Combating obesity with mathematics

Digital Patients to support clinical interventions in Metabolic Syndrome

Due to an aging population and the obesity epidemic, an increasing number of people suffer from the so-called Metabolic Syndrome (MetSyn). Those people have a combination of related disease phenotypes, such as high cholesterol, disturbed sugar metabolism and insulin insensitivity. Moreover, they are at a high risk to develop type 2 diabetes, fatty liver and cardiovascular diseases.
To understand how the processes involved in metabolism of cholesterol, lipids and sugars become imbalanced, a systems biology approach is adopted. Computer simulation models are used that describe the metabolic networks in cells and the interactions between organs and tissues. Changes in genetic and protein networks regulating metabolism are also incorporated. The models use patient data as input. Different types of molecular data, including metabolic profiles, protein activity and gene expression, are integrated. By applying a new modeling approach, developed at TU/e (called ADAPT), the models can describe the development and progression of MetSyn over a long period of time. ADAPT makes use of unique, in-house developed technology for uncertainty analysis (PUA) to quantify the effects of uncertainty in experimental data and model on the model predictions.
These ‘digital patients’ are applied to analyze and predict the effect of changes in life style (healthier diet, more exercise) for obese patient with MetSyn, For morbidly obese patients the effects of clinical interventions, such as bariatric surgery (including gastric bypass surgery), are analyzed. By using the ADAPT method the effect of a therapeutic intervention can be analyzed for a long period of time.


Computational modeling of disease progression and treatment-in-time effects 


RESOLVEProject: RESOLVE, FP7 Health - Systems Medicine program, budget: over 13.5 million euro, duration: 5 year Funding

EC FP7



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