Cancer is considered as one of the most serious chronic diseases which has low survival rate in the long term. Therefore, understanding the mechanisms of cancer development and recurrence becomes crucial.
Chemotherapy is one of the most common treatment for cancer. However, the reported cancer recurrence after chemotherapy remains high, due to that the cancer cells develop resistance to the drugs during treatment.
While understanding how cancer cells become drug-resistant is still under study, various hypotheses have been raised in the literature with support of experimental observations.
The aim of this project is to use mechanistic models to investigate how the intrinsic heterogeneity in cancer cells affects treatment outcomes.
In particular, cancer cells can have different proliferation and death rates, resulting in a heterogenous group when exposed to drugs.
You will employ both agent-based models and differential equations to mimic the heterogenous population growth and its responses to various chemotherapies.
You will then compare modelling results to patient data of tumour shrinkage as well as long-term survival rates, to provide insights for possible mechanisms that can lead to drug resistance and cancer recurrence.
You can start the project in Semester 2, 2019 or Semester 1, 2020.
We expect to produce a paper and submit it to a mathematical biology journal.
Skills and experience
We expect you to have good knowledge in ordinary differential equations and stochastic modelling and good programming skills (MATLAB).
You may be able to apply for a research scholarship in our annual scholarship round.
Contact the supervisor for more information.