I use mathematical and computational models to investigate the evolution and ecology of cancer, in collaboration with experimental biologists and clinicians.
- Pursuing a systematic understanding of somatic evolution;
- Developing methods for patient-specific tumour progression forecasting;
- Devising treatment strategies that optimally manipulate tumour evolutionary dynamics;
- Using life history theory and population data to disentangle contributions to cancer risk.
Whereas these four themes pertain to distinct applications, an evolutionary perspective reveals them to be fundamentally connected. A common thread in my research is investigating how aspects of ecology shape evolutionary dynamics. My overarching vision is to unite complementary lines of enquiry across biological scales to establish general principles of cancer evolution.
The methods I use to pursue these aims include agent-based models, analysis of dynamical systems, stochastic processes, matrix population models, and Bayesian data analysis.
Software and blogging
I blog about science at These few lines.