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

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.

10 figures

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• Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations
• Sensitivity of asymmetric rate-dependent critical systems to initial conditions: insights into cellular decision making
• Analytical results for a stochastic model of gene expression with arbitrary partitioning of proteins
• Stationarity and inference in multistate promoter models of stochastic gene expression via stick-breaking measures