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Modeling microbial metabolic trade-offs in a chemostat

Author(s): Li, Zhiyuan; Liu, Bo; Li, Sophia Hsin-Jung; King, Christopher G; Gitai, Zemer; et al

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Abstract: Microbes face intense competition in the natural world, and so need to wisely allocate their resources to multiple functions, in particular to metabolism. Understanding competition among metabolic strategies that are subject to trade-offs is therefore crucial for deeper insight into the competition, cooperation, and community assembly of microorganisms. In this work, we evaluate competing metabolic strategies within an ecological context by considering not only how the environment influences cell growth, but also how microbes shape their chemical environment. Utilizing chemostat-based resource-competition models, we exhibit a set of intuitive and general procedures for assessing metabolic strategies. Using this framework, we are able to relate and unify multiple metabolic models, and to demonstrate how the fitness landscape of strategies becomes intrinsically dynamic due to species-environment feedback. Such dynamic fitness landscapes produce rich behaviors, and prove to be crucial for ecological and evolutionarily stable coexistence in all the models we examined.
Publication Date: 28-Aug-2020
Citation: Li, Zhiyuan, Liu, Bo, Li, Sophia Hsin-Jung, King, Christopher G, Gitai, Zemer, Wingreen, Ned S. (2020). Modeling microbial metabolic trade-offs in a chemostat. PLoS computational biology, 16 (8), e1008156 - e1008156. doi:10.1371/journal.pcbi.1008156
DOI: doi:10.1371/journal.pcbi.1008156
ISSN: 1553-734X
EISSN: 1553-7358
Pages: e1008156 - e1008156
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: PLoS Computational Biology
Version: Final published version. This is an open access article.

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