Statistical mechanics for gene transcription

Abstract

We study the behavior of a DNA regulatory sequence using statistical mechanics and computational simulations, focusing on the simple repression model. We applied the Kawasaki algorithm, a Monte Carlo method, to simulate the system dynamics. Our results make use of theoretical models that have been elsewhere supported by experimental data and align with these. Not only our findings enhance understanding of gene expression by validating the existing predictive models, but also introduce an interaction energy between neighboring repressors. This represents a novelty in biophysics that demands alternative models, such as the one-dimensional Ising model.