ArticleJournal of the Royal Society, Interface2016
Analytical approximations for spatial stochastic gene expression in single cells and tissues.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation.Computational and structural biotechnology journal · 2025Article
- Learning differential equation models from stochastic agent-based model simulations.Journal of the Royal Society, Interface · 2021Article
- Review
- Accurate and efficient discretizations for stochastic models providing near agent-based spatial resolution at low computational cost.Journal of the Royal Society, Interface · 2019Article
- Revisiting the Reduction of Stochastic Models of Genetic Feedback Loops with Fast Promoter Switching.Biophysical journal · 2019Article
- Spatial Stochastic Intracellular Kinetics: A Review of Modelling Approaches.Bulletin of mathematical biology · 2019Review
- The Linear Noise Approximation for Spatially Dependent Biochemical Networks.Bulletin of mathematical biology · 2019Article
- Single-cell variability in multicellular organisms.Nature communications · 2018Article
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Authors and funding
3 authors.
Funding
Abstract
Gene expression occurs in an environment in which both stochastic and diffusive effects are significant. Spatial stochastic simulations are computationally expensive compared with 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.
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