Effect of gene-expression bursts on stochastic timing of cellular events

TL;DR

This paper models how gene expression noise, especially translation bursts, affects the timing of cellular events, providing analytical tools to understand and potentially control stochastic timing in biological systems.

Contribution

It introduces a general analytical framework for the first-passage time distribution in gene expression with arbitrary burst size distributions, and analyzes feedback regulation robustness.

Findings

Feedback strategies are robust to burst size variability.

Analytical formulas for FPT moments are derived.

Burst size distribution influences event timing noise.

Abstract

Gene expression is inherently a noisy process which manifests as cell-to-cell variability in time evolution of proteins. Consequently, events that trigger at critical threshold levels of regulatory proteins exhibit stochasticity in their timing. An important contributor to the noise in gene expression is translation bursts which correspond to randomness in number of proteins produced in a single mRNA lifetime. Modeling timing of an event as a first-passage time (FPT) problem, we explore the effect of burst size distribution on event timing. Towards this end, the probability density function of FPT is computed for a gene expression model with burst size drawn from a generic non-negative distribution. Analytical formulas for FPT moments are provided in terms of known vectors and inverse of a matrix. The effect of burst size distribution is investigated by looking at how the feedback regulation strategy that minimizes noise in timing around a given time deviates from the case when burst is deterministic. Interestingly, results show that the feedback strategy for deterministic burst case is quite robust to change in burst size distribution, and deviations from it are confined to about 20% of the optimal value. These findings facilitate an improved understanding of noise regulation in event timing.