At the Bonn Center for Mathematical Life Sciences, we bring together mathematicians, biologists, and medical researchers to address pressing scientific and clinical challenges. Modern life sciences rely on large, complex datasets that require sophisticated mathematical tools for interpretation. Our research is dedicated to developing and applying such tools, leading to new insights in biology and medicine — and often inspiring new mathematics in the process.
BCML’s projects are supported by third-party funding from prestigious sources, including the German Research Foundation, the Federal Ministry of Research, Technology and Space (former Federal Ministry for Education and Research), the Horizon Europe program, and various additional foundations. This funding enables the center to conduct important research and foster innovative solutions in mathematical life sciences.
Research areas
Understanding Health Through Mathematical Models
Research Area "Mathematical Biology"
We use advanced mathematical modeling approaches to explore complex biological systems. A particular focus lies in understanding the role of the immune system across different diseases. For instance, we develop models that explain how biological processes function and how they respond to therapeutic interventions. This helps improve the prediction of disease progression and optimize treatment strategies.
In immunology, our models capture how immune cells communicate and react to stimuli — providing insights into infection, vaccination, and autoimmunity. By integrating data into systems of ordinary and partial differential equations, as well as agent-based simulations, we aim to simulate biological processes from the molecular to the tissue level.
From Pixels to Insight: Reconstructing Life with Mathematics
Research Area "Mathematical Image Analysis"
Medical and biological images provide rich insights — but must be precisely processed to unlock their full potential. Our team develops cutting-edge mathematical methods to analyze data from MRI scans, microscopy, and other imaging modalities. By incorporating artificial intelligence into traditional variational models, we enhance image reconstruction and enable the detection of subtle structural and functional changes in organs and tissues.
For example, we build tools that assist radiologists in interpreting low-quality MRI images or monitoring cardiac motion across the heartbeat cycle. These methods improve diagnostic accuracy and clinical decision-making. In addition, we developed tailored image analysis pipelines for advanced tissue imaging techniques such as CODEX.
Making Sense of Big Biological Data
Research Area "Bioinformatics and Machine Learning"
To address questions in molecular biology, personalized medicine, and epidemiology, we apply machine learning and bioinformatics to large-scale biological datasets. Our researchers develop computational tools to analyze and integrate diverse data types — including single-cell transcriptomics, metabolomics, and patient health records. We also pioneer new methods, such as Universal Differential Equation models and cutting-edge inference algorithms.
A critical component of our work is assessing model uncertainty to ensure reliability — both in basic biological research and in healthcare applications. These methods are paving the way for future advances in precision medicine and epidemiological forecasting by making complex data understandable and actionable.
