This project will develop statistical methodology and computational algorithms to estimate model parameters using live-cell imaging data provided by the lab of our collaborator Dr Cerys Manning at the University of Manchester. Live-cell imaging is a powerful technique for real-time observation of the expression of targeted genes in single cells. These observations are important in understanding cellular processes, such as stem cell differentiation, which strongly depend on dynamic gene expression. Stem cell differentiation is a critical biological process for embryonic development, regeneration, and regenerative therapy approaches. Dr Cerys Manning has previously shown that gene expression oscillations are observed in stem cells of the central nervous system, and these are important for regulating the differentiation process. We now wish to unravel the mechanisms driving these oscillations. We also wish to examine the role of stochasticity in stem cell differentiation and its interplay with oscillations. For this purpose, we will develop stochastic models that will be fitted to the highly variable live-cell imaging data. Bayesian statistical methodology will be employed to estimate model parameters, quantify model uncertainty, perform model comparisons, and derive predictions. The ideal candidate will be interested in Bayesian statistics, dynamical systems, and stem cell differentiation. Background in at least one of the above subjects will be beneficial, but candidates with other backgrounds will be considered.
This project is offered in collaboration with Dr Cerys Manning (University of Manchester).
For more information, including how to apply, please see this document (pdf file): Postgraduate Opportunities in Statistics. See also the School’s Postgraduate Research web page.