Use of cancer modelling in clinical practice is still in its infancy. Majority of developed cancer models rely on classical ODE/PDE approaches while other techniques are rarely explored. However, to understand cancer one needs to cover multiple spatial and temporal biological scales such as: a) nano- scale of individual molecular interactions, b) genetics and metabolic properties that give rise to different cancer cell phenotypes, c) tumours as heterogeneous structures whose composition depends on constantly changing interactions between cancer cells and their microenvironment. To model such diversity of phenomena requires development of not only novel approaches that would deal with specific local problems, but also development of framework that will integrate them.
In this special issue some of the computational approaches we will cover are: molecular dynamics, agent based modelling, machine learning, cellular automata, ODE/PDE modelling as well as possibilities of integrating several of these approaches into unified framework. Overarching goals of such framework is to enable efficient exploration of potential strategies to optimize cancer treatments.