(333c) Bridging Statistical Inference and Stochastic Modeling to Shed Light on Gene Regulation

Cell biological data is increasingly available at single-cell, single-nucleotide, and single-molecule resolution. Such experiments reveal often-unexpected levels of heterogeneity at these scales. In the Read lab, we use stochastic biochemical network modeling to study the origins of this heterogeneity and discover signatures in the noise that offer clues to molecular mechanisms.

I will present recent work in genomics and epigenomics where we are bridging “top-down” statistical inference with “bottom-up” stochastic mechanistic modeling to maximize the utility of big data. For example, stochastic models of enzymatic processes that confer epigenetic marks on DNA inform our interpretation of regional correlations derived from epigenome sequencing, with implications for epigenome stability.