Associate Investigators

Dr. Clemens Kreutz

Dr. Clemens Kreutz


Dr. Clemens Kreutz

Institute of Medical Biometry and Statistics
(University Medical Center, Faculty of Medicine)

T +49 761 270 83741

Further Information


Statistical analyses, mathematical modelling and bioinformatic algorithms are essential for exploiting the full information content of experimental data, for drawing valid conclusions and for the integration of information from different sources. We develop, optimize and apply methods of mathematical modeling, statistical and bioinformatic analysis in systems biology and systems medicine.

Our mission is providing experiences and access to the latest computational methodology and to improve these methods by novel generic or project-specific extensions. We focus on a broad spectrum of measurement techniques applied in molecular biology, in particular on proteomics, sequencing methods and cytometry.

Many existing computational methods are not well-tested in application settings and their applicability is frequently seriously delimited and/or they have to be tailored to specific research questions. We therefore perform comprehensive benchmark studies for assessing the performance of computational approaches and for deriving guidelines of optimal selection between competing algorithms.

Our research is very relevant for CIBSS because the quantitative analysis of signaling compounds is essential. Moreover, novel modelling approaches are required for investigation and understanding of cellular signaling at multiple scales.



Bioinformatics, Experimental Planning, Mathematical Modelling, Proteomics, Sequencing, Signalling, Statistics.


10 selected publications:

  • Tyrosine kinase inhibitors can activate the NLRP3 inflammasome in myeloid cells through lysosomal damage and cell lysis.
    Neuwirt, E., …, Kreutz, C., ... Groß, O. (2023).
    Science Signaling, 16(768), eabh1083.
  • Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity.
    Fröhlich, K., Brombacher, E., Fahrner, M., Vogele, D., Kook, L., Pinter, N., ... Kreutz, C. (2022).
    Nature Communications, 13(1), 2622.
  • Data-driven prediction of COVID-19 cases in Germany for decision making.
    Refisch, L., Lorenz, F., Riedlinger, T., Taubenböck, H., Fischer, M., Grabenhenrich, L., ... & Kreutz, C. (2022).
    BMC Medical Research Methodology, 22(1), 1-13.
  • A blind and independent benchmark study for detecting differentially methylated regions in plants.
    Kreutz, C., ... , Rensing, S. A. (2020).
    Bioinformatics, 36(11), 3314-3321.
  • Benchmark problems for dynamic modeling of intracellular processes.
    Hass H, et int., Kreutz C (2019).
    Bioinformatics, 35(17), 3073-3082.
  • Guidelines for benchmarking of optimization-based approaches for fitting mathematical models.
    Kreutz C (2019).
    Genome Biology, 20(1), 281.
  • An easy and efficient approach for testing identifiability.
    Kreutz C. (2018).
    Bioinformatics, 34(11), 1913-1921.
  • Quantifying post-transcriptional regulation in the development of Drosophila melanogaster.
    Becker K, ..., Kreutz C, ... Legewie S (2018).
    Nature communications, 9(1), 1-14.
  • Resolving the Combinatorial Complexity of Smad Protein Complex Formation and Its Link to Gene Expression.
    Lucarelli, P., Schilling, M., Kreutz, C., . . . Klingmüller, U. (2018)
    Cell systems, 6(1), 75-89.e11.
  • Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells.
    Merkle, R., ... Kreutz, C., ... Klingmüller, U. (2016).
    1. PLoS Computational Biology, 12(8), e1005049