The field of immunology has experienced a true revolution. Where it once was a largely descriptive field of research, immunologists nowadays generate large, quantitative, and temporal data sets on gene and protein expression, diversity of lymphocyte repertoires, and coevolution of pathogens and the immune system. Immunology has thereby become a lot more quantitative. Such quantitative insights are extremely valuable to understand the complexity of the immune system in health and disease. The interpretation of such data sets requires computational approaches and specific expertise.
Read the story Computational Immunology: the digital future of translational immunology.
For general inquiries, please contact us via mail.
Computational Immunology at the Center for Translational Immunology brings together bioinformaticians, biostatisticians and mathematical modelers to develop and apply new methodologies to deal with the challenges imposed by the current data revolution in immunology.
We aim to identify immunological markers to tailor healthcare approaches for individual patients and improve patient care. By understanding how biomarkers vary between individuals, we seek to improve the accuracy of diagnosis, prognosis, and therapeutic decision-making. The ultimate goal is to move beyond the “one-size-fits-all” approach in medicine and develop more targeted, effective treatments based on each patient’s unique biological profile.
By employing cutting-edge technologies such as genomics, proteomics, and advanced computational tools, we aim to translate biomarker discoveries into practical clinical applications. These include predictive models for treatment response and disease progression, as well as the development of personalized therapeutic strategies.
We integrate experimental and computational immunology to understand, predict, and engineer adaptive immune recognition. This includes developing immunoinformatics software and machine learning algorithms, high-dimensional profiling of immune responses, and designing computationally-guided therapeutics. Using these tools we aim to unravel how the adaptive immune system orchestrates the molecular recognition of a seemingly infinite array of antigenic structures through their highly diverse and specific B and T cell receptors. Our studies focus on preclinical and clinical settings, including infection, vaccination and inflammatory disease.
By combining experimental work with mathematical modelling, we aim to obtain quantitative insights into the immune system to better understand the dynamics of the immune system in health and disease. Mathematical models are not only used to interpret the experimental data, but also to optimize the design of new experiments, thereby creating an iterative cycle between maths and experiments. More specifically, we study the production and loss rates of different types of leukocytes, how they are maintained during healthy ageing, and how these processes are disturbed in diseases, such as leukemia, HIV infection, and after haematopoietic stem-cell transplantation. We also use mathematical models to predict potential vaccine efficiency at the population level.