Abstract

Cancers are pathologies driven by genetic mutations that disrupt a multitude of signalling pathways operating across different cell types interacting in highly complex ways. No two cancers, even of the same type, are the same. The holy grail of cancer treatment is to analyse the patient’s genome and predict a sequence and combination of therapies that will destroy that patient’s cancer with no adverse side effects. By developing executable models that can simulate cancer tumours at different levels of abstraction, we are on the threshold of being able to deliver on this vision. The state-of-the-art in executable biology is the use of formal methods and software verification to describe biological systems and explore their properties. Using program synthesis methods we can directly build such models from patients’ data. This approach has already been used to find new more efficient therapies for Leukaemia in partnership with AstraZenenca. The next big question, as we collect more and more patient genomic data and history of cancer treatments, is how can we use AI methods to drive therapeutic regimes directly from patients’ data? In the talk, I will showcase recent results and share my ambitions in this space.