About me

I use data-driven mathematical and computational models to investigate the evolution and ecology of cancer, in collaboration with experimental biologists and clinicians.

I lead a mathematical oncology group at City St George’s, University of London focussed on four research themes:

• 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, and Bayesian data analysis.

Software and blogging

I created and maintain the ggmuller R package for plotting evolutionary dynamics and the demon tumour evolution model, which you can run on your own computer or on an HPC using the warlock workflow.

My PhD student Kim Verity created the RUIindices R package to evaluate tree shape in terms of our robust, universal and interpretable indices.

I (used to, before having kids) blog about science at These few lines.

My Mastodon profile is ecoevo.social/@robjohnnoble. On Bluesky I’m @robjohnnoble.bsky.social.