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.
Machine learning for drug discovery
Using machine learning and chemoinformatics approaches to discover novel molecules with desired biological activity
Using a forward engineering approach to design and control biological systems.
Systems Biology and Metabolic Disease
Using mathematical models to enhance understanding and treatment of disease.
Systems Biology for Oncology
Systems biology is applied to (immuno-)oncology with the ultimate goal of gaining insights in cancer deregulations and drugs mode of action,…
Application and development of molecular simulations to elucidate self-assembly and self-organization processes in biologically relevant…
Data Science and Bioinformatics
In our Bioinformatics and Mathematical Modelling research we apply methods from computer science and mathematics in medicine and biology.
Meet some of our Researchers
Tom de Greef
Natal van Riel
Our most recent peer reviewed publications
Prognostic Value of Combined Biomarkers in Patients With Heart FailureAnnals of Laboratory Medicine (2023)
Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancerLung Cancer (2023)
The Accuracy of Wrist-Worn Photoplethysmogram-Measured Heart and Respiratory Rates in Abdominal Surgery PatientsJMIR Perioperative Medicine (2023)
Precision and accuracy of receptor quantification on synthetic and biological surfaces using DNA-PAINTACS Sensors (2023)
Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate ConditionsMetabolites (2023)
Visiting addressBuilding 15, Gemini South (room 2.106)Groene Loper5612 AZ EindhovenNetherlands