Supervisor: Dr Weini Huang
Project description:
Many diseases in human are caused by genetic alternations and errors starting from a single cell. The origin of these genetic errors and the expansion of the abnormal cells carrying these genetic errors are often stochastic processes. There is an urgent need to understand the difference between health and non-health tissues such as tumours, in order to explain the mechanisms of disease development and even predict the possible dynamics in such stochastic systems.
The aim of this PhD project is to use stochastic models based on simple mechanistic assumptions to explain observed patterns in human cells, including healthy tissues and tumours. Analytical work will involve to use, for example, probabilistic and Markov models to calculate the expected frequency distributions of these genetic errors in a population of human cells. We will also use Gillespie simulations and Bayesian statistics to compare the mechanistic models with measured genetic patterns, e.g. the distribution of accumulated mutations in single cells especially when some mutations can drive the initiation of tumour progress. These will be done in cooperation with our partner research groups in the Barts Cancer Institute in the School of Medicine and Dentistry in Queen Mary University of London. I
In general, we aim to build general stochastic models and analysis to understand and quantify the growth history of human tissues by using experimental and clinical data of genetic information. The candidate should hold a BSc, MSc or an equivalent degree in applied mathematics, physics, computer science or computational biology.
Further information: How to apply Entry requirements Fees and funding