Stochastic models of gene transcription with upstream drives: Exact solution and sample path characterisation

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Title: Stochastic models of gene transcription with upstream drives: Exact solution and sample path characterisation
Authors: Dattani, J
Barahona, M
Item Type: Journal Article
Abstract: Gene transcription is a highly stochastic and dynamic process. As a result, the mRNA copy number of a given gene is heterogeneous both between cells and across time. We present a framework to model gene transcription in populations of cells with time-varying (stochastic or deterministic) transcription and degradation rates. Such rates can be understood as upstream cellular drives representing the effect of different aspects of the cellular environment. We show that the full solution of the master equation contains two components: a model-specific, upstream effective drive, which encapsulates the effect of cellular drives (e.g., entrainment, periodicity or promoter randomness), and a downstream transcriptional Poissonian part, which is common to all models. Our analytical framework treats cell-to-cell and dynamic variability consistently, unifying several approaches in the literature. We apply the obtained solution to characterise different models of experimental relevance, and to explain the influence on gene transcription of synchrony, stationarity, ergodicity, as well as the effect of time-scales and other dynamic characteristics of drives. We also show how the solution can be applied to the analysis of noise sources in single-cell data, and to reduce the computational cost of stochastic simulations.
Issue Date: 4-Jan-2017
Date of Acceptance: 29-Nov-2016
ISSN: 1742-5689
Publisher: Royal Society, The
Journal / Book Title: Journal of the Royal Society Interface
Volume: 14
Issue: 126
Copyright Statement: © 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/I017267/1
Keywords: chemical master equation
gene expression
stochastic models
General Science & Technology
MD Multidisciplinary
Publication Status: Published
Article Number: 20160833
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences

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