Multi-scale modelling, omics data analyses and statistical support

Dr. Clemens Kreutz (CIBSS-AI), ZBSA Center for Biological Systems Analysis, University of Freiburg

CIBSS Profile page


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. A vast variety of computational methods have been developed within recent years. However, many algorithms are not available for experimental groups or require expert knowledge for being applied. Moreover, many computational methods are not well-tested in application settings and their applicability might be seriously delimited and/or they have to be tailored to specific research questions.

In this project, we support experimental collaborators by analysing their data using computational state-of-the-art methodology. We assess the performance of existing approaches and derive guidelines of optimal selection between competing algorithms. We developed new statistical approaches as well as methodology for multi-scale modelling. Moreover, we establish, extend and optimize comprehensive data analysis pipelines for mass-spec based proteomics.


Publications resulting from the project

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, Bronsert P, Timme-Bronsert S, Schmidt A, Bärenfaller K, Kreutz C, Schilling O.
Nat Commun. 2022; doi: 10.1038/s41467-022-30094-0.

Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles.
Litwin T, Timmer J, Kreutz C.
Front Mol Biosci. 2022; doi: 10.3389/fmolb.2022.800856.