Nonequilibrium Theory for Adaptive Systems in Varying Environments

TL;DR

This paper develops a nonequilibrium physics framework to understand biological adaptation, highlighting how organisms balance static generalism and dynamic tracking in changing environments, with implications for control strategies.

Contribution

It introduces a theoretical model linking adaptation components to environmental variability and switching times, offering new insights into optimal strategies and control of adaptive systems.

Findings

Tracking gain scales with environmental variability and switching time-scales

Optimal bet-hedging and phenotypic memory arise from the interplay of adaptation components

Control strategies can target robustness and tracking to suppress pathogens

Abstract

Biological organisms are adaptive, able to function in unpredictably changing environments. Drawing on recent nonequilibrium physics, we show that in adaptation, fitness has two components parameterized by observable coordinates: a static Generalism component characterized by state distributions, and a dynamic Tracking component sustained by nonequilibrium fluxes. Our findings: (1) General Theory: We prove that tracking gain scales strictly with environmental variability and switching time-scales; near-static or fast-switching environments are not worth tracking. (2) Optimal Strategies: We explain optimal bet-hedging and phenotypic memory as the interplay between these components. (3) Control: We demonstrate, with an example, how to suppress pathogens by independently attacking their Generalism robustness (via environmental time fractions) and Tracking capabilities (via environmental switching speed). This work provides a physical framework for understanding and controlling adaptivity.