The Chern-Simons current in systems of DNA-RNA transcriptions

TL;DR

This paper introduces a novel approach using Chern-Simons currents from supersymmetry theory to analyze gene expression, providing new insights into DNA-RNA transcription and genetic variations over time.

Contribution

It develops a supersymmetry-based algebraic framework for gene analysis, including ghost fields for non-active DNA regions, and applies it to time series genetic data.

Findings

Computed hidden states of codons using Chern-Simons 3 forms

Analyzed genetic shift and drift through tensor correlation networks

Demonstrated the method on viral and host gene time series data

Abstract

A Chern-Simons current, coming from ghost and anti-ghost fields of supersymmetry theory, can be used to define a spectrum of gene expression in new time series data where a spinor field, as alternative representation of a gene, is adopted instead of using the standard alphabet sequence of bases $A, T, C, G, U$. After a general discussion on the use of supersymmetry in biological systems, we give examples of the use of supersymmetry for living organism, discuss the codon and anti-codon ghost fields and develop an algebraic construction for the trash DNA, the DNA area which does not seem active in biological systems. As a general result, all hidden states of codon can be computed by Chern-Simons 3 forms. Finally, we plot a time series of genetic variations of viral glycoprotein gene and host T-cell receptor gene by using a gene tensor correlation network related to the Chern-Simons current. An empirical analysis of genetic shift, in host cell receptor genes with separated cluster of gene and genetic drift in viral gene, is obtained by using a tensor correlation plot over time series data derived as the empirical mode decomposition of Chern-Simons current.