Summary: Cancer is one of the major causes of death in the world (particularly the developed world), with around 11 million people diagnosed and around 7 million people dying each year. The World Health Organization predicts that current trends show around 9 million people will die in 2015, with the number rising to 11.5 million in 2030. Cancer growth is a complicated complex phenomenon involving many interrelated processes across a wide range of spatial and temporal scales, and in spite of many advances, it is still difficult to treat and cure as the previous statistics show. New approaches are necessary if further progress in curing the disease is to be made. The description of most biological processes in the human body involves many different but interconnected phenomena, which occur at different spatial and temporal scales.
From the modelling viewpoint, there are three natural scales of interest: subcellular, cellular and tissue. The modelling described in this paper has a common theme of quantitative predictive mathematical modelling, analysis and computational simulation of key aspects of cancer growth and treatment. The long-term goal is to build a ‘virtual cancer made up of different but connected mathematical models at the different biological scales (from genes to tissue to organ)’. The development of quantitative predictive models (based on sound biological evidence and underpinned and parameterized by biological data) will no doubt have a positive impact on patients suffering from diseases such as cancer through improved clinical treatment.