The Edinburgh Mathematical Society’s Popular Lecture this year will take place as part of Maths Week Scotland. It will be held at 1630 on 14 September 2018, in Graham Kerr Lecture Theatre (Room 224)
(Graham Kerr Building, University of Glasgow; B3 on the campus map). Refreshments will be served in the Maths Building Foyer (C3 on the campus map) from 1530. The lecture is open to all!
The speaker is Professor Mark Chaplain (University of St Andrews), and he will speak on
Mathematical Modelling, Mutations and Metastases: Can We Cure Cancer with Calculus?
Cancer is one of the major causes of death in the world, particularly the developed world, second only to heart disease. 8.8 million people died from cancer in 2015 i.e. globally, almost 1 in 6 (16-17%) deaths can be attributed to cancer. The latest data from the World Health Organisation shows that approximately 12 million new cases were recorded in 2012 with this figure expected to increase by approximately 70% over the next twenty years or so. There are few individuals whose lives 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 to tissues) in order to fully understand cancer. In this respect, there are three natural, key scales linked to each other which, when considered together, go to make up understanding the complex phenomenon that is cancer: the sub-cellular scale, the cellular scale and the tissue scale.
In this talk, we will present an overview of the contribution mathematical modelling is playing in the fight against cancer, and highlight recent multiscale modelling that is giving real insight into several key processes involved in solid tumour growth and progression. The long-term goal of the current work is to build a “virtual tumour” 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/clinical evidence and underpinned and parametrised by biological/clinical data) has the potential to have a positive impact on patients suffering from the disease through improved clinical treatment and personalised medicine.