Modeling Gene Expression with Time-Varying Transcription Rates

Single-cell measurements reveal that transcription rates fluctuate over time, reflecting dynamic cellular states and regulatory processes. In this talk, I will present a minimal stochastic framework that captures these fluctuations by allowing the transcription rate itself to evolve as a random process. This "doubly stochastic" formulation yields closed-form results for mRNA noise statistics, reproduces features observed in single-cell data, and enables inference of underlying kinetic parameters.