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Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
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Nonequilibrium Theory for Adaptive Systems in Varying Environments.

Ying-Jen Yang, Charles D Kocher, Ken A Dill

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    Summary
    This summary is machine-generated.

    Organisms adapt to changing environments by balancing generalist traits and active tracking. Optimal strategies, like phenotypic memory, enhance fitness by anticipating environmental shifts, providing a physical theory of adaptivity.

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    Area of Science:

    • Physics
    • Biology
    • Ecology

    Background:

    • Biological organisms must adapt to unpredictable environmental changes to survive and thrive.
    • Understanding the physical basis of adaptivity is crucial for fields ranging from evolutionary biology to medicine.

    Purpose of the Study:

    • To develop a physical theory of adaptivity by analyzing the components of biological fitness in fluctuating environments.
    • To determine the optimal adaptive strategies for organisms and the optimal environmental control strategies for external agents.

    Main Methods:

    • Application of nonequilibrium physics principles to model organismal fitness.
    • Computation of fitness components, including static generalist and dynamic nonequilibrium tracking.
    • Derivation of optimal adaptive and environmental control strategies.

    Main Results:

    • Fitness comprises a static generalist component and a dynamic nonequilibrium tracking component.
    • Environmental changes require specific rates and magnitudes to be worth tracking; not all changes are beneficial to track.
    • Anticipatory tracking in coherent environments significantly enhances fitness.
    • Optimal strategies such as bet hedging and phenotypic memory were computed and explained.
    • Optimal environmental control strategies, applicable to drug regimen design, were also computed.

    Conclusions:

    • A unified physical framework for adaptivity connects fitness, adaptive strategy, and environmental variability.
    • The study provides a quantitative understanding of how biological systems adapt to dynamic environments.
    • The findings have implications for understanding evolution, ecology, and designing interventions in biological systems.