May 24, 2016

Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization

BioRxiv : the Preprint Server for Biology
Justine Dattani, Mauricio Barahona

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 the cellular drives (e.g., entrainment, periodicity or promoter randomness), and a downstream transcriptional Poissonian part, which is common to all models. Our analytical framework allows us to treat cell-to-cell and dynamic variability consistently, unifying several approaches in the literature. We apply the obtained solution to characterize several gene transcription 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 a...Continue Reading

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Mentioned in this Paper

Genes
Transcription, Genetic
Environment
Promoter
Reverse Transcriptase Polymerase Chain Reaction
Cellularization
Literature
Simulation
Analysis
RNA, Messenger

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