New insights into biological processes such as skin cancer spread and wound healing can be obtained through collective cell spreading experiments. These experiments involve placing cells in a dish and observing (by taking images and using image processing tools) how the population of cells evolve under a variety of experimental treatments and conditions. An important component for obtaining insight into these processes is the biological modelling and subsequent calibration of the model. Cells move and proliferate (give birth to other cells) according to random mechanisms so it is important to consider stochastic models.
However, stochastic models of collective cell spreading are so complex that standard parameter estimation approaches are not feasible. This project will develop efficient Bayesian computational methods for such models.
This project requires the following:
- Developing and implement efficient Bayesian computational techniques for the calibration of complex stochastic collective cell spreading models.
- Compare with previous estimation approaches in the literature.
- Writing up results as journal articles
Findings will be written up as journal articles for relevant journals.
Skills and experience
The student will develop their skills in the following:
- Statistical inference
- Bayesian computation
- Mathematical and stochastic modelling
- Scientific programming
- Academic writing
You may be able to apply for a research scholarship in our annual scholarship round.
Contact the supervisor for more information.