Study level

  • Honours

Faculty/School

Faculty of Science

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Supervisors

Dr Adrianne Jenner
Position
Senior Lecturer
Division / Faculty
Faculty of Science
Dr Leah South
Position
Senior Lecturer in Mathematical Sciences
Division / Faculty
Faculty of Science

Overview

In 2015, the Food and Drug Association (FDA) approved a lab-engineered virus for the treatment of melanoma (skin cancer). Since then, there has been a significant increase in the number of lab-grown viruses that are being tested in clinical trials as potential treatments of cancer. Unfortunately, it seems that a large number of patients in these clinical trials fail under this treatment and currently there is no way to distinguish between responders and non-responders to treatment.

Fortunately, we can use mathematics to model the effects of lab-engineered viruses on cancer cells and hopefully develop a way to distinguish between responses and non-responders to treatment.

In this project, we would use a system of ordinary differential equations that describe how these viruses infect and kill cancer cells in the human body. Then using techniques from Bayesian inference, we would obtain estimates for the distribution of parameters that describe the treatment’s effectiveness in humans. We can also simulate how different patients respond to treatment based on their specific characteristics, creating what is commonly called “personalised medicine”.

Research activities

This project will involve:

  • numerically simulating a system of differential equations that describe viruses decided to treat cancer, injected into a population of tumour cells
  • implementing Bayesian inference methods for obtaining estimates for the distribution of parameters in the model
  • running the inference method on tumour growth data
  • summarising what characterisations predispose a patient to respond or not respond to this novel therapy.

Outcomes

The output of this project will be to apply Bayesian inference techniques to obtain estimates of parameters in a model for the novel cancer therapy: viruses.

Skills and experience

  • confidence using Matlab (required)
  • knowledge of differential equations (required)
  • understanding of statistical inference (desirable).

Keywords

Contact

Contact the supervisor for more information.