Data Science & Machine Learning Division, PerMediQ GmbH, Salzbergweg 18, 85368, Wang, Germany.
J Mol Evol. 2018 Jan;86(1):47-57. doi: 10.1007/s00239-017-9823-7. Epub 2017 Dec 16.
Explanations based on low-level interacting elements are valuable and powerful since they contribute to identify the key mechanisms of biological functions. However, many dynamic systems based on low-level interacting elements with unambiguous, finite, and complete information of initial states generate future states that cannot be predicted, implying an increase of complexity and open-ended evolution. Such systems are like Turing machines, that overlap with dynamical systems that cannot halt. We argue that organisms find halting conditions by distorting these mechanisms, creating conditions for a constant creativity that drives evolution. We introduce a modulus of elasticity to measure the changes in these mechanisms in response to changes in the computed environment. We test this concept in a population of predators and predated cells with chemotactic mechanisms and demonstrate how the selection of a given mechanism depends on the entire population. We finally explore this concept in different frameworks and postulate that the identification of predictive mechanisms is only successful with small elasticity modulus.
基于底层相互作用元素的解释是有价值和强大的，因为它们有助于确定生物功能的关键机制。然而，许多基于具有明确、有限和完整初始状态信息的底层相互作用元素的动态系统生成的未来状态是无法预测的，这意味着复杂性增加和无限进化。这样的系统就像图灵机一样，与无法停止的动力系统重叠。我们认为，生物体通过扭曲这些机制来找到停止条件，为不断推动进化的创造力创造条件。我们引入一个弹性模量来测量这些机制在计算环境变化时的变化。我们在具有趋化机制的捕食者和被捕食细胞群体中测试了这个概念，并展示了给定机制的选择如何取决于整个群体。我们最后在不同的框架中探索了这个概念，并假设预测机制的识别只有在弹性模量较小的情况下才会成功。
