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 systems biology modeling methods, data analysis techniques,  machine learning, and parameter estimation algorithms. Topics of research are, amongst others, complex biochemical networks, diseases like metabolic syndrome, diabetes mellitus and cancer, and applied clinical data science. Next to data obtained through collaborations and partnerships with other university groups, companies and hospitals, within the Systems Biology for Oncology theme data is also achieved by own experiments. 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 Systems Biology and Metabolic Disease, Systems Biology for Oncology, Immuno Systems Biology, and Data Science and Bioinformatics.