Source-linked AI summary
Intrinsic noise in stochastic models of gene expression with molecular memory and bursting
Tao Jia, Rahul V. Kulkarni
TL;DR
Gene-expression noise is shaped by stochastic production and decay, but basic models use memoryless waiting times despite evidence that these processes involve multiple biochemical steps. The paper maps bursty expression to queueing models with general waiting-time distributions, deriving steady-state noise expressions that distinguish contributions from gestation, senescence, and bursting.
Problem
Basic gene-expression models use exponential waiting times, although the underlying processes involve multiple biochemical steps and may have more general waiting-time distributions.
Source: p. 1Method
The authors map bursty protein synthesis and decay to queueing models and derive analytical expressions using general waiting-time distributions.
Source: pp. 1–2Results
The derived noise expression agrees with simulations in the considered case, while the result from Ref. is less accurate when µp⟨T⟩ is large.
Source: p. 3Takeaways & Limitations
The expressions distinguish how gestation, transcriptional bursting, and translational bursting contribute to steady-state protein noise and can inform analysis of experiments and phenotypic consequences.
Source: pp. 2, 4The results are valid for τm ≪ τp; obtaining an exact expression for general gestation and bursting distributions without that condition is difficult.
Source: p. 3
Abstract
from arXiv · showhide
Regulation of intrinsic noise in gene expression is essential for many cellular functions. Correspondingly, there is considerable interest in understanding how different molecular mechanisms of gene expression impact variations in protein levels across a population of cells. In this work, we analyze a stochastic model of bursty gene expression which considers general waiting-time distributions governing arrival and decay of proteins. By mapping the system to models analyzed in queueing theory, we derive analytical expressions for the noise in steady-state protein distributions. The derived results extend previous work by including the effects of arbitrary probability distributions representing the effects of molecular memory and bursting. The analytical expressions obtained provide insight into the role of transcriptional, post-transcriptional and post-translational mechanisms in controlling the noise in gene expression.