Glioblastoma multiforme (GBM) is the most common and most malignant form of brain cancer, being characterised by relentless growth and aggressive invasion into the healthy brain tissue, resulting in extremely poor outcome. Given the complexity and cell heterogeneity observed in this type of tumour, it is natural to study its development from an integrative and systems perspective. This project aims to develop an executable hybrid model of tumour growth. The model integrates a multi-level description of tumour growth, it includes extracellular events such as nutrient availability, cell density and cell migration, as well as intracellular aspects such as cell cycle progression. It uses a discrete cellular automaton approach for describing the stages of the lifecycle of a cell (proliferation, quiescence, death) and the transitions between them, together with molecular dynamics methods to calculate the spatial characteristics of a cell (such as its location, velocity and forces acting upon it). Both parts of the model are coupled to the discretised version of a reaction-diffusion system describing the supply of nutrients to the tumour. The model also includes the effect of radiation on cell death and tumour re-growth. The long-term goal of the project is to provide an accurate tool for predicting the development of GBM that could eventually be used in clinical settings for deciding on best treatment options. In collaboration with Raj Jena (Department of Oncology, University of Cambridge).