial conditions.In Chapter 2, I study spatial growth and decline in a one-dimensional setup using the largest eigenvalue of a linearized Fisher-KPP model. I experimentally validate the predicted responses and show how outcomes depend on migration rate, system size, and average growth rate.In Chapter 3, I generalize to networks of microhabitats and derive a new exact criterion for population response based on a regularized Laplacian kernel reweighted by local growth rates. This framework also informs possible spatial drug assignment strategies.Chapter 4 introduces an evolutionary model based on the Price equation, linking spatial growth rates and resistance levels to predict how quickly resistance evolves and which mutants dominate, especially under multi-drug combinations.In Chapter 5, I focus on a two-microhabitat system to explore how migration and fitness asymmetries influence bacterial diversity during drug resistance evolution. Both theory and experiments reveal a pattern satisfying the "Intermediate Disturbance Hypothesis"-a peak in diversity at intermediate migration levels.Together, these results advance our understanding of how spatial drug heterogeneity influences both ecological and evolutionary dynamics of bacterial populations. This thesis is the first attempt to systematically connect these diverse outcomes using a simple but general mathematical framework, supported by experimental evidence. The insights also provide a foundation for clinical strategies, such as optimizing spatial antibiotic dosing to better control infections.
- 일반주제명
- Biophysics
- 일반주제명
- Pharmacology
- 일반주제명
- Pathology
- 일반주제명
- Biochemistry
- 기타저자
- University of Michigan Biophysics
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202105237
■006m o d
■007cr#unu||||||||
■020 ▼a9798291568019
■035 ▼a(MiAaPQ)AAI32271966
■035 ▼a(MiAaPQ)umichrackham006531
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574.191
■1001 ▼aHu, Zhijian.
■24510▼aSpatial Drug Heterogeneity Reshapes Population Response of Bacterial Pathogens
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a276 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Veatch, Sarah;Wood, Kevin.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aPathogens such as bacteria inhabit spatially heterogeneous environments inside the human body, where nutrients, oxygen, and drug concentrations vary across scales-from individual cells to whole organs. These spatial differences can lead to surprising and sometimes contradictory outcomes. Compared to uniform environments, spatial drug heterogeneity can either promote bacterial growth or trigger population decline. It can also accelerate or delay resistance evolution, influence which mutants dominate, and affect diversity within microbial communities.In this thesis, I use a combination of theoretical modeling and experimental validation to study how spatial drug heterogeneity shapes bacterial population responses. I analyze both short-term dynamics like growth or decline, and long-term evolutionary outcomes such as resistance emergence and diversity patterns. My work is grounded in a minimal, deterministic mathematical framework suitable for large populations, allowing general predictions across many spatial conditions.In Chapter 2, I study spatial growth and decline in a one-dimensional setup using the largest eigenvalue of a linearized Fisher-KPP model. I experimentally validate the predicted responses and show how outcomes depend on migration rate, system size, and average growth rate.In Chapter 3, I generalize to networks of microhabitats and derive a new exact criterion for population response based on a regularized Laplacian kernel reweighted by local growth rates. This framework also informs possible spatial drug assignment strategies.Chapter 4 introduces an evolutionary model based on the Price equation, linking spatial growth rates and resistance levels to predict how quickly resistance evolves and which mutants dominate, especially under multi-drug combinations.In Chapter 5, I focus on a two-microhabitat system to explore how migration and fitness asymmetries influence bacterial diversity during drug resistance evolution. Both theory and experiments reveal a pattern satisfying the "Intermediate Disturbance Hypothesis"-a peak in diversity at intermediate migration levels.Together, these results advance our understanding of how spatial drug heterogeneity influences both ecological and evolutionary dynamics of bacterial populations. This thesis is the first attempt to systematically connect these diverse outcomes using a simple but general mathematical framework, supported by experimental evidence. The insights also provide a foundation for clinical strategies, such as optimizing spatial antibiotic dosing to better control infections.
■590 ▼aSchool code: 0127.
■650 4▼aBiophysics
■650 4▼aPharmacology
■650 4▼aPathology
■650 4▼aBiochemistry
■653 ▼aSpatial drug heterogeneity
■653 ▼aPopulation growth
■653 ▼aDrug resistance evolution
■690 ▼a0786
■690 ▼a0487
■690 ▼a0419
■690 ▼a0571
■71020▼aUniversity of Michigan▼bBiophysics.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359932▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
