Within our research group we take a Systems Biology based approach towards Personalized Healthcare and Personalized Medicine. You can find important focus areas below.
Integration of large, multi-dimensional sets of molecular data
The collection of biological data has grown expansively, producing vast amounts of data. The integration of large, multi-dimensional datasets (combining, e.g., genomics, transcriptomics, proteomics and metabolomics) is currently considered to be one of the major bottlenecks in molecular life sciences and systems biology.
Metabolomics and metabolic profiling
The metabolome (metabolite profile) can be considered as a good indication of the current physiological state of a cell or organism, constituting the interaction of the genotype with the environment. In medical research and clinical diagnostics metabolomics (metabolite profiling) can provide the bridge to interpret genetic data in the context of individual patients.
Genome-scale metabolic models and Constrained-based simulation
The basis of the data integration method are computational models containing all metabolic reactions known to exist in a certain species or in a specific cell type of a multicellular organism (Genome-Scale Metabolic Models, GSMM). The models enable to link individual variations in the genome to the phenotype-specific (personal) metabolic profile. The aim is to obtain a better understanding of how genetic differences affect metabolism. And the other way around, how the metabolic profile reflects the combination of one’s genotype and health condition. This is relevant for medical research and clinical diagnostics.
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.
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.
Within this project we go for patient empowerment through personalized tools for disease education. We do this by patient-specific modeling. Examples are;
- The Eindhoven Diabetes Game - A Personalized Virtual Diabetic Patient Simulator, with application in patient education in diabetic care. We do this project in collaboration with Máxima Medical Centre Eindhoven and Design of Technology and Instrumentation (TU/e). The project is funded by NovoNordisk.
- Understanding of disease pathways for applications in Personalized Medicine and Clinical Decision Support Tools.
- The Effects of Skin Composition on Glucose Sensing - spatio-temporal modeling for biosensor development in collaboration with Philips Research Eindhoven (NL) .