Associate Professor (Universitair Hoofddocent)
Institute for Computing and Information Sciences
Radboud University,
Nijmegen, The Netherlands

Radboud Institute for Molecular Life Sciences,
Tumor Immunology department,
Radboud University Medical Center,
Nijmegen, The Netherlands

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contact | research | publications | software | dissertation | awards

Contact Information

My office is at:
Mercator building
Second floor, room 17
Toernooiveld 212

278 Tumor Immunology
Geert Grooteplein 26-28
6525 GA Nijmegen
The Netherlands

mail: johannes.textor }at{
or: johannes.textor }at{


I lead the Computational Immunology group at the Data Science section of the Institute for Computing and Information Sciences at Radboud University and the department of Tumor Immunology at the Radboud Institute for Molecular Life Sciences, Radboud University Medical Center (Radboudumc), Nijmegen, The Netherlands.

We use simulation models (like this one), machine learning, and causal inference methods (like DAGs) to study information processing in the adaptive immune sytem.

Our work has three main goals. First and foremost, we want to understand how the immune system perceives and interacts with "abnormal" information coming from pathogens or tumors. Such knowledge is useful to design immunological treatments such as vaccines or tumor immunotherapies.

Second, we are interested in designing immunologically inspired machine learning and information processing systems -- Artificial Immune Systems. We use these to understand more fundamentally how the immune system stores, retrieves and modifies information, and how this differs from the second major information-processing system in our bodies, the central nervous system. Ultimately, our goal is to understand why these two marvelously complex information-processing systems exist in our bodies, why they are so different in their architecture, and how they complement each other.

Finally, we happily collaborate with diverse and inspiring colleagues at the Tumor Immunology department and in other research groups around the world, and we try our best to help them in their research by helping to address computational questions and identifying appropriate statistical methodology.


Show: complete list | selected publications

Author order is highlighted where it is meaningful (e.g. it is not meaningful in theoretical computer science, but it is in computational biology). If two or more authors are highlighted, this means they contributed equally.





  • Inge M N Wortel, Can Keşmir, Rob J De Boer, Judith N Mandl, Johannes Textor:
    Is T Cell Negative Selection a Learning Algorithm?
    Cells 9(3): 690, 2020. doi: 10.3390/cells9030690

    The immune system uses cell repertoires to process and store information. Here we build a computer model of such a cell repertoire and show that it's capable of distinguishing between different languages. We use this "toy problem" to show how the immune system could "learn" to distinguish foreign pathogens from the body's own cells. Our model predicts that immunological learning should benefit if the T cell repertoire is "trained" in a certain way during development. We outline experimental approaches to test our prediction.

  • Nicolas Levernier, Johannes Textor, Olivier Bénichou, and Raphaël Voituriez:
    Inverse Square Lévy Walks are not Optimal Search Strategies for d≥2.
    Physical Review Letters 124(8), 2020. doi: 10.1103/PhysRevLett.124.080601
  • Jasper JP van Beek, Georgina Flórez-Grau, Mark AJ Gorris, Till SM Mathan, Gerty Schreibelt, Kalijn F Bol, Johannes Textor, I Jolanda M de Vries:
    Human pDCs Are Superior to cDC2s in Attracting Cytolytic Lymphocytes in Melanoma Patients Receiving DC Vaccination.
    Cell Reports 30(4):1027-1038.e4, 2020. doi: 10.1016/j.celrep.2019.12.096
  • 2019













    PhD Dissertation

    My PhD thesis was carried out at the Institute of Theoretical Computer Science, University of Luebeck, Germany, under joint supervision of Rüdiger Reischuk (Theoretical Computer Science) and Jürgen Westermann (Immunology).

    Johannes Textor:
    Search and Learning in the Immune System: Models of Immune Surveillance and Negative Selection.
    PhD Dissertation, University of Luebeck, 2011.
    Download PDF


    Some software I wrote for my research is available for download. You can also visit my profile on github.

    Awards & important grants

    Dates listed refer to the year in which the award was given.