TY - JOUR
T1 - Modeling microbial metabolic trade-offs in a chemostat
AU - Li, Zhiyuan
AU - Liu, Bo
AU - Li, Sophia Hsin Jung
AU - King, Christopher G.
AU - Gitai, Zemer
AU - Wingreen, Ned S.
N1 - Funding Information:
Funding: This work was supported by the National Institutes of Health Grant R01GM082938 (NW, nih. org) and by the National Science Foundation, through the Center for the Physics of Biological Function (nsf.org, PHY-1734030).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
Copyright: © 2020 Li et al
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1008156
DO - 10.1371/journal.pcbi.1008156
M3 - Article
C2 - 32857772
AN - SCOPUS:85090791485
SN - 1553-734X
VL - 16
JO - PLoS computational biology
JF - PLoS computational biology
IS - 8
M1 - e1008156
ER -