Markov Modelling for Mucoviscidosis using Genomic Data

Jeniffer David Packia Muthu and Senthamarai Kannan Kaliyaperumal
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Jeniffer David Packia Muthu: Manonmaniam Sundaranar University, India
Senthamarai Kannan Kaliyaperumal: Manonmaniam Sundaranar University, India

European Journal of Mathematics and Statistics, 2022, vol. 3, issue 6, 27-34

Abstract: The CFTR gene, which encodes a protein present in the cell membranes of epithelial tissues, has an effect on a number of organ systems in the human body. Mutations in the CFTR gene lead to incorrect regulation of cell electrolytes and water levels. The importance of this gene for typical human development has been clearly stated through studies on the CFTR mutation. A new born that inherits one mutant copy of the CFTR gene from each parent may have cystic fibrosis, which is an autosomal recessive disease. This paper establishes a model for mutant genes that will assist in determining whether or not the embedded gene is mutated. Early detection enables the possibility of slightly earlier disease risk reduction. Modelling mutant genes and correspondingly matching the new gene with them would be a crucial and cost-effective method of preventing various chronic diseases and treatment resistance.

Keywords: Cystic Fibrosis; Embedded Markov Chain; Gene Mutation; Markov Chain; Multiple Sequence Alignment (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejmath:v:3:y:2022:i:6:id:14169

DOI: 10.24018/ejmath.2022.3.6.169

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