从相关性到因果关系:在考夫曼模型中揭示封闭性对开放式进化的影响

IF 1.9 4区 生物学 Q2 BIOLOGY
M. Faggian
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引用次数: 0

摘要

进化是一个高度复杂的过程,其特点是产生新的事物,这需要个体的历史和集体组织。本文探讨了生物组织与开放式进化(OEE)之间的关系,并着重探讨了两者之间的因果关系。为了定量研究化学系统内的因果关系,我们利用装配理论来评估自催化集对Kauffman模型的复杂性动力学的影响。在论文的第二部分,我们通过分析最简单的自催化集对Kauffman模型中复杂性动力学的影响,特别是在没有参数相关性的情况下,来加强这一猜想。通过将自催化集解释为化学系统中的组织结构,我们的发现为研究生物组织与OEE之间的因果关系提供了第一个数值支持。本研究为OEE与生物组织之间的动力学关系的初步研究提供了一个有前景的领域,并可能促进理论生物学对它们之间联系的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From correlation to causation: Unraveling the impact of closure on open-ended evolution within the Kauffman model
Evolution is a highly intricate process marked by the generation of novelty, which requires the historical and collective organization of individuals. In this paper, we investigate the relationship between biological organization and Open-Ended Evolution (OEE), with a particular focus on the causal connection between the two. To quantitatively investigate the causal relation within a chemical system, we utilize assembly theory to assess the influence of auto-catalytic sets on the complexity dynamics of Kauffman’s model. In the second part of the paper, we strengthen this conjecture by analyzing the effects of the simplest auto-catalytic set on the complexity dynamics in Kauffman’s model, specifically in the absence of parametric correlation. By interpreting auto-catalytic sets as organizational structures in chemical systems, our findings provide the first numerical support for investigating the causal relationship between biological organization and OEE. This work represents a promising area for the initial study of the dynamical relationship between OEE and biological organization, and may advance the understanding of their connection in theoretical biology.
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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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