Stochastic gene expression with delay

TL;DR

This paper extends classical gene expression models by incorporating arbitrary delays in transcription and translation, deriving formulas for the resulting variance reduction in RNA and protein numbers at equilibrium.

Contribution

It introduces a novel stochastic gene expression model with delays and provides explicit formulas for the second-order structure in equilibrium.

Findings

Delay decreases variance in RNA and protein numbers.

Explicit formulas for second-order moments with delays are derived.

Delays impact the burst-like behavior of gene expression.

Abstract

The expression of genes usually follows a two-step procedure. First, a gene (encoded in the genome) is transcribed resulting in a strand of (messenger) RNA. Afterwards, the RNA is translated into protein. Classically, this gene expression is modeled using a Markov jump process including activation and deactivation of the gene, transcription and translation rates together with degradation of RNA and protein. We extend this model by adding delays (with arbitrary distributions) to transcription and translation. Such delays can e.g.\ mean that RNA has to be transported to a different part of a cell before translation can be initiated. Already in the classical model, production of RNA and protein come in bursts by activation and deactivation of the gene, resulting in a large variance of the number of RNA and proteins in equilibrium. We derive precise formulas for this second-order structure with the model including delay in equilibrium. As a general fact, the delay decreases the variance of the number of RNA and proteins.