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
Responsible: Lukas Refisch, Janine Egert, Clemens Kreutz
Our collaborators: Jens Timmer, Andreas Raue