Speaker
Description
Recent live-cell microscopy techniques allow the simultaneous tracking of distal genomic elements and transcription activity, offering new ways to study chromatin dynamics and gene regulation. However, drawing robust conclusions from such data is statistically challenging due to substantial technical noise, intrinsic fluctuations and limited time resolution. In this talk, I will present a method to infer the statistical relationship between transcription activation and enhancer-promoter distance from live-cell measurements. The problem is formulated as a generalized state-space model, accounting for chromatin dynamics, stochastic transcription activation as well as technical noise. Based on this model, we develop a path-space variational inference scheme, which reveals posterior densities over gene promoter states, polymerase loading events and enhancer-promoter distance. This allows us to quantify the spatiotemporal relationship between enhancer-promoter interactions and gene activation events. We applied the approach to experimental data in mESCs where enhancer-promoter distance and nascent transcription have been quantified across a broad range of conditions. I will conclude the talk by discussing potential implications and future work.
