Relational model of all genetic codes

IF 1.9 4区 生物学 Q2 BIOLOGY
Nikola Štambuk , Paško Konjevoda , Albert Štambuk
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引用次数: 0

Abstract

The most common way to display the rules of all genetic codes is the Standard Genetic Code (SGC) table and its variants. This article takes an alternative approach to the genetic code table based on the relational model (Konjevoda and Štambuk, 2021). The relational model (RM) proposes distributed storage of the data into a collection of tables, which are called relations. Basic elements of the SGC table are rows (called records or tuples) and columns (called attributes). The SGC table, according to the RM, represents the so-called unnormalized form of a table, and it can be decomposed or divided into 4 tables using a set of rules called normal forms. The rows and columns of a single table are defined by the first and second base, and individual tables are specified by the third codon base. The result of this model is an approach to managing genetic code data, represented in terms of tuples and grouped into relations, with table structure and language consistent with the first-order logic, and sixteen truth functions defined by IUPAC ambiguity codes for incomplete nucleic acid specification. It is concluded that the relational model is a suitable method to display the rules of the Standard Genetic Code and its 28 variants according to Marcello Barbieri's concepts of ambiguity reduction and codepoiesis.
所有遗传密码的关系模型。
显示所有遗传密码规则的最常用方法是标准遗传密码(SGC)表及其变体。本文采用一种基于关系模型的遗传密码表的替代方法(Konjevoda和Štambuk, 2021)。关系模型(RM)建议将数据分布式存储到一组表中,这些表称为关系。SGC表的基本元素是行(称为记录或元组)和列(称为属性)。根据RM, SGC表表示所谓的表的非规范化形式,并且可以使用一组称为范式的规则将其分解或划分为4个表。单个表的行和列由第一和第二碱基定义,而单个表由第三个密码子碱基指定。该模型的结果是一种管理遗传密码数据的方法,该数据以元组表示并分组成关系,具有符合一阶逻辑的表结构和语言,以及IUPAC不完全核酸规范模糊码定义的16个真值函数。根据Marcello Barbieri的歧义还原和密码生成的概念,得出关系模型是一种合适的方法来显示标准遗传密码及其28个变体的规则。
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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