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Title:Modeling for microbial communities with cross-feeding and predator-prey interactions
Author(s):Wang, Tong
Director of Research:Maslov, Sergei
Doctoral Committee Chair(s):Goldenfeld, Nigel
Doctoral Committee Member(s):Golding, Ido; O'Dwyer, James
Department / Program:Physics
Discipline:Physics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Microbial communities, mathematical modeling
Abstract:Microbial communities exist nearly everywhere on our planet, from extreme environments such as hydrothermal vents to host environments like the human gut, where microbes interact in diverse ways, such as resource competition and predator-prey interactions. In ecology, mathematical models have been proved effective in simulating diverse interactions and studying the stability of ecosystems under different interacting patterns and strengths. However, such a community-level understanding of microbial communities is still limited. This thesis focuses on two types of interactions: (1) cross-feeding interaction where a metabolite produced by one microbial species is consumed by another species and (2) predator-prey interaction between microbes and their viruses. The network composed of cross-feeding interactions between microbes can be utilized to systematically understand the mechanistic relationship between species and metabolites. Here, we proposed a trophic model comprising rounds of resource consumption and the following byproduct generation to predict metabolite levels according to the relative abundance of microbial species. Later, a machine learning algorithm compatible with the trophic model is used to computationally predict new cross-feeding interactions that are not yet tested by experiments. Besides, on the microscopic level of microbial metabolism, a biophysical model with two cross-feeding microbial strains modulated by the thermodynamics of the overflow pathway is investigated. Apart from the cross-feeding interactions, the predation of viruses on microbes acts as a top-down control of microbial abundances. When viruses infect migrating bacteria, whether the bacterial abundance is limited by nutrient capacity (nutrient-limited regime) or the viral predation (virus-limited regime) is not explored. To quantitatively explore possible regimes and the transition between different regimes, we designed a model based on partial differential equations with microbial ecology and evolution being considered to match and explain the experimental results. When bacteria fail to spatially escape from viral predation, they usually rely on immunity or resistance to survive. At the end of this thesis, the CRISPR-induced arms-race co-evolution between bacteria and viruses in a well-mixed environment is examined.
Issue Date:2021-04-13
Type:Thesis
URI:http://hdl.handle.net/2142/110794
Rights Information:Copyright 2021 Tong Wang
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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