Universal dynamical function behind all genetic codes: P-adic attractor dynamical model

IF 2 4区 生物学 Q2 BIOLOGY
Ekaterina Yurova Axelsson, Andrei Khrennikov
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引用次数: 0

Abstract

The genetic code is a map which gives the correspondence between codons in DNA and amino acids. In the attractor dynamical model (ADM), genetic codes can be described as the sets of the cyclic attractors of discrete dynamical systems - the iterations of functions acting in the ring of 2-adic integers Z2. This ring arises from representation of nucleotides by binary vectors and hence codons by triples of binary vectors. We construct a Universal Function B such that the dynamical functions for all known genetic codes can be obtained from B by simple transformations on the set of codon cycles - the “Addition” and “Division” operations. ADM can be employed for study of phylogenetic dynamics of genetic codes. One can speculate that the “common ancestor genetic code” was caused by B. We remark that this function has 24 cyclic attractors which distribution coincides with the distribution for the hypothetical pre-LUCA code. This coupling of the Universal Function with the pre-LUCA code assigns the genetic codes evolution perspective to ADM. All genetic codes are generated from B through the special chains of the “Addition” and “Division” operations. The challenging problem is to assign the biological meaning to these mathematical operations.
所有遗传密码背后的通用动力学函数P-adic 吸引子动力学模型
遗传密码是 DNA 密码子与氨基酸之间的对应关系图。在吸引子动力学模型(ADM)中,遗传密码可以描述为离散动力系统的循环吸引子集--作用于二元整数环 Z2 的函数迭代。二进制向量表示核苷酸,二进制向量的三元组表示密码子,从而产生了这个环。我们构建了一个通用函数 B,通过对密码子循环集的简单变换--"加法 "和 "除法 "运算--可以从 B 得到所有已知遗传密码的动力学函数。ADM 可用于研究遗传密码的系统发育动力学。我们可以推测,"共同祖先遗传密码 "是由 B 引起的。我们注意到,该函数有 24 个循环吸引子,其分布与假定的前 LUCA 密码的分布相吻合。通用函数与前 LUCA 代码的这种耦合将遗传密码进化的视角赋予了 ADM。所有遗传密码都是通过 "加法 "和 "除法 "运算的特殊链从 B 生成的。如何为这些数学运算赋予生物学意义是一个具有挑战性的问题。
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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