Stochastic modelling and inference for live-cell gene expression time-series data to unravel the mechanisms of stem cell differentiation

This project will develop statistical methodology for noisy time-series data and stochastic computational models to analyse 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 activity of genes in single cells. These observations are important in understanding many cellular processes which strongly depend on dynamic gene activity. One of these is the process by which stem cells generate mature cell types (stem cell differentiation). This is a critical biological process not only for embryonic development, but also regeneration, and modern stem cell-based regenerative therapy approaches. Dr Cerys Manning has previously shown that oscillations in gene activity 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 use clustering methods to identify groups of cells that exhibit similar patterns of gene expression. We will also fit stochastic models described by Stochastic Differential Equations to the time-series data and use Bayesian statistics to estimate model parameters, quantify model uncertainty, perform model comparisons, and derive predictions.

The ideal candidate for this project will be interested in Bayesian statistics, stochastic processes, 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 supervised by Giorgos Minas and Jochen Kursawe, in collaboration with Cerys Manning (University of Manchester).