Constructing computational models to improve the knowledge of diseases, biomedical processes and structures

Describing, understanding and ultimately controlling life's processes

The research group Computational Biology develops computational models that help to obtain qualitative and quantitative knowledge of diseases, biomedical processes and structures. The computational models are developed based on a thorough understanding of molecular modeling methods, data analysis techniques,  machine learning, systems biology, and parameter estimation techniques. Next to data obtained through collaborations and partnerships with other university groups, companies and hospitals, within the Synthetic Biology arena data is also achieved by own experiments. Topics of research are, amongst others, biomembranes, protein interactions, complex biochemical networks, and diseases like metabolic syndrome, diabetes mellitus and cancer. The group also puts efforts in enhancing the shift from ‘describing’ life’s processes to ‘understanding’ them and ‘capturing’ them in validated predictive models, and even ‘managing’ or ‘controlling’ them in real life. Research themes of the group are Molecular Simulations, Systems Biology and Metabolic Disease, Systems Biology for Oncology, Synthetic Biology, Data Science and Bioinformatics.