进化如何让我们衰老:介绍可进化的衰老理论。

IF 2 4区 生物学 Q2 BIOLOGY Biosystems Pub Date : 2024-07-20 DOI:10.1016/j.biosystems.2024.105271
Alessandro Fontana, Marios Kyriazis
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引用次数: 0

摘要

在任何时刻,进化都面临着一项艰巨的挑战:完善生物物种已经高度优化的设计,这一壮举是通过前几代人完成的。在这种情况下,随机变化(进化所采用的方法)的影响更可能是有害而非有利的,有可能降低受影响个体的繁殖能力。我们的假设是,老化至少部分是由进化过程中为改进物种设计而进行的所有实验的累积效应造成的。鉴于其伪随机性,这些实验几乎总是不成功,对身体造成伤害,并最终导致死亡。这一假说与 "终端添加 "的概念相一致,即自然界偏向于在发展末期添加创新。从进化作为一种优化算法的角度来看,衰老是有利的,因为它允许在创新对适应性的影响存在但不那么明显的阶段对其进行测试。我们的推论表明,老化具有关键的生物学作用,因为它通过对进化的适应性景观施加规则化效应来提高系统的可进化性。
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How evolution makes us age: Introducing the evolvable soma theory of ageing

At any moment in time, evolution is faced with a formidable challenge: refining the already highly optimised design of biological species, a feat accomplished through all preceding generations. In such a scenario, the impact of random changes (the method employed by evolution) is much more likely to be harmful than advantageous, potentially lowering the reproductive fitness of the affected individuals. Our hypothesis is that ageing is, at least in part, caused by the cumulative effect of all the experiments carried out by evolution to improve a species’ design. These experiments are almost always unsuccessful, as expected given their pseudorandom nature, cause harm to the body and ultimately lead to death. This hypothesis is consistent with the concept of “terminal addition”, by which nature is biased towards adding innovations at the end of development. From the perspective of evolution as an optimisation algorithm, ageing is advantageous as it allows to test innovations during a phase when their impact on fitness is present but less pronounced. Our inference suggests that ageing has a key biological role, as it contributes to the system’s evolvability by exerting a regularisation effect on the fitness landscape of evolution.

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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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