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Algebra for Transforming Genetic Codes Based on Matrices Applied in Quantum Field Theories

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Advances in Artificial Systems for Medicine and Education II (AIMEE2018 2018)

Abstract

Genetic algebra shows 2 + 2 type character defined in a purine–pyrimidine field, and 1 + 3 type character defined in an RNA–DNA field. Its 1 + 3 type character has not been studied with algebraic methods. Physical matrices, among others in quantum electrodynamics (QED), demonstrate 1 + 3 character too. The latter is expressed with the help of the hypersymmetry and its algebra (called tau). The similitude between the two descriptions allowed showing that similar matrices can demonstrate the 1 + 3 character in genetic algebra like those that govern the matrices elaborated for QED. We show that certain algebraic methods and transformation formula developed in the theory of QED fields appear also in the transformation properties of certain genetic matrices. This paper is a minor contribution to demonstrate that combined transformations applied together in the convolution of the mentioned two fields may explain the structure change between RNA and DNA. This study shows that there are some general regularities in nature that appear in different domains of our knowledge about nature (here concretely, genetics and physical field theory), which have been considered distant and not overlapping in their methods and laws.

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Acknowledgments

This paper is part of the results of a project in the framework of a long-term bilateral cooperation between the Russian and Hungarian Academies of Sciences under the item 5 in the working plan entitled “Non-linear models and symmetrologic analysis in biomechanics, bioinformatics, and the theory of self-organizing systems”.

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Correspondence to György Darvas .

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Darvas, G., Petoukhov, S.V. (2020). Algebra for Transforming Genetic Codes Based on Matrices Applied in Quantum Field Theories. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education II. AIMEE2018 2018. Advances in Intelligent Systems and Computing, vol 902. Springer, Cham. https://doi.org/10.1007/978-3-030-12082-5_5

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