Smiatek Jens
Institute for Computational Physics, University of Stuttgart, Allmandring 3, 70569, Stuttgart, Germany.
J Mol Evol. 2024 Dec;92(6):703-719. doi: 10.1007/s00239-024-10195-8. Epub 2024 Aug 29.
We present a non-equilibrium thermodynamics approach to the multilevel theory of learning for the study of molecular evolution. This approach allows us to study the explicit time dependence of molecular evolutionary processes and their impact on entropy production. Interpreting the mathematical expressions, we can show that two main contributions affect entropy production of molecular evolution processes which can be identified as mutation and gene transfer effects. Accordingly, our results show that the optimal adaptation of organisms to external conditions in the context of evolutionary processes is driven by principles of minimum entropy production. Such results can also be interpreted as the basis of some previous postulates of the theory of learning. Although our macroscopic approach requires certain simplifications, it allows us to interpret molecular evolutionary processes using thermodynamic descriptions with reference to well-known biological processes.
我们提出一种非平衡热力学方法,用于研究分子进化的多层次学习理论。这种方法使我们能够研究分子进化过程明确的时间依赖性及其对熵产生的影响。通过解释数学表达式,我们可以表明,有两个主要因素影响分子进化过程的熵产生,这两个因素可被确定为突变和基因转移效应。因此,我们的结果表明,在进化过程中,生物体对外部条件的最佳适应是由最小熵产生原则驱动的。这些结果也可以被解释为学习理论中一些先前假设的基础。虽然我们的宏观方法需要一定的简化,但它使我们能够参照众所周知的生物过程,用热力学描述来解释分子进化过程。