Developmental noise, entropy, and biological system condition

IF 2 4区 生物学 Q2 BIOLOGY
Vladimir M. Zakharov, Ilya E. Trofimov
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引用次数: 0

Abstract

Developmental noise is considered as a permissible level of entropy, as a compromise between the cost and needed precision of the realization of genetic information. In terms of entropy, noise is a measure of acceptable level of disorder to ensure a reliable system operation. Developmental noise plays a role in the observed phenotypic diversity and is associated with other indicators of the biological system condition. The thermodynamic characteristic of entropy by the energy metabolism also turns out to be related to the developmental noise. Phenotypic variability is largely determined by developmental homeostasis, including both canalization (an ability to form a similar phenotype under different conditions) and developmental stability (a capability for perfect development measured by noise level). It is shown that the change in the noise level, as an expression of the certain entropy level, unlike other forms of phenotypic variability, is a reflection of a change in the system condition. Although the entropy indices of ontogeny and community under certain conditions can change simultaneously, the entropy index at the level of developmental noise proves to be a more unambiguous and universal measure of the disorder of a biological system, compared to biodiversity indices at the community level.

发育噪音、熵和生物系统状况
发育噪音被认为是一种可允许的熵水平,是实现遗传信息的成本与所需精确度之间的折衷。从熵的角度来看,噪音是可接受的无序程度的衡量标准,以确保系统运行的可靠性。发育噪音在观察到的表型多样性中起着一定的作用,并与生物系统状况的其他指标相关联。能量代谢产生的熵的热力学特征也与发育噪音有关。表型变异性在很大程度上由发育平衡决定,包括管道化(在不同条件下形成相似表型的能力)和发育稳定性(以噪音水平衡量的完美发育能力)。研究表明,作为一定熵水平的一种表现形式,噪声水平的变化与其他形式的表型变异不同,它反映了系统条件的变化。虽然在某些条件下,本体和群落的熵指数会同时发生变化,但事实证明,与群落水平的生物多样性指数相比,发育噪声水平的熵指数是衡量生物系统无序程度的更明确、更普遍的指标。
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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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