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

TL;DR

This paper introduces an exact analytical framework for modeling stochastic gene transcription influenced by dynamic cellular environments, unifying various approaches and aiding in data analysis and simulation efficiency.

Contribution

It provides a comprehensive solution to the master equation for gene transcription with time-varying rates, capturing upstream drives and downstream Poissonian effects.

Findings

Characterizes the influence of cellular drives on gene transcription variability.

Unifies multiple existing models into a single analytical framework.

Facilitates analysis of noise sources and reduces simulation costs.

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.