Data Analysis and Algorithms Selection Group
- Benchmarking in Computational Biology -
Center for Biological Systems Analysis (ZBSA)
University of Freiburg
Open Positions Research summary Group members Publications CV Software packages Data Awards Datenschutz
  Dr. Clemens Kreutz

Dr. rer. nat. Clemens Kreutz

Junior Group Leader

Freiburg Center for Systems Biology (ZBSA)
Freiburg Center for Data Analysis and Modeling (FDM)
Institute of Physics

University of Freiburg
Habsburger Str. 49
79104 Freiburg

ckreutz at
Phone: ++49 761 203 97207
Phone: ++49 761 203 8533

Private Adresse:
Bergackerweg 7
79874 Breitnau
07652 917829



Benchmarking in Computational Biology

Our Wiki for benchmarking results
Our 20 benchmark models on github


Modelling Workshop

In September 2017 we are hosting a workshop about ODE-based modelling and applications in systems biology.
Here is a link to the website.


Research Summary

  • Analysis of complex and high-dimensional data
  • Development of new algorithms and statistical approaches
  • Machine learning/deep learning for classification, prediction and pattern recognition
  • Uncertainty analyses
  • Experimental design
  • Mathematical modelling of biochemical reaction networks
  • Transcriptomics, proteomics, cytometry, imaging
  • Cancer
  • Insulin-, growth factor-, apoptotic- and TGFb signal transduction
  • Linking signal transduction, genomics and transcription to phenotype
  • Biomarker
  • Benchmarking
  • Selection and optimization of algorithms for specific application settings

Open Positions

  One open PostDoc position (starting 1.4.2019 or later) is available upon request.

Further PostDoc positions are currently available upon request.
A very strong expertise in statistics, bioinformatics, systems biology, or computational biology is required.

  A PhD position in Physics/Mathematics is currently available (starting 1.1.2019 or later).
The theses will cover the following topics:
(1) Enhancing mathematical modelling approaches for the requirements of multi-scale models
(2) Mathematical modelling of multi-scale differentiation processes in collaboration with experimental partners
(3) Development of new algorithms for analyses of mass-spectromety based proteomics data

  Master theses typically comprise application of a statistical method or modelling technique as well as assessing its performance.
The exact topic and details of a master thesis depend on the starting time and can be demanded (please send email).

One topic is described here: Current master topic. Other topics are available on request.

  We currently offer two topics for Bacholor thesis (SS 2019 or WS19/20):
  1. Topic: Implementation of a Neural ODE model (which is a combination of neural network and ordinary differential equation model) and first evaluation of feasibility and fitting performance in our Data2Dynamics environment.
  2. Topic: Automatic adaptation of tolerances of numerical algorithms used for estimating parameters in dynamical systems (ODEs) by a reinforcement learning approach and performance evaluation.
Further information about these topics are available upon requrest.


Group Members



S. Bernhardt, C. Toensing, D. Mitra, N. Erdem, K. Mueller-Decker, U. Korf, C. Kreutz, J. Timmer, S. Wiemann
Functional proteomics of breast cancer metabolism identifies GLUL as responder during hypoxic adaptation
J. Proteome Res, 2019, 18, 3, 1352-1362.

H. Hass, C. Loos, E. Raimundez Alvarez, J. Timmer, J. Hasenauer, C. Kreutz
Benchmark Problems for Dynamic Modeling of Intracellular Processes
Bioinformatics, btz020, 2019, accepted.
BioRxiv preprint version

K. Becker, A. Bluhm, N. Casas-Vila, N. Dinges, M. Dejung, S. Sayols, C. Kreutz, J. Roignant, F. Butter, S. Legewie
Quantifying post-transcriptional regulation in the development of Drosophila melanogaster
Nature Communications, 9: 4980, 1-14, 2018.

R. Seitz-Alghrouz, J. Hidalgo, C. Kayser, C. Kreutz, K. Technau-Hafsi, C. Diaz, A. von Deimling, J. Timmer, M. Werner, M. Malkovsky, P. Fisch
BRAF V600E Mutations in Nevi and Melanocytic Tumors of Uncertain Malignant Potential (MELTUMPs)
Journal of Investigative Dermatology, 2018, 138(11), 2489-2491.

C. Kreutz
An easy and efficient approach for testing identifiability of parameters.
Bioinformatics, 34(11), 1913-1921, 2018.
arXiv-preprint (Aug, 2017)

P. Lucarelli, M. Schilling, C. Kreutz, A. Vlasov, M. Boehm, N. Iwamoto, B. Steiert, S. Lattermann, M. Waesch, M. Stepath, M. Matter, M. Heikenwaelder, K. Hoffmann, D. Deharde, G. Damm, D. Seehofer, M. Muciek, N. Gretz, W. Lehmann, J. Timmer, U. Klingmueller
Resolving the Combinatorial Complexity of Smad Protein Complex Formation and the Link to Gene Expression
Cell Systems, 2018, 24;6(1):75-89.e11.

C. Toensing, C. Kreutz, J. Timmer
Profile likelihood based analyses of infectious disease models.
Statistical Methods in Medical Research, 2018, 27(7), 1979-1998.

C. Kreutz, S. MacNelly, M. Follo, A. Waeldin, P. Binninger-Lacour, J. Timmer, M. Bartolome Rodriguez
Hepatocyte ploidy is a diversity factor for liver homeostasis Front. Physiol. (8) 862, 2017.

P. Fisch, S. Rathmann, C. Keck, C. Kreutz, N. Weit, M. Mueller, J. Timmer, S. Glatzel, M. Follo, M. Malkovsky, J. Finke, R. Handgretinger, M. Werner.
Partial break in tolerance of NKG2A-/LIR-1- single KIR+ NK cells early in the course of HLA matched, KIR mismatched hematopoietic cell transplantation. Bone Marrow Transplantation , 2017, 52(8), 1144-1155.

O. Ucar, K. Li, D. Dvornikov, C. Kreutz, J. Timmer, S. Matt, L. Brenner, C. Smedley, M.A. Travis, T.G. Hofmann, U. Klingmueller B. Kyewski.
A thymic epithelial stem cell pool persists throughout ontogeny and is modulated by TGF-beta.
Cell Reports 17, 448-457, 2016.

C. Kreutz
New Concepts for Evaluating the Performance of Computational Methods.
IFAC-PapersOnLine (2016) 49(26): 63-70.

Tim Maiwald, H. Hass, B. Steiert, J. Vanlier, R. Engesser, A. Raue, F. Kipkeew, H. Bock, D. Kaschek, C. Kreutz, J. Timmer
Driving the Model to Its Limit: Profile Likelihood Based Model Reduction
PLoS ONE (2016) 11(9): e0162366. doi: 10.1371/journal.pone.0162366

H. Binder, T. Kurz, S. Teschner, C. Kreutz, M. Geyer, J. Donauer, A. Kraemer-Guth, J. Timmer, M. Schumacher and G. Walz
Dealing with prognostic signature instability: a strategy illustrated for cardiovascular events in patients with end-stage renal disease
BMC Medical Genomics (2016) 9(43), doi:10.1186/s12920-016-0210-9

B. Steiert, J. Timmer, C. Kreutz
L1 regularization facilitates detection of cell type-specific parameters in dynamical systems.
Bioinformatics (2016) 32(17): i718-i726 doi:10.1093/bioinformatics/btw461

R. Merkle, B. Steiert, F. Salopiata, S. Depner, A. Raue, N. Iwamoto, M. Schelker, H. Hass, M. Waesch, M. Boehm, O. Muecke, D. Lipka, C. Plass, W. Lehmann, C. Kreutz, J. Timmer, M. Schilling, U. Klingmueller
Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells.
PLoS Comput Biol (2016) 12(8): e1005049. doi:10.1371/journal.pcbi.1005049

H. Hass, C. Kreutz J. Timmer and D. Kaschek
Fast integration-based prediction bands for ordinary differential equation models.
Bioinformatics (2016) 32 (8): 1204-1210, doi:10.1093/bioinformatics/btv743

C. Kreutz, A. Raue, J. Timmer
Statistics for Model Calibration
Multiple Shooting and Time Domain Decomposition Methods, Vol. 9 of the series Contributions in Mathematical and Computational Sciences, 2015, pp 355-375

L.O. Schwen, A. Schenk, C. Kreutz J. Timmer, M.M. Bartolome Rodriguez, L. Kuepfer, T. Preusser
Representative sinusoids for hepatic four-scale pharmacokinetics simulations.
Plos One (2015) 10, e0133653

A. Raue, B. Steiert, M. Schelker, C. Kreutz T. Maiwald, H. Hass, J. Vanlier, C. Toensing, L. Adlung, R. Engesser, W. Mader, T. Heinemann, J. Hasenauer, M. Schilling, T. Hoefer, E. Klipp, F. Theis, U. Klingmueller, B. Schoeberl, J. Timmer.
Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems
Bioinformatics (2015) 31 (21): 3558-3560

J. Karr, A. Williams, J. Zucker, A. Raue, B. Steiert, J. Timmer, C. KreutzS. Wilkinson, B.Allgood, B. Bot, B. Hoff, M. Kellen, M. Covert, G. Stolovitzky, P. Meyer, DREAM8 Parameter Estimation Challenge Consortium
Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models
Plos Comput Biol. (2015) 11(5), e1004096

Goldmann T, Zeller N, Raasch J, Kierdorf K, Frenzel K, Ketscher L, Basters A, Staszewski O, Brendecke SM, Spiess A, Tay TL C. Kreutz Timmer J, Mancini GM, Blank T, Fritz G, Biber K, Lang R, Malo D, Merkler D, Heikenwaelder M, Knobeloch KP, Prinz M
USP18 lack in microglia causes destructive interferonopathy of the mouse brain
The EMBO journal, 2015, e201490791

C. Toensing, J. Timmer, C. Kreutz
On the cause and cure of sloppiness in ordinary differential equation models
Phys. Rev. E (2014) 90, 023303
arXiv preprint version

S. Wahane, N. Hellbach, M. Prentzell, S. Weise, R. Vezzali, C. Kreutz, J. Timmer, K. Krieglstein, K. Thedieck, T. Vogel
PI3K-p110-alpha-subtype-signalling mediates survival, proliferation and neurogenesis of cortical progenitor cells via activation of mTORC2
JNC (2014) 130(2):255-267

K. Aumann, A.-V. Frey, A.M. May, D. Hauschke, C. Kreutz, J.P. Marx, J. Timmer, M. Werner, H.L. Pahl
Differenzialdiagnose myelproliferativer Neoplasien
Der Pathologe 34:201-209, DOI 10.1007/s00292-013-1824-8

Meyer, P.; Cokelaer, T.; Chandran, D.; Kim, K. H.; Loh, P.-R.; Tucker, G.; Lipson, M.; Berger, B.; Kreutz, C. Raue, A.; Steiert, B.; Timmer, J.; Bilal, E.; DREAM6 and DREAM7 Consortium; Sauro, H. M.; Stolovitzky, G. & Saez-Rodriguez, J.
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach. BMC systems biology, 8(1), 1, 2014

Raue A, Schilling M, Bachmann J, Matteson A, Schelker M, Kaschek D, Hug S, Kreutz C, Harms BD, Theis F, Klingmueller U and Timmer J.
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology.
PLOS ONE 8(9), e74335, 2013

Raue A, Kreutz C, Theis F and Timmer J.
Joining Forces of Bayesian and Frequentist Methodology: A Study for Inference in the Presence of Non-Identifiability.
Phil. Trans. Roy. Soc. A, 371, 20110544, 2013;

Kreutz C, Raue A, Kaschek D, and Timmer J.
Profile Likelihood in Systems Biology.
FEBS Journal, 2013;

A. Yalcin, C. Kreutz, D. Pfeifer, M. Abdelkarim, G. Klaus, J. Timmer, M. Luebbert, B. Hackanson
MeDIP coupled with a promoter tiling array as a platform to investigate global DNA methylation patterns in AML cells.
Leukemia Research 2012;

C. Kreutz, A. Raue, J. Timmer
Likelihood based observability analysis and confidence intervals for predictions of dynamic models.
BMC Systems Biology 2012; 6; doi:10.1186/1752-0509-6-120
arXiv preprint version 2011

Schelker M, Raue A, Timmer J and Kreutz C
Comprehensive estimation of input signals and dynamical parameters in biochemical reaction networks.
Bioinformatics, 28(18), i522-i528, 2012;

B. Steiert, A. Raue, J. Timmer, C. Kreutz
Experimental design for parameter estimation of gene regulatory networks.
PLoS ONE 2012; 7, e40052; doi:10.1371/journal.pone.0040052

K Sa Ferreira, C Kreutz, S. MacNelly, K Neubert, A Haber, M Bogyo, J Timmer, C Borner
Caspase-3 feeds back on caspase-8, Bid and XIAP in type I Fas signaling in primary mouse hepatocytes
Apoptosis 2012; 17(5), 503-515; doi: 10.1007/s10495-011-0691-0

Kreutz C, Gehring JS, Lang D, Reski R, Timmer J, Rensing SA
TSSi - An R package for transcription start site identification from 5' mRNA tag data
Bioinformatics 2012; doi: 10.1093/bioinformatics/bts189

Bachmann J, Raue A, Schilling M, Bohm ME, Kreutz C, Kaschek D, Busch H, Gretz N, Lehmann WD, Timmer J, Klingmueller U.
Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range.
Mol Syst Biol., 2011, 7, 516.

Raue A, Kreutz C, Maiwald T, Klingmueller U, Timmer J.
Addressing Parameter Identifiability by Model-Based Experimentation
IET Systems Biology, 2011, 5(2), 120-130.

Zellmer S, Schmidt-Heck W, Bauer A, Meyer C, Lehmann T, Sparna T, Godoy P, Amin P, Schormann W, Bedawy E, Hammad S, Kern C, Kreutz C, Timmer J, Walz G, von Weizs\"acker F, Thürmann PA, Dooley S, Merfort I, Guthke R, Hengstler JG, Gebhardt R.
The transcription factors ETF, E2F and SP-1 are involved in cytokine-independent proliferation of murine hepatocytes.
Hepatology , 2010, 52(6), 2127-2136.

Schilling M, Maiwald T, Hengl S, Winter D, Kreutz C, Kolch W, Lehmann WD, Timmer J, Klingmüller U
Theoretical and experimental analysis links isoform-specific ERK signalling to cell fate decisions.
Mol Syst Biol. 2009;5:334.

Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, Timmer J.
Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.
Bioinformatics. 2009 Aug 1;25(15):1923-9.

Bartholome K., Kreutz C., Timmer J.
Estimation of gene induction enables a relevance-based ranking of gene sets
J Comput Biol. 2009 Jul;16(7):959-67.

Kreutz C, Timmer J.
Systems biology: experimental design.
FEBS J. 2009 Feb;276(4):923-42

Rumberger B, Kreutz C, Nickel C, Klein M, Lagoutte S, Teschner S, Timmer J, Gerke P, Walz G, Donauer J.
Combination of immunosuppressive drugs leaves specific "fingerprint" on gene expression in vitro.
Immunopharmacol Immunotoxicol. 2009 Mar 9:1-10

Lassmann S, Kreutz C, Schoepflin A, Hopt U, Timmer J, Werner M.
A novel approach for reliable microarray analysis of microdissected tumor cells from formalin-fixed and paraffin-embedded colorectal cancer resection specimens.
J Mol Med
. 2009 Feb;87(2):211-24.
Maiwald, T; Kreutz, C; Pfeifer, AC; et al.
Dynamic pathway modeling - Feasibility analysis and optimal experimental design

Rumberger, B; Vonend, O; Kreutz, C; et al.
cDNA microarray analysis of adaptive changes after renal ablation in a sclerosis-resistant mouse strain
KIDNEY & BLOOD PRESSURE RESEARCH, 30 (6): 377-387 2007

Hengl, S; Kreutz, C; Timmer, J; et al.
Data-based identifiability analysis of non-linear dynamical models
BIOINFORMATICS, 23 (19): 2612-2618 OCT 1 2007

Kreutz, C; Rodriguez, MMB; Maiwald, T; et al.
An error model for protein quantification
BIOINFORMATICS, 23 (20): 2747-2753 OCT 15 2007

Lindenmeyer, MT; Kern, C; Sparna, T;  Donauer J, Wilpert J, Schwager J, Porath D, Kreutz C, Timmer J , Merfort I 
Microarray analysis reveals influence of the sesquiterpene lactone parthenolide on gene transcription profiles in human epithelial cells
LIFE SCIENCES, 80 (17): 1608-1618 APR 3 2007

Pfeifer, D; Pantic, M; Skatulla, I; Rawluk J, Kreutz C, Martens UM, Fisch P, Timmer J, Veelken H
Genome-wide analysis of DNA copy number changes and LOH in CLL using high-density SNP arrays
BLOOD, 109 (3): 1202-1210 FEB 1 2007

Klingmuller, U; Bauer, A; Bohl, S; Nickel PJ, Breitkopf K, Dooley S, Zellmer S, Kern C, Merfort I, Sparna T, Donauer J, Walz G, Geyer M, Kreutz C, Hermes M, Gotschel F, Hecht A, Walter D, Egger, Neubert K, Borner C, Brulport M, Schormann W, Sauer C, Baumann F, Preiss R, MacNelly S, Godoy P , Wiercinska E, Ciuclan L, Edelmann J, Zeilinger K, Heinrich M , Zanger UM, Gebhardt R, Maiwald T, Heinrich R, Timmer J, von Weizsacker F, Hengstler JG 
Primary mouse hepatocytes for systems biology approaches: a standardized in vitro system for modelling of signal transduction pathways

Schieren, G; Rumberger, B; Klein, M; Kreutz C, Wilpert J, Geyer M, Faller D, Timmer J, Quack I, Rump LC, Walz G, Donauer J
Gene profiling of polycystic kidneys

Fang, X; Zeisel,MB; Wilpert, J; Gissler B, Thimme R, Kreutz C, Maiwald T, Timmer J, Kern WV, Donauer J, Geyer M, Walz G, Depla E, von Weizsacker F, Blum HE, Baumert TF
Host cell responses induced by hepatitis C virus binding
HEPATOLOGY, 43 (6): 1326-1336 JUN 2006

Schilling, M; Maiwald, T; Bohl, S; Kollmann M, Kreutz C, Timmer J, Klingmuller U
Quantitative data generation for systems biology: the impact of randomisation, calibrators and normalisers

Schilling, M; Maiwald, T; Bohl, S;  Kollmann M, Kreutz C, Timmer J, Klingmuller U
Computational processing and error reduction strategies for standardized quantitative data in biological networks
FEBS JOURNAL, 272 (24): 6400-6411 DEC 2005

Goerttler, PS; Kreutz, C; Donauer, J; et al.
Gene expression profiling in polycythaemia vera: overexpression of transcription factor NF-E2

Fang, X; Wilpert, J; Barth, H; Gissler B, Kreutz C, Timmer J, Donauer J, von Weizsacker F, Blum HE, Baumert TF
Binding of hepatitis C virus envelope to target cells induces a cascade of cell signals important for antiviral immune responses and lipid metabolism.
HEPATOLOGY, 40 (4): 451A-451A Suppl. 1 OCT 2004

Goerttler, PS; Faller, D; Donauer, J; Klein M, Kreutz C, Maiwald T, Rumberger B, Sparna T, Timmer J, Wilpert J, Walz G, Pahl HL
cDNA microarray analysis of patients with polycythemia vera and secondary erythrocytosis.
BLOOD, 102 (11): 147A-147A 505 Part 1 NOV 16 2003

Kreutz, C; Honerkamp J.
Controlling the continuos positive airway pressure-device using partial observable Markov decision processes


PhD and diploma thesis

Clemens Kreutz.
Statistical Approaches for Molecular and Systems Biology
PhD Thesis, University of Freiburg, 2012.

Clemens Kreutz.
Steuerung Stochastischer Systeme
Dipoma thesis, University of Freiburg, 2003.


The data from the manuscript An error model for protein quantification

Software packages

A Matlab package tailored to efficient parameter estimation and model inference:
Data2Dynamics Software (D2D)

Bioconductor R-package for the estimation of the number of regulated genes in a gene set:
Gene Set Regulation Index (GSRI)

Bioconductor R-package for Identifying Regulation by Tiling Microarray Data:
Loci of Enhanced Significance (LES)

Bioconductor R-package for Transcription Start Site Prediciton (TSS):
Transcription Start Site Identification (TSSi)


Certificate for best performing in the DREAM6 parameter estimation challenge.
Certificate for best performing in the DREAM7 network inference challenge.
Certificate for best performing in the DREAM8 whole cell parameter inference challenge.


1. Verantwortlich im datenschutzrechtlichen Sinne:

Clemens Kreutz
Habsburger Strasse 49
79098 Freiburg
ckreutz at (replace " at " by @)

2. Datenschutzbeauftragter

Albert-Ludwigs-Universität Freiburg
datenschutz at (replace " at " by @)

3. Ihre Rechte

  • Sie haben das Recht, von der Universität Auskunft über die zu Ihrer Person gespeicherten Daten zu erhalten und/oder unrichtig gespeicherte Daten berichtigen zu lassen.
  • Sie haben darüber hinaus das Recht auf Löschung oder auf Einschränkung der Verarbeitung oder ein Widerspruchsrecht gegen die Verarbeitung.
  • Sie haben das Recht die Sie betreffenden personenbezogenen Daten, die Sie uns bereitgestellt haben, in einem strukturierten, gängigen und maschinenlesbaren Format zu erhalten, und Sie habe das Recht, diese Daten einem anderen Verantwortlichen ohne Behinderung durch uns zu übermitteln.
  • Sie haben das Recht auf Beschwerde bei der Aufsichtsbehörde, wenn Sie der Ansicht sind, dass die Verarbeitung der Sie betreffenden personenbezogenen Daten gegen die Rechtvorschriften verstösst.

Die zuständige Aufsichtsbehörde ist der Landesbeauftragte für den Datenschutz und die Informationsfreiheit Baden-Württemberg.

4. Zugriff auf die Webseite

Die Webseite verwendet keine Cookies oder sonstige Tools, die personenbezogene Daten abspeichern oder analysieren.
Denn können serverseitig automatisch Informationen allgemeiner Natur erfasst werden. Diese Informationen (Server-Logfiles) beinhalten etwa die Art des Webbrowsers, das verwendete Betriebssystem, den Domainnamen Ihres Internet-Service-Providers und ähnliches. Hierbei handelt es sich ausschließlich um Informationen, welche keine Rückschlüsse auf Ihre Person zulassen. Diese Informationen sind technisch notwendig, um von Ihnen angeforderte Inhalte von Webseiten korrekt auszuliefern und fallen bei Nutzung des Internets zwingend an.