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@article{Dattani:2017:10.1098/rsif.2016.0833,
author = {Dattani, J and Barahona, M},
doi = {10.1098/rsif.2016.0833},
journal = {Journal of the Royal Society Interface},
title = {Stochastic models of gene transcription with upstream drives: Exact solution and sample path characterisation},
url = {http://dx.doi.org/10.1098/rsif.2016.0833},
volume = {14},
year = {2017}
}
TY - JOUR
AB - Gene transcription is a highly stochastic and dynamic process. As a result, the mRNA copynumber of a given gene is heterogeneous both between cells and across time. We present a frameworkto 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 drivesrepresenting the effect of different aspects of the cellular environment. We show that the full solutionof the master equation contains two components: a model-specific, upstream effective drive, whichencapsulates 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 analyticalframework treats cell-to-cell and dynamic variability consistently, unifying several approaches in theliterature. 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 asthe effect of time-scales and other dynamic characteristics of drives. We also show how the solutioncan be applied to the analysis of noise sources in single-cell data, and to reduce the computationalcost of stochastic simulations.
AU - Dattani,J
AU - Barahona,M
DO - 10.1098/rsif.2016.0833
PY - 2017///
SN - 1742-5689
TI - Stochastic models of gene transcription with upstream drives: Exact solution and sample path characterisation
T2 - Journal of the Royal Society Interface
UR - http://dx.doi.org/10.1098/rsif.2016.0833
UR - http://hdl.handle.net/10044/1/42759
VL - 14
ER -