Analysis of changes occurring in Codon Positions due to mutations through the cellular automata transition rules

Author:

Sengupta Antara,Ghosh Sreeya,Choudhury Pabitra Pal

Abstract

AbstractVariation in the nucleotides of a codon may cause variations in the evolutionary patterns of a DNA or amino acid sequence. To address the capability of each position of a codon to have non-synonymous mutations, the concept of degree of mutation has been introduced. The degree of mutation of a particular position of codon defines the number of non-synonymous mutations occurring for the substitution of nucleotides at each position of a codon, when other two positions of that codon remain unaltered. A Cellular Automaton (CA), is used as a tool to model the mutations of any one of the four DNA bases A, C, T and G at a time where the DNA bases correspond to the states of the CA cells. Point mutation (substitution type) of a codon which characterizes changes in the amino acids, have been associated with local transition rules of a CA. Though there can be transitions of a 4-state CA with 3-neighbourhood cells, here it has been possible to represent all possible point mutations of a codon in terms of combinations of 16 local transition functions of the CA. Further these rules are divided into 4 classes of equivalence. Also, according to the nature of mutations, the 16 local CA rules of substitutions are classified into 3 sets namely, ‘No Mutation’, ‘Transition’ and ‘Transversion’. The experiment has been carried out with three sets of single nucleotide variations(SNVs) of three different viruses but the symptoms of the diseases caused by them are to some extent similar to each other. They are SARS-CoV-1, SARS-CoV-2 and H1N1 Type A viruses. The aim is to understand the impact of nucleotide substitutions at different positions of a codon with respect to a particular disease phenotype.

Publisher

Cold Spring Harbor Laboratory

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analysis of One-Dimensional 4-State Cellular Automata Rules and DNA Evolutions;Advances in Intelligent Systems and Computing;2023

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