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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Published on: February 3, 2023

Phenotype switching and mutations in random environments.

Drew Fudenberg¹, Lorens A Imhof

¹Department of Economics, Harvard University, Cambridge, MA 02138, USA.

Bulletin of Mathematical Biology

|September 9, 2011

Summary

This summary is machine-generated.

Stochastic phenotype switching allows cells to adapt to changing environments. Optimal switching rates depend on environmental change frequency, balancing adaptation and fitness.

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Area of Science:

Evolutionary Biology
Microbial Ecology
Systems Biology

Background:

Cell populations adapt to environmental changes by altering phenotypes.
Stochastic phenotype switching is a key mechanism for generating phenotypic diversity.
The relationship between switching rates and environmental dynamics is not fully understood.

Purpose of the Study:

To model the evolution of phenotype switching in finite populations under environmental shocks.
To determine how optimal switching rates are influenced by the frequency of environmental changes.
To analyze the trade-offs between switching rates and fitness in fluctuating environments.

Main Methods:

Developed a simple mathematical model for phenotype switching.
Simulated competing genotypes with varying switching rates in a changing environment.
Analyzed the impact of environmental shock frequency on optimal switching strategies.

Main Results:

Optimal switching rates mirror environmental change frequency when changes are rare (mutation-like).
When environments change frequently, optimal strategies involve maximizing fitness in the common environment or maximizing switching rates.
The optimal switching strategy is largely insensitive to the specific fitness values in each environment.

Conclusions:

Phenotype switching is a crucial evolutionary strategy for adapting to unpredictable environments.
Environmental change frequency is a primary driver of optimal stochastic switching rates.
The model provides insights into the evolution of bet-hedging strategies in microbial populations.