Analytical approximations for spatial stochastic gene expression in single cells and tissues

Stephen Smith, Claudia Cianci, Ramon Grima

Research output: Contribution to journalArticlepeer-review

Abstract

Gene expression occurs in an environment in which both stochastic and diffusive effects are significant. Spatial stochastic simulations are computationally expensive compared to their deterministic counterparts and hence little is currently known of the significance of intrinsic noise in a spatial setting. Starting from the reaction-diffusion master equation (RDME) describing stochastic reaction-diffusion processes, we here derive expressions for the approximate steady-state mean concentrations which are explicit functions of the dimensionality of space, rate constants and diffusion coefficients. The expressions have a simple closed form when the system consists of one effective species. These formulae show that, even for spatially homogeneous systems, mean concentrations can depend on diffusion coefficients: this contradicts the predictions of deterministic reaction-diffusion processes, thus highlighting the importance of intrinsic noise. We confirm our theory by comparison with stochastic simulations, using the RDME and Brownian dynamics, of two models of stochastic and spatial gene expression in single cells and tissues.
Original languageEnglish
JournalJournal of the Royal Society, Interface
Volume13
Issue number118
DOIs
Publication statusPublished - 4 May 2016

Fingerprint

Dive into the research topics of 'Analytical approximations for spatial stochastic gene expression in single cells and tissues'. Together they form a unique fingerprint.

Cite this