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Understanding the molecular basis of disease requires innovative approaches: this session brings together mathematicians, computer scientists and biologists to talk about how they are attempting to model complex biological systems with the aim of understanding disease and improving health. Although on the outskirts of Exeter, this venue is on three bus routes: H, 5 and 55. Just ask for “Cowley Bridge” if coming from town, £1.70 each way. The events take place in the restaurant, and there are a few steps so this venue is not accessible for those with impaired mobility.
Modelling the brain as a complex network to improve epilepsy surgery
Leandro Junges
(Research Fellow, University of Exeter)
About a third of epilepsy patients do not respond to drug treatment. For these patients, surgery can be the only option to eliminate or reduce the frequency of seizures. Unfortunately, the rate of success of this procedure is far from optimal. In this talk, a new approach to estimate the outcome from resection surgery will be presented. The combination of mathematical modelling, dynamical systems, and network science can revolutionise the way clinicians define strategies for epilepsy surgery.
Oral bacteria, cardiovascular health, and ageing
Joanna L'Heureux
(PhD student, University of Exeter)
Nitric oxide is important for cardiovascular health, and maintaining blood pressure. These physiological processes typically decline during ageing. Bacteria in the mouth convert nitrate found in a vegetable-rich diet into nitrite, which is swallowed and further reduced to nitric oxide. We can use next-generation sequencing approaches to study these oral bacteria, and enhance the production of nitric oxide to get the best out of your diet and improve cardiovascular health.
Using neural mass models to study epilepsy
Lauric Ferrat
(PhD student, University of Exeter)
With more than 50 million patients, Epilepsy is one of the main neurological disorders. T better understand the brain, mathematical models representing the neurons have been built. Nevertheless, these mathematical models are complex and not directly useful. We use a new approach based on machine learning to apprehend these models. The results challenge past conclusions and open new perspectives to treat patients.
Recovering failing heart signals with maths
Harry Green
(PhD Student, University of Exeter)
Atrial fibrillation is the most common heart disease in the world today. The current leading treatment method involves burning regions of heart tissue to separate the good bits of the heart from the bit causing problems. However, important signals recorded during this process can sometimes be unreliable, and the cardiologist can be faced with information loss when treating their patient. Using mathematical models, we build simulations of what is happening inside the patient's heart, and we can use this to recover the lost information in clinic to help the cardiologist make better decisions.
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