Limits...
An Economic Framework of Microbial Trade.

Tasoff J, Mee MT, Wang HH - PLoS ONE (2015)

Bottom Line: Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities.Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum.This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

View Article: PubMed Central - PubMed

Affiliation: Department of Economics, Claremont Graduate University, Claremont, California, United States of America.

ABSTRACT
A large fraction of microbial life on earth exists in complex communities where metabolic exchange is vital. Microbes trade essential resources to promote their own growth in an analogous way to countries that exchange goods in modern economic markets. Inspired by these similarities, we developed a framework based on general equilibrium theory (GET) from economics to predict the population dynamics of trading microbial communities. Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities. We find that the economic concept of comparative advantage is a necessary condition for mutualistic trade. Our model suggests that microbial communities can grow faster when species are unable to produce essential resources that are obtained through trade, thereby promoting metabolic specialization and increased intercellular exchange. Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum. We experimentally tested this tradeoff using a synthetic consortium of Escherichia coli cells and found the results match the predictions of the model. This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

No MeSH data available.


Related in: MedlinePlus

Population dynamics.(a) Growth rate as a function of the log population ratio. When u1 < u2 the population ratio increases, and when u2 < u1 the population ratio decreases. When u1 = u2 the population ratio does not change. The steady state  is stable since small perturbations in the population ratio lead the population ratio back to . (b) Log population ratio over time after a perturbation at time step 0. Population converges back to SSS over time.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4519184&req=5

pone.0132907.g004: Population dynamics.(a) Growth rate as a function of the log population ratio. When u1 < u2 the population ratio increases, and when u2 < u1 the population ratio decreases. When u1 = u2 the population ratio does not change. The steady state is stable since small perturbations in the population ratio lead the population ratio back to . (b) Log population ratio over time after a perturbation at time step 0. Population converges back to SSS over time.

Mentions: The biotic equilibrium specifies the growth rate of both species at a single point in time. To capture the population dynamics over time, the population level is updated according to the equation, Ni(t + 1) = (1 + ui(xi(t)))Ni(t), where xi(t) denotes a cell of species i’s consumption at time t, and Ni(t) denotes the population level of species i at time t. While the model captures the population dynamics, however complex, we will focus on analyzing the steady-state outcomes in this paper. We define as the population ratio between species. As the population ratios change, the biotic equilibrium also changes. The population ratio is said to be in steady state, denoted by , when the growth rates of both species are equal. The steady state is stable, denoted by , if small perturbations in the population ratio lead the population to converge back to steady state. The formal definition of a stable steady state (SSS) is provided in Methods Section. Since many microbial communities exhibit SSS behavior, we focus on understanding these SSS processes. For a 2-member community, the steady state population ratio is stable when small decreases in the population ratio cause growth rate differences u2 > u1 and small increases cause u1 > u2, leading to convergence back to u1 = u2 = u* (Fig 4A). Sometimes, environmental stresses (e.g. exposure to antibiotics) can perturb the growth of one or more members of the community, leading to population disequilibria out of its steady state. The BGET model can thus be used to study the population dynamics as the community relaxes back to its stable steady state (Fig 4B). Various shock analyses can be applied to study the rate of reversion back to stable steady state after environmental perturbations.


An Economic Framework of Microbial Trade.

Tasoff J, Mee MT, Wang HH - PLoS ONE (2015)

Population dynamics.(a) Growth rate as a function of the log population ratio. When u1 < u2 the population ratio increases, and when u2 < u1 the population ratio decreases. When u1 = u2 the population ratio does not change. The steady state  is stable since small perturbations in the population ratio lead the population ratio back to . (b) Log population ratio over time after a perturbation at time step 0. Population converges back to SSS over time.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4519184&req=5

pone.0132907.g004: Population dynamics.(a) Growth rate as a function of the log population ratio. When u1 < u2 the population ratio increases, and when u2 < u1 the population ratio decreases. When u1 = u2 the population ratio does not change. The steady state is stable since small perturbations in the population ratio lead the population ratio back to . (b) Log population ratio over time after a perturbation at time step 0. Population converges back to SSS over time.
Mentions: The biotic equilibrium specifies the growth rate of both species at a single point in time. To capture the population dynamics over time, the population level is updated according to the equation, Ni(t + 1) = (1 + ui(xi(t)))Ni(t), where xi(t) denotes a cell of species i’s consumption at time t, and Ni(t) denotes the population level of species i at time t. While the model captures the population dynamics, however complex, we will focus on analyzing the steady-state outcomes in this paper. We define as the population ratio between species. As the population ratios change, the biotic equilibrium also changes. The population ratio is said to be in steady state, denoted by , when the growth rates of both species are equal. The steady state is stable, denoted by , if small perturbations in the population ratio lead the population to converge back to steady state. The formal definition of a stable steady state (SSS) is provided in Methods Section. Since many microbial communities exhibit SSS behavior, we focus on understanding these SSS processes. For a 2-member community, the steady state population ratio is stable when small decreases in the population ratio cause growth rate differences u2 > u1 and small increases cause u1 > u2, leading to convergence back to u1 = u2 = u* (Fig 4A). Sometimes, environmental stresses (e.g. exposure to antibiotics) can perturb the growth of one or more members of the community, leading to population disequilibria out of its steady state. The BGET model can thus be used to study the population dynamics as the community relaxes back to its stable steady state (Fig 4B). Various shock analyses can be applied to study the rate of reversion back to stable steady state after environmental perturbations.

Bottom Line: Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities.Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum.This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

View Article: PubMed Central - PubMed

Affiliation: Department of Economics, Claremont Graduate University, Claremont, California, United States of America.

ABSTRACT
A large fraction of microbial life on earth exists in complex communities where metabolic exchange is vital. Microbes trade essential resources to promote their own growth in an analogous way to countries that exchange goods in modern economic markets. Inspired by these similarities, we developed a framework based on general equilibrium theory (GET) from economics to predict the population dynamics of trading microbial communities. Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities. We find that the economic concept of comparative advantage is a necessary condition for mutualistic trade. Our model suggests that microbial communities can grow faster when species are unable to produce essential resources that are obtained through trade, thereby promoting metabolic specialization and increased intercellular exchange. Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum. We experimentally tested this tradeoff using a synthetic consortium of Escherichia coli cells and found the results match the predictions of the model. This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.

No MeSH data available.


Related in: MedlinePlus