Nonequilibrium Theory for Adaptive Systems in Varying Environments
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911454801690624 |
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| author | Yang, Ying-Jen Kocher, Charles D. Dill, Ken A. |
| author_facet | Yang, Ying-Jen Kocher, Charles D. Dill, Ken A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonequilibrium Theory for Adaptive Systems in Varying Environments Yang, Ying-Jen Kocher, Charles D. Dill, Ken A. Statistical Mechanics Biological Physics Populations and Evolution 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. |
| title | Nonequilibrium Theory for Adaptive Systems in Varying Environments |
| topic | Statistical Mechanics Biological Physics Populations and Evolution |
| url | https://arxiv.org/abs/2506.19018 |