Computational Biology (CB)
The aim of the Computational Biology group is to understand biomedical processes in living systems by using a variety of computational data analysis and modeling techniques. The systems range from atomic interactions within a single molecule, membranes and vesicles, to cellular interactions, metabolic pathways and networks of organisms. Current research areas comprise systems biology, synthetic biology, modeling self-assembly of biomaterials, data analysis, and modeling drug delivery and radiation therapy.
Typical examples of diseases being studied are diabetes, psoriasis and metabolic disorders such as tryptophan deficiencies in patients suffering from autism.
We use a variety of techniques such as:
- Design of algorithms, graphs, neural networks, data analysis, clustering and classification algorithms
- Multiscale modeling, differential equations, numerical methods, parameter estimation, parameter sensitivity analysis
- Large scale parallel simulations, molecular systems
- Mathematics and computer science for biomedical applications including our own original methods.
We developed original computational methods in proteomics analysis in type 2 diabetes to establish the potential disease state and intervention specific biomarkers. The results of this research elucidate the underlying mechanisms of the improvements observed in low calorie diets and exercises to improve in obese type 2 diabetes patients.
HUMETICS (Human Metabolic Syndrome)
Public-private-partnership ApeT and TU/e
The aim of the project is to develop a software platform for the automated analysis of human metabolome data. Our group develops the computational models, numerical algorithms and the software.The prototype of the platform was tested on metabolomics data from about 1500 subjects obtained from Erasmus MC.