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Institut für Medizinische Biometrie
und Statistik (IMBI)

Ausgewählte Projekte

The goal of MSB is to provide expertise on state-of-the-art methods for the analysis of the most important high-throughput experiments and for mathematical modelling for cooperation partners. We also develop customized methods that go beyond standard analyses and pipelines. In doing so, we try to transfer findings and methodological approaches for data analysis to new fields of research.

Bioinformatic analyses in proteomics

In this project, we develop computational approaches for analysis of mass-spectrometry based proteomics data. We currently focus on dealing optimally with missing values and on data preprocessing like normalization.

Responsible: Janine Egert, Clemens Kreutz

Our collaborators: Oliver Schilling, Bettina Warscheid

Statistical and bioinformatic analyses of microbiome data

In this project, we develop and apply statistical approaches for analysis of sequencing-based microbiome data. We currently focus on statistical models that account for the compositional nature, excessive zero counts, overdispersion, and patient-specific effects.

Responsible: Eva Kohnert, Clemens Kreutz

Our collaborators: Ann-Kathrin Lederer, Roman Huber, Nadine Binder

Prediction of local COVID-19 progression

In this project, we analyze the IfSG data of the confirmed COVID-19 cases at the level of counties (Landkreise) and provide daily updated predictions for new infections for all ICUs in Germany.

Responsible: Lukas Refisch, Fabian Lorenz, Clemens Kreutz

Our collaborators: Robert Koch Institute (RKI), Federal Institute for Population Research (BIB), German Aerospace Center (DLR)

COVID-19 Surveillance of Hospitalized Patients

In this project, we optimize surveillance and test strategies for patients and employees in the Oberbergkliniken based on agent-based stochastical dynamical models.

Responsible: Tim Litwin, Jens Timmer, Clemens Kreutz

Our collaborators: Matthias Müller, Andreas Wahl Kordon, Marcus Panning, Mathias Berger

Modelling in Systems Biology

In this project, we develop and extend existing methodology for establishing mathematical models of biochemical processes in living cells. We focus on

  • development of reliable and robust optimization methods for parameter estimation
  • deriving a valid statistical methodology for parameter and prediction uncertainties as well as for identifiablity and observability analyses
  • establishing strategies like L1 regularization for deriving small minimal models
  • approximating differential equation models by functions as a prerequisite for multi-scale modelling
  • evaluate deep learning approaches in the context of ODE modelling

Responsible: Lukas Refisch, Clemens Kreutz

Our collaborator: Jens Timmer, Andreas Raue (Merrimack Pharm.)

Benchmarking in Systems Biology

In this project, we evalute the performance of existing computational methods for mathematical modelling.

  • We compare the performance of optimization methods which are applied for model calibration/parameter estimation
  • We establish benchmark problems which can be used to assess modelling methods
  • We develop and extend methodology for performing reliable benchmark studies
  • We establish approaches for generation of simulation data realistically

Link: Benchmark models on github

Responsible: Lukas Refisch, Janine Egert, Clemens Kreutz

Our collaborators: Jens Timmer, Jan Hausenauer


In this project, we develop and extend the Data2Dynamics modelling environment which is a high-performance expert implementation for mathematical modelling.

Link: D2D repository at github

Responsible: Lukas Refisch, Janine Egert, Clemens Kreutz

Our collaborators: Jens Timmer, Andreas Raue

Optimizing adaptamer arrays

In this project, we analyze kinetic data of adaptamer binding and dissociation by mathematical models. We assess binding characteristics and develop predictive models for testing new adaptamer sequences.

Responsible: Lukas Refisch, Clemens Kreutz

Our collaborator: Günter Roth

Optimal Personalized Treatment of Anemia

In this project, we apply a mathematical model of EPO binding and its effect on erythropoesis to predict the optimal time- and dosage strategy for treating anemia. We consider cancer as well as patients with chronic kidney disease.

Responsible: Lukas Refisch, Clemens Kreutz

Our collaborators: Ursula Kingmüller, Jens Timmer

Identification of Differentially Methylated Regions (DMRs) from Bisulfite Sequencing (BSSEQ) Data

In this project, we compare and assess the performances of algorithms for detecting differentially methylated regions (DMRs) from bisulfite sequencing (BSSEQ) data.

Responsible: Clemens Kreutz

Our collaborators: Stefan Rensing

Clustering of Mass Cytometry Data

In this project, we compare the performance of unsupervised machine learning methods (e.g. clustering) for processing of mass cytometry based single cell data.

Responsible: Ariane Schad, Clemens Kreutz

Our collaborators: Stefan Reinker (Novartis)

Benchmarking Wiki

In this project, we started to develop a wiki as a repository for published benchmark studies. We intend to collect insights from literature obtained by comparing the performance of computational approaches.

The benchmarking wiki also offers the opportunity to publish (possibly small or unpublished) own results.

Everybody is welcome to contribute!

Link: Benchmark Wiki

Responsible: Clemens Kreutz

Modelling of Pattern Formation

In this project, develop spatio-temporal models of cellular signalling during morphogenesis in Drosophila (wing formation) and Xenopus (cilia and mucociliary development).

Responsible: Clemens Kreutz

Collaborators: Georgios Pyrowolakis, Peter Walentek

Analysis Pipelines for High-Throughput Data

In this project, we apply and optimize comprehensive data analysis pipelines from raw data to high-level statistical analyses. We also investigate the sensitivity/robustness of results to choice of algorithms and configuration parameters.

Responsible: Clemens Kreutz

Collaborators: Oliver Schilling, Bettina Warscheid, Ursula Kingmüller


Dr. Clemens Kreutz

  • Development, assessment and improvement of computational and statistical methods
  • Mathematical modelling, high-throughput/omics data analyses

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