Understanding the mechanisms underlying RNA polymerase II-mediated transcription and exploring their influence on transcriptional dynamics

The project aims to understand the mechanisms influencing RNA polymerase II (pol II) -mediated transcription, their relationships with each other and how they affect transcriptional dynamics. The primary process of interest will be polymerase pausing, in which the elongation rate of pol II is greatly reduced at certain sites. The nature of pausing will be investigated by attacking the problem from multiple angles using a variety of data types. The recently developed time-variant PRO seq (TV-PRO seq) allows for genome-wide detection of pausing sites and measurement of pausing times. TV-PRO seq data will be linked with other data types, including ChIP seq data on distributions of histone modifications, NET seq data on pol II distributions, ATAC seq data on chromosome accessibility, single cell RNA seq data on mRNA levels and variability and SLAM seq data on mRNA degradation rates. Statistical analysis of integrated data sets to find correlations between pausing sites and other genomic features will provide mechanistic insights. Mathematical models of transcription will be constructed that account for various mechanisms and processes of interest, including pol II pausing, initiation, reinitation from the gene's 3' terminus and spontaneous disengaging. The models will be designed to provide read-outs that can be directly compared with the aforementioned data, such as polymerase and nascent mRNA distributions along the gene, as well as mature mRNA count distributions. This will allow theoretical exploration of the interplay between different mechanisms and how they contribute to the stochastic dynamics of gene expression. Simulation/analytical results will be fitted to the data, using Bayesian inference to, for example, optimise parameter values and model structures. Conclusions made regarding transcriptional mechanisms/dynamics will be confirmed with experimental verification. Insights regarding stochasticity in transcription may have applications in synthetic biology.