Minimal model of transcriptional elongation processes with pauses

TL;DR

This paper introduces a simple mean-field model that accurately predicts transcriptional elongation rates in the presence of random pauses, extending the classical TASEP framework to more realistic biological scenarios.

Contribution

It presents a novel mean-field approach to incorporate random pauses into the TASEP model of transcription, providing accurate predictions across all pause durations.

Findings

Mean-field model predicts particle current accurately for all pause durations.

Model captures the effect of pauses on transcriptional elongation.

Simple description of blocking behind paused particles enhances model accuracy.

Abstract

Fundamental biological processes such as transcription and translation, where a genetic sequence is sequentially read by a macromolecule, have been well described by a classical model of non-equilibrium statistical physics, the totally asymmetric exclusion principle (TASEP). This model describes particles hopping between sites of a one-dimensional lattice, with the particle current determining the transcription or translation rate. An open problem is how to analyze a TASEP where particles can pause randomly, as has been observed during transcription. In this work, we report that surprisingly, a simple mean-field model predicts well the particle current for all values of the average pause duration, using a simple description of blocking behind paused particles.