Dr. Clemens Kreutz
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.
An experienced statistician can decide on the basis of suitable analyses and correct interpretations, how a data set is to be evaluated correctly and with maximum power. I firmly believe that these decisions can be built into analytical procedures. Statistical and bioinformatics methods must be improved to provide reliable and powerful results without manual tuning and subjective judgement.
A main goal of my group is to investigate the applicability and performance of algorithms to understand how to combine the individual analysis steps in an optimal way. Thereby we want to enhance the applicability, robustness and performance of computational approaches in systems biomedicine.
10 selected publications:
- Guidelines for benchmarking of optimization-based approaches for fitting mathematical models.
Kreutz C (2019).
Genome Biol. 20(1):281.
- Benchmark problems for dynamic modeling of intracellular processes.
Hass H, Loos C, Raimúndez-Álvarez E, Timmer J, Hasenauer J, Kreutz C (2019).
- Quantifying post-transcriptional regulation in the development of Drosophila melanogaster.
Becker K, Bluhm A, Casas-Vila N, Dinges N, Dejung M, Sayols S, Kreutz C, Roignant JY, Butter F, Legewie S (2018).
Nat Commun. 9(1):4970.
- An easy and efficient approach for testing identifiability.
Kreutz C. (2018).
Bioinformatics 34(11), 1913-1921.
- Resolving the combinatorial complexity of smad protein complex formation and its link to gene expression. .
Lucarelli P, Schilling M, Kreutz C, Vlasov A, Boehm ME, Iwamoto N, . . . Klingmüller U (2018).
Cell Syst. 6(1), 75-89.e11.
- Hepatocyte ploidy is a diversity factor for liver homeostasis. .
Kreutz C, MacNelly S, Follo M, Wäldin A, Binninger-Lacour P, Timmer J, Bartolomé-Rodríguez MM (2017).
Front Physiol.8, 862. doi:10.3389/fphys.2017.00862
- L1 regularization facilitates detection of cell type-specific parameters in dynamical systems..
Steiert B, Timmer J, Kreutz C (2016).
- Profile likelihood in systems biology.
Kreutz C, Raue A, Kaschek D, Timmer J (2013).
FEBS J 280(11), 2564-2571.
- Likelihood based observability analysis and confidence intervals for predictions of dynamic models.
Kreutz C, Raue A, Timmer J (2012).
BMC Syst Biol. 6, 120.
- Systems biology: experimental design.
Kreutz C, Timmer J (2009).
FEBS J. 276(4), 923-942.