Natal van Riel is Professor of Biomedical Systems Biology at the department of Biomedical Engineering at Eindhoven University of Technology, where he leads the Computational Biology group and the Systems Biology and Metabolic Diseases research program. He is also part-time Professor of Computational Modelling at Amsterdam University Medical Centers (location AMC, University of Amsterdam's Faculty of Medicine). His research focuses on modelling of metabolic networks and physiology, scientific machine learning for personalized models and human digital twins, and methods for analysing complex dynamical systems. He applies these approaches to cardiometabolic diseases, including obesity and type 2 diabetes. He actively collaborates with engineers, AI specialists, physiologists, clinicians, healthcare professionals, social scientists, and patients to translate data science and computational models into medical applications.

The role of bile acids in the complex interaction between gut microbiome and metabolic health is currently an important research focus in his group (e.g. in the RESOLVE project in collaboration with Amsterdam UMC). Within the NWO program ‘Complexity in Health and Nutrition’, Natal van Riel also focuses on modelling the digestion and metabolism of nutrients in the project ‘Metabolic adaptation, transitions and resilience in overweight individuals’. In cooperation with the Catharina hospital in Eindhoven he has developed the Metabolic Health Index (MHI) to quantify the benefit of bariatric surgery to resolve metabolic diseases (type 2 diabetes, dyslipidemia), which is a second important outcome of the surgical treatment in addition to weight reduction. He develops metabolic digital twins of human individuals to enable predictive, preventive, personalized, and participatory medicine. In the DiaGame project digital twins are developed to empower patients with diabetes in self-management of their disease.

Natal van Riel studied Electrical Engineering at Eindhoven University of Technology (TU/e, The Netherlands) where he was trained in system identification and control engineering. After receiving his MSc degree in 1995, he started PhD research in the Biotechnology group of Unilever Research Vlaardingen (The Netherlands) under supervision of Prof. Theo Verrips, on integrating computational modelling and experiments to study cell metabolism. In 2000, he obtained his PhD from Utrecht University (The Netherlands). From 2000 to 2003, he worked in the department of Electrical Engineering of TU/e, investigating the application of system and control theory to understand biological processes. In 2003, he was appointed as Assistant Professor in the department of Biomedical Engineering at TU/e where he initiated Systems Biology research. This was expanded when he joined the group of Prof. Peter Hilbers in 2006, to lead the Computational Systems Biology research program, investigating complex, multi-factorial diseases. In 2014, Natal van Riel was appointed Associate Professor of Systems Biology and Metabolic Diseases. In that same year he was a visiting scholar of the department of Bioengineering at the University of California San Diego (UCSD) in the group of Prof. Bernhard Palsson. In 2015, he was appointed part-time Professor of Computational Modelling at the Academic Medical Center AMC (University of Amsterdam). In 2019, he was appointed full Professor of Biomedical Systems Biology at TU/e. In 2023 he became head of the Computational Biology group at TU/e.

Natal van Riel is chair of the Examination Committee of BmE and as such member of the general Examination Committee of the TU/e and member of the Quality Committee of the department. He is member of the Curriculum Committee of the department and coordinates the learning line on Biomedical Systems Modeling. He is also member of the Research Committee of the department and member of the Interdepartmental Committee (IFC) for Engineering.

Systems Biology has matured into a core methodology for integrative and quantitative life sciences. Our ambition is to translate its principles to biomedical research, clinical practice, and education - to better understand, cure, and ultimately prevent disease.

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