12:00 – 12:30
What if your personal digital twin could predict the consequences of lifestyle and medical treatment options? You would not be burdened unnecessarily and healthcare costs would drop. Natal van Riel is working on mathematical models that show the human metabolism for each person. Via funded projects like Digitwin and Diagame, he aims to build a digital copy of individual patients to predict the success and to determine the necessary aftercare of a treatment such as gastric bypass surgery. Such digital twins should consider food intake and also intestinal bacteria and hormones whose job is to turn digestive processes on and off.
Natal van Riel is full professor of biomedical systems biology at the department of Biomedical Engineering (research group Computational Biology) at Eindhoven University of Technology, where he leads the systems biology and metabolic diseases research program. He is also part-time professor of computational modelling at Amsterdam University Medical Centers and a board member of the TUE’s Data Science Center. His research focuses on modelling of metabolic networks and physiology, learning algorithms to develop personalized models (metabolic ‘digital twins’), methods for analysis of dynamic models, and applications in metabolic syndrome and associated diseases such as type 2 diabetes.