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Modeling microbial metabolic trade-offs in a chemostat.
Li, Zhiyuan; Liu, Bo; Li, Sophia Hsin-Jung; King, Christopher G; Gitai, Zemer; Wingreen, Ned S.
Afiliação
  • Li Z; Center for Quantitative Biology, Peking University, Beijing, China.
  • Liu B; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
  • Li SH; Center for the Physics of Biological Function, Princeton University, Princeton, New Jersey, United States of America.
  • King CG; Princeton Center for Theoretical Science, Princeton University, Princeton, New Jersey, United States of America.
  • Gitai Z; Yuanpei College, Peking University, Beijing, China.
  • Wingreen NS; Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America.
PLoS Comput Biol ; 16(8): e1008156, 2020 08.
Article em En | MEDLINE | ID: mdl-32857772
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bactérias / Reatores Biológicos / Modelos Biológicos Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bactérias / Reatores Biológicos / Modelos Biológicos Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China