Bayesian inference of polymerase dynamics over the exclusion process

TL;DR

This paper introduces a Bayesian inference framework to estimate RNA polymerase speeds during transcription from spatial distribution data, revealing position-dependent rates and minimal congestion effects, enhancing understanding of gene expression.

Contribution

It presents a novel Bayesian approach to infer polymerase dynamics from spatial data without explicit system inversion, advancing mechanistic modeling of transcription.

Findings

Polymerase speeds vary significantly along the genome.

Traffic congestion has a minor impact on transcription dynamics.

The method accurately infers progression rates from spatial distributions.

Abstract

Transcription is a complex phenomenon that permits the conversion of genetic information into phenotype by means of an enzyme called RNA polymerase, which erratically moves along and scans the DNA template. We perform Bayesian inference over a paradigmatic mechanistic model of non-equilibrium statistical physics, i.e., the asymmetric exclusion processes in the hydrodynamic limit, assuming a Gaussian process prior for the polymerase progression rate as a latent variable. Our framework allows us to infer the speed of polymerases during transcription given their spatial distribution, whilst avoiding the explicit inversion of the system's dynamics. The results, which show processing rates strongly varying with genomic position and minor role of traffic-like congestion, may have strong implications for the understanding of gene expression.