Mathematical and computational models for understanding tumour evolution and improving cancer therapy
Tumours are a product of evolution in spatially structured populations of cells. Genetic heterogeneity among tumour cells has been proposed as a basis for forecasting cancer evolution, but its spatially dynamic nature makes accurate prediction challenging. I will present a novel computational model of cell proliferation, competition, mutation and migration, used to assess when and how genetic diversity is most predictive of tumour progression. These findings help explain the multiformity of tumour evolution and contribute to establishing a theoretical foundation for the emerging field of predictive oncology. I will go on to explain how related mathematical models and experimental results support a radical new approach to cancer therapy, which aims to control tumour burden by exploiting competition between drug-sensitive and -resistant cells.
This is a roughly weekly series of didactical blackboard talks focussing on some theoretical aspect of Condensed Matter, Biological, and Statistical Physics..