Institute for Computational Biomedicine

AG Univ. Prof. Andreas Schuppert

Schuppert Group is focusing on hybrid modelling and next generation computational technologies, such as quantum computing, for a broad range of applications in HealthCare and Digital Patients.


    • SMITH 
      The use case ASIC: Algortihmic Surveillance in Intensive Care is one of the use cases of the BMBF-funded consortium SMITH. ASIC develops digital solutions to improve intensive care for patients suffering from ARDS, acute respiratory distress syndrome.
    • SimLab Digital Patientis part of the NHR4CES consortium.
      The Simlab will foster High Performance Computing for Medical applications. We strive to set up a HPC platform for the development and application of digital patient models for research and clinics.
    • The JPND project NeuroNode is an international consortium of research groups in UK, Canada, Sweden and Germany.
      Its vision is to understand physiological processes in brain with relevance to rare diseases. Our part is dedicated to learning the underlying mechanisms from data and their systems biology modelling
    • HDS-LEE - Helmholtz graduate school for Data Science – Life, Earth and Energy intends to develop new mathematical methods and computational technologies for a broad range of applications.
      Within HDS-LEE we develop hybrid technologies to learn the structure of poorly understood biological mechanisms, e.g. drug response, from data.

    AG Stumpf

    The stem cell systems biology group aims to understand how stem cells contribute to health and disease. This work includes analyses of (multi-) omics data, for instance obtained from single-cell sequencing but also clinical data. We use computer models and machine learning to make sense of these data.


      • Bio2integrate
        Integration of data from wearable devices (smart watch) and clinical data in order to improve the pain management for patients suffering from small fiber neuropathy. To read more about this work see
      • MPN/MDS(UK Aachen – UK Düsseldorf)
        In cooperation with clinicians from the University Hospitals Aachen and Düsseldorf, this project aims to better understand cancer subtypes and disease progression through pattern recognition of Myeloproliferative and Myelodysplastic neoplasms.
      • TREAT-SGS
        Schinzel-Giedion Syndrome (SGS) is a rare disease that affects young children. One debilitating symptom of SGS are frequent seizures that are difficult to manage using conventional therapies. In this consortium project we conduct a preclinical study to repurpose a drug to treat the seizures associated with Schinzel-Giedion-Syndrome. The project is funded by European Joint Programme on Rare Diseases via the Deutsche Forschungsgemeinschaft (DFG).
      • Stem cell - niche interactions
        The aim of this project is to better understand the influence of stem-cell-niche interactions on self-renewal of blood stem cells in normal hematopoiesis. We are also interested in how these interactions change as the organism ages.
        Project partners:
        Fumio Arai & Hisa Yao, Kyushu University, Fukuoka, Japan.
        To read more about this work, see Arai et al. Cell Systems. 2020; 11 (6): 640-652.e5
      • Theory of stem cell differentiation
        This project aims establish theoretical foundations of stem cell differentiation and self-renewal. Modern single-cell methods can read the molecular status of individual cells in exquisite detail. However, the full complexity of the molecular composition of cells remains inaccessible. We argue that mathematical models that take these unknown configurations into account enable a better understanding of stem cell differentiation dynamics in the absence of detailed knowledge of the full molecular status of the cell.
        Project partners:
        Ben MacArthur, University of Southampton, UK
        Fumio Arai, Kyushu University, Fukuoka, Japan.
        To read more about this work see:
        Stumpf, Arai, MacArthur, Cell Stem Cell (2021).
        Stumpf et al. Cell Systems (2017).