Cancer is one of the major causes of death in the world, particularly the developed world, with around 11 million people diagnosed and around 9 million people dying each year. The World Health Organisation (WHO) predicts that current trends show the number rising to 11.5 million in 2030. There are few individuals who have not been touched either directly or indirectly by cancer. While treatment for cancer is continually improving, alternative approaches can offer even greater insight into the complexity of the disease and its treatment. Biomedical scientists and clinicians are recognising the need to integrate data across a range of spatial and temporal scales (from genes through cells to tissues) in order to fully understand cancer. 

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 parameterised by biological data) has the potential to have a positive impact on patients suffering from diseases such as cancer through improved clinical treatment.

Further details of my current research can be found at the Mathematical Biology Research Group web page.