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Efficient Bayesian computation for stochastic collective cell spreading models

Study level

PhD

Master of Philosophy

Honours

Vacation research experience scheme

Faculty/Lead unit

Science and Engineering Faculty

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Supervisors

Associate Professor Chris Drovandi
Position
Associate Professor
Division / Faculty
Science and Engineering Faculty
Professor Matthew Simpson
Position
Professor
Division / Faculty
Science and Engineering Faculty

Overview

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 how the population of cells evolve under a variety of experimental treatments and conditions by taking images and using image processing tools.

An important component for obtaining insight into these processes is the biological modelling and subsequent calibration of the model to real experimental data.

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.

Research activities

In this project, you will develop and implement efficient Bayesian computational techniques for the calibration of complex stochastic collective cell spreading models.

An important component of successful parameter estimation for these types of models is to find useful summarisations of the data that are informative about the parameters.

Some of the activities involved in this project include:

  • stochastic modelling
  • data summarisation
  • developing Bayesian methods for parameter estimation
  • writing up results as journal articles.

Outcomes

We expect the results of this topic to include:

  • gain new insights into cell biological systems
  • expand the class of stochastic models for which parameter estimation is possible
  • publish results in journals.

Skills and experience

We expect you to have the following skills:

  • stochastic modelling
  • programming.

Ideally, we desire you to also have competency in statistical inference and mathematical modelling.

This project is most suitable for students who have an interest in computational statistics applied to problems in mathematical biology.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

Contact

Contact the supervisor for more information.