Speaker
Description
Gene expression is intrinsically stochastic, leading to substantial cell-to-cell variability in mRNA and protein levels, now routinely quantified with single-cell technologies. In this talk, I will discuss extensions of the classical two-state telegraph model to incorporate salient features of single-cell biology, including cell division, DNA replication, mRNA maturation, gene dosage compensation, growth-dependent transcription, cell-size control strategies and cell-cycle duration variability. I will also present our statistical inference and machine learning approaches for fitting both classical and complex gene-expression models to single-cell data (smFISH, live-cell imaging, and scRNA-seq). These frameworks provide principled ways to separate biological from technical noise, estimate transcriptional parameters, and infer the mechanisms most compatible with observed transcriptional dynamics.
