TakeAIM is an annual competition organised by the Smith Institute. Established in 2011, the competition is an opportunity for university students to showcase their work on the industrial stage. TakeAIM’s goal is to highlight the crucial role mathematics plays in solving real-world problems while rewarding the academic exploration of future innovators who undertake pioneering research.

Enrico Gavagnin, a mathematics PhD student at Bath has won the award with his research that will aid in the prediction of skin cancer patterns. He will present his research to TakeAIM sponsoring companies, industrial partners of the Smith Institute, and the wider academic community at the TakeAIM awards ceremony in February.

An extract from Enrico's entry:

Everything is a collective behaviour. Actually, it is probably more accurate to say that collective behaviour is a way of interpreting nature. From physics to biology, every complex phenomenon can be seen as a collective effort of many little components, either molecules, birds or fish which, by interacting and competing with each other, give rise to a global behaviour. Connecting the simple interactions between the individual components with the resulting global behaviour of the system represents the core of the understanding of these phenomena.

As humans, we are just the result of a collective behaviour and our components are our cells. Typically, our cells behave cooperatively, for the benefit of the organism. Sometimes, however, some cells start to reproduce and move faster than others which, eventually, might develop into a malign cancer and threat the entire organism’s life. With my research, I aim to gain a better understanding of aggressive cancers, such as melanoma, by applying mathematical models of collective behaviour to melanocytes - the cells responsible for the pigmentation of human skin. By working in direct contact with experimental biologists, we are able to produce hypotheses on the development of skin cancer and to calibrate optimal drug treatments which maximise the effectiveness of a given anticancer therapy.