Model checking cancer automata

Abstract

Cancer is a chronic disease where cells grow and multiply in an uncontrollable manner ultimately spreading and invading surrounding tissue, and metastasising in other parts or organs of the body. Automata can be used to capture cancer evolving through a (discrete finite) sequence of progressive stages called phenotypes. Automata consist of states (known as hallmarks of cancer) and transitions between states, indicating a progression or regression of the cancer. We explore extensions and combinations of different variants of timed automata and associated tools to model and analyse a model of the disease in different ways. We combine patient information and comorbidities with the cancer automaton through composition. The goal of this work is to use model checking as an analysis technique to provide further insights into the effectiveness of treatment plans for a given patient, and how these could potentially inhibit or slow down the progression of cancer.