Genetic code, the problem of coding biological cycles, and cyclic Gray codes

IF 2 4区 生物学 Q2 BIOLOGY
Sergey V. Petoukhov
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引用次数: 0

Abstract

This article is devoted to the problem of genetically coding of inherited cyclic structures in biological bodies, whose life activity is based on a great inherited set of mutually coordinated cyclic processes. The author puts forward and arguments the idea that the genetic coding system is capable of encoding inherited cyclic processes because it itself is a system of cyclic codes connected with Boolean algebra of logic. In other words, the physiological processes in question are cyclical because they are genetically encoded by cyclic codes. In support of this idea, the author presents a set of his results on the connection of the genetic coding system with cyclic Gray codes, which are one of many known types of cyclic codes. This opens up the possibility of using for modeling inherited cyclic biostructures those algebraic and logical theories and constructions that are associated with Gray codes and have long been used in engineering technologies: Karnaugh maps, Hilbert curve, Hadamard matrices, Walsh functions, dyadic analysis, etc. The author believes that when studying the origin, evolution and function of the genetic code, it is necessary to take into account the ability of the genetic system to encode many mutually related cyclic processes.
遗传密码、生物循环编码问题和循环格雷码
本文专门讨论生物体中遗传循环结构的基因编码问题,生物体的生命活动是建立在一套巨大的相互协调的遗传循环过程基础之上的。作者提出并论证了这样一个观点:遗传编码系统之所以能够对遗传循环过程进行编码,是因为它本身就是一个与布尔逻辑代数相连的循环编码系统。换句话说,有关的生理过程是循环的,因为它们是由循环码遗传编码的。为了支持这一观点,作者介绍了他在遗传编码系统与循环格雷码之间的联系方面取得的一系列成果。这为使用与格雷码相关的代数和逻辑理论及构造来模拟遗传循环生物结构提供了可能性,而这些理论和构造早已用于工程技术领域:卡尔诺图、希尔伯特曲线、哈达玛矩阵、沃尔什函数、二元分析等。作者认为,在研究遗传密码的起源、演变和功能时,有必要考虑到遗传系统对许多相互关联的循环过程进行编码的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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