Reconstructing cis-regulatory networks underlying cell state transitions based on multiomics data, with applications to immunity and cancer

Our lab employs a combination of advanced wet-lab and computational techniques to study how human cells make decisions to change their transcriptional state, with a particular emphasis on the role of DNA regulatory regions such as gene enhancers in this process. The cell models we are currently looking at include pluripotent stem cells, innate immune cells and cancer cells acquiring drug resistance. The proposed project will focus on one of these models and a range of “omics” readouts (including, depending on the exact system and question, single-cell and bulk transcriptomics, epigenomics, chromosomal conformation and phosphoproteomics). The project will involve generating some of these data as required in the lab, analysing it computationally using advanced statistical, machine learning and network analysis methods and validating the computational predictions experimentally. Depending on the exact system, the results will provide insights into such questions as the function of non-coding disease-associated variation, mechanisms of immune stimulus response and means to leverage cancer drug resistance. The exact experimental question and system, and the ratio of “wet” to “dry” work can be adjusted depending on the student’s background and preferences. The student will join a close-knit, supportive and collaborative team and ample training will be provided.

To apply, please visit https://lms.mrc.ac.uk/study-here/phd-studentships/lms-3-5yr-studentships/