Mathematical and statistical modelling of cell fate in Haematopoiesis

The way in which blood stem cells first emerge and go on to function in full homoeostasis is not yet fully understood. This project will address this question via mathematical modelling applied to lineage tracing and single cell data. The student will develop stochastic and Bayesian models to model cell fate dynamics in haematopoiesis from the point that the blood stem cells are first produced to the steady state dynamics that follow it. The data used to calibrate the model will be lineage tracing data, of which there are a number of public data sets for adult mouse and the De Bruijn lab is currently working on for development. Once the model is built it will be used in two ways, first to assess the effect of mutations with lineage tracing data where a mutation is induced along with the lineage tracing label. Secondly Bayesian models will be developed to combine the model with single cell data to study the fate mechanisms at a molecular level. During the course of the PhD, the student will develop skills in probabilistic modelling, stochastic process and deep learning, as well as knowledge of the haematopoietic system.

The project is both quantitative and interdisciplinary. The candidate comes from a pure maths background and will be trained in the use of stochastic modelling and statistical machine learning (including Bayesian Inference and deep learning) applied to studying cell fate and homeostasis in haematopoiesis.

The project is fully interdisciplinary, with one supervisor a former theoretical physicist that works on modelling stem cells and the other a biomedical scientist with extensive expertise in blood stem cells.