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| Главный автор: | |
|---|---|
| Формат: | Recurso digital |
| Язык: | английский |
| Опубликовано: |
Zenodo
2025
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| Предметы: | |
| Online-ссылка: | https://doi.org/10.5281/zenodo.17561994 |
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Оглавление:
- <p>This paper introduces a unified theoretical framework for the evolution of senescence, re-<br>casting it as an information-dynamic optimization process. We apply the Dynamic Entropy-<br>Complexity Correspondence (DECC) to model an organism’s life history. We establish a<br>rigorous foundation by modeling the developmental program as a generalized stochastic pro-<br>cess, explicitly incorporating mortality via absorbing boundaries. We rigorously derive the<br>Age-Dependent Complexity Dynamics Equation (CDE) and its renormalized form for the<br>surviving cohort, correcting for the loss of probability mass due to death using the renor-<br>malization theorem, with full proofs. By weighting the CDE terms by Fisher’s reproductive<br>value (Va), we transform life-history evolution into an optimal control problem. We provide<br>a rigorous derivation using the calculus of variations, explicitly solving the Euler-Lagrange<br>equations for a model Lagrangian representing the Disposable Soma (DS) trade-off. This<br>derivation formally proves that the optimal maintenance rate necessarily declines with Va,<br>leading to senescence when accounting for baseline damage. We demonstrate that Antagonis-<br>tic Pleiotropy (AP) and Mutation Accumulation (MA) emerge as constrained or degenerate<br>solutions to this variational problem. Furthermore, we utilize the Algorithmic Free Energy<br>(AFE) framework to interpret the evolution of the developmental program as an optimiza-<br>tion process balancing algorithmic complexity and fitness. This synthesis provides a unified,<br>mathematically rigorous foundation for senescence.</p>