  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.
