Current Projects | Universitätsklinikum Freiburg

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.

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 generate realistically simulated data to select the optimal analysis strategy. We also investigate the sensitivity/robustness of results to choice of algorithms and configuration parameters.

Responsible: Eva Kohnert, Carlotta Meyring, Clemens Kreutz

Collaborators: Oliver Schilling, Fabian Cieplik

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. We also benchmark approaches, in particular methods for data-independent acquisition (DIA).

Responsible: Eva Brombacher, 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, Fabian Cieplik, Ali Al Ahmad

AI & Foundation Models for Single Cell Data

In this project, we compare the performance of AI-based models and tests (e.g. scGPT) for analyzing of single cell data, e.g. from scRNA-seq. We also fine-tune foundation models for downstream tasks such as cell type annotation, predicting the expression of unobserved genes or for unobserved biological conditions.

Responsible: Jonatan Menger, Eva Brombacher, Clemens Kreutz

Our collaborators: Andreas Raue (Augsburg), Wolfgang Schamel (CIBSS, Freiburg), Thomas Ott (CIBSS, Freiburg), Thomas Brox (CIBSS, Freiburg)

Modelling Methods 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 low dimensional representations of the dynamics of regulation networks
further advance the RTF modelling approach as an novel modelling approach
approximating differential equation models by the RTF for multi-scale modelling
evaluate deep learning approaches in the context of ODE modelling
implement novel methods as R-package or into the Data2Dynamics modelling framework

Responsible: Niklas Neubrandt, Timo Rachel, Tim Litwin, Clemens Kreutz

Our collaborator: Olaf Groß (CIBSS, Freiburg), Jens Timmer (CIBSS, Freiburg), Jan Hasenauer (Bonn)

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

Modelling in Developmental Biology

In this project, develop spatio-temporal models of cellular signalling and cell type specification of Xenopus (cilia and mucociliary development).

Responsible: Tim Litwin, Clemens Kreutz

Collaborators: Peter Walentek (CIBSS)

Data2Dynamics

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