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Systems Approaches to Infer Microbial Community Interactions and Their Impacts on Ecosystem Function.
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Systems Approaches to Infer Microbial Community Interactions and Their Impacts on Ecosystem Function.
자료유형  
 학위논문
Control Number  
0017160908
International Standard Book Number  
9798383219522
Dewey Decimal Classification Number  
576
Main Entry-Personal Name  
Carr, Alex.
Publication, Distribution, etc. (Imprint  
[S.l.] : University of Washington., 2024
Publication, Distribution, etc. (Imprint  
Ann Arbor : ProQuest Dissertations & Theses, 2024
Physical Description  
133 p.
General Note  
Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
General Note  
Advisor: Baliga, Nitin.
Dissertation Note  
Thesis (Ph.D.)--University of Washington, 2024.
Summary, Etc.  
요약Microbes and the communities they form play critical roles in ecosystem function, from facilitating the biogeochemical cycling of essential nutrients, such as carbon, nitrogen, and sulfur, to acting as the foundation of complex food webs. Microbes also play important roles as eukaryotic symbionts, where they can have profound effects on host fitness. Thus, understanding how microbial community interactions and environmental context shape the functional capabilities of microbiomes is of vital importance if we want to engineer these systems to address challenges in human health and the health of natural ecosystems. Here, I show how systems approaches can be leveraged to overcome the inherent limitations of inferring microbial community interactions directly from correlation structure, which has been the standard approach in the microbiome field. Specifically, I highlight how multi-omic characterization, MCMMs, and synthetic communities (SynComs) can be leveraged to better understand niche competition between nitrate-reducing bacteria and sulfate-reducing bacteria in oxygen-depleted ecosystems, the importance of pathway partitioning in nitrate-reducing communities, and the role of nitrate-reducing communities in nitrous oxide emissions. I also highlight how microbial community-scale metabolic models (MCMMs) can be leveraged to predict Clostridioides difficile (C. difficile) colonization in the human gut microbiome, provide mechanistic insights into the niche of C. difficile across different community contexts, and assess probiotic interventions designed to inhibit C. difficile growth.
Subject Added Entry-Topical Term  
Microbiology.
Subject Added Entry-Topical Term  
Ecology.
Subject Added Entry-Topical Term  
Systematic biology.
Subject Added Entry-Topical Term  
Environmental science.
Subject Added Entry-Topical Term  
Analytical chemistry.
Index Term-Uncontrolled  
Denitrification
Index Term-Uncontrolled  
Human gut microbiome
Index Term-Uncontrolled  
Metabolic modeling
Index Term-Uncontrolled  
Microbial ecology
Index Term-Uncontrolled  
Synthetic communities
Added Entry-Corporate Name  
University of Washington Molecular Engineering and Sciences
Host Item Entry  
Dissertations Abstracts International. 86-01B.
Electronic Location and Access  
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Control Number  
joongbu:657218
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