A statistical-mechanical study of evolution of robustness in noisy environment
Ayaka Sakata, Koji Hukushima, and Kunihiko Kaneko

TL;DR
This paper models the evolution of robustness in noisy environments using a statistical-mechanical spin system, revealing phase transitions that influence the speed and stability of evolving target patterns.
Contribution
It introduces a novel spin-based model linking phenotype evolution, mutational robustness, and phase transitions, providing insights into protein folding dynamics.
Findings
Robust evolution occurs in an intermediate temperature phase with funnel-like dynamics.
Low-temperature phase exhibits spin-glass behavior with slow convergence.
Higher temperatures facilitate rapid, mutation-tolerant evolution.
Abstract
In biological systems, expression dynamics that can provide fitted phenotype patterns with respect to a specific function have evolved through mutations. This has been observed in the evolution of proteins for realizing folding dynamics through which a target structure is shaped. We study this evolutionary process by introducing a statistical-mechanical model of interacting spins, where a configuration of spins and their interactions represent a phenotype and genotype, respectively. The phenotype dynamics are given by a stochastic process with temperature under a Hamiltonian with . The evolution of is also stochastic with temperature and follows mutations introduced into and selection based on a fitness defined for a configuration of a given set of target spins. Below a certain temperature , the interactions that…
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