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.