This project is at the interface between traditional mathematical modelling and modern machine learning in the context of cancer biology.
This project aims to build a theoretical framework to understand cancer progression in an environment with more than two drugs by combining evolutionary game theory, population dynamics, and agent-based modelling. In the first stage, the student will design pay-off matrices to represent hypothetical relationships between cytotoxic activity and drug resistance. In the second stage, the student will search for evolutionary stable strategies (unchanging clonal compositions) in the corresponding replicator equations.
In the final stage, the student will implement these drug schedules in a graph-structured agent-based model to study the interactions between adaptive therapies, the unique vulnerabilities of small populations, and spatial effects.
Find out more about this scholarship.
Closing date: 2 October 2023