各种适应形式的人口动态模型。

Biophysics and Physicobiology Pub Date : 2023-09-02 eCollection Date: 2023-01-01 DOI:10.2142/biophysico.bppb-v20.0034
So Nakashima, Tetsuya J Kobayashi
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引用次数: 0

摘要

适应不断变化的环境是生物的普遍特征之一。由于个体的适应方式多种多样,因此统一认识这些不同的适应方式对于理解适应至关重要。在信息方面,至少可以从两个角度对适应进行分类。一是适应的被动性和活动性,二是信息传递的类型。在达尔文的自然选择中,生物是在随机产生的性状中被选择出来的,在这种情况下,生物个体是被动的,即它们不处理任何环境信息。另一方面,生物也可以通过感知环境并改变自身特征来适应环境。这是一种主动适应,因为它利用了环境信息。就信息传递而言,通过表型异质性(如细菌的 "对赌")进行的适应是代内适应,其性状不会传递给下一代。相比之下,通过遗传多样性实现的适应则是代际适应。种群动力学理论使我们能够统一这些不同的适应模式,并利用定量遗传学和信息热力学的技术对其特性进行定性和定量分析。此外,这些方法还可应用于生物从过去的经验中学习并代代相传的情况。在这项研究中,我们介绍了基于种群动力学的生物适应统一理论,并展示了该理论在评估信息的适合度价值和分析实验系谱树数据方面的潜在应用。最后,我们讨论了其未来的发展前景。本综述文章是《SEIBUTSU BUTSURI》第 57 卷第 287-290 页(2017 年)日文文章的扩展版。
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Population dynamics models for various forms of adaptation.

Adaptability to changing environments is one of the universal characteristics of living organisms. Because individual modes of adaptation are diverse, a unified understanding of these diverse modes is essential to comprehend adaptation. Adaptations can be categorized from at least two perspectives with respect to information. One is the passivity and activity of adaptation and the other is the type of information transmission. In Darwinian natural selection, organisms are selected among randomly generated traits under which individual organisms are passive in the sense that they do not process any environmental information. On the other hand, organisms can also adapt by sensing their environment and changing their traits. This is an active adaptation in that it makes use of environmental information. In terms of information transfer, adaptation through phenotypic heterogeneity, such as bacterial bet-hedging, is intragenerational in which traits are not passed on to the next generation. In contrast, adaptation through genetic diversity is intergenerational. The theory of population dynamics enables us to unify these various modes of adaptations and their properties can be analyzed qualitatively and quantitatively using techniques from quantitative genetics and information thermodynamics. In addition, such methods can be applied to situations where organisms can learn from past experiences and pass them on from generation to generation. In this work, we introduce the unified theory of biological adaptation based on population dynamics and show its potential applications to evaluate the fitness value of information and to analyze experimental lineage tree data. Finally, we discuss future perspectives for its development. This review article is an extended version of the Japanese article in SEIBUTSU BUTSURI Vol. 57, p. 287-290 (2017).

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