Former Projects

ExITox/ ExlTox2

ExITox/ ExlTox2

Acronym: ExITox (Explain Inhalation Toxicity)

Full title of the project: “Development of an integrated testing strategy for the prediction of toxicity after repeated dose inhalation exposure: a proof of concept”

This project aims at developing an integrated testing strategy (ITS) for the human health risk assessment of repeated dose toxicity after inhalation exposure for the replacement of de novo animal testing. In the pilot phase chemicals with different mode of action will be selected and tested with human precision cut lung slices (PCLS) and human pulmonary cell cultures in order to identify route specific biomarkers. Genome wide transcriptome analyses will be conducted in these models and evaluated using bioinformatics methods. These results will be complemented with data mining results and QSAR predictions. Further, structurally related chemicals will be tested in addition to investigate the possibility of the test system to support read across. The outcome of this pilot project will be a proposal for an integrated testing strategy for respiratory toxicity. Further validation e.g. testing of a broader spectrum of chemicals is foreseen in a follow up project. The proposed ITS, the developed methodologies on data sharing and data integration are not limited to the evaluation of transcriptome data but allow to integrate proteome and metabolome data in a follow up project.

The project is funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the call e:ToP.

Funding period: 01.11.2013 – 31.10.2015


Dr. S. Escher, Fraunhofer ITEM, Hannover (coordinator)
Dr. K. Sewald, Airway Immunology, Fraunhofer ITEM, Hannover
Dr. M. Niehof, In Vitro and Mechanistic Toxicology, Fraunhofer ITEM, Hannover
Dr. C. Helma, Inst. f. Physics/In Silico Toxicol. Group, Albert Ludwigs University of Freiburg
Dr. A. Kel, geneXplain GmbH, Wolfenbüttel


Bhar, A., Haubrock, M., Mukhopadhyay, A., Wingender, E:
Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes
BMC Bioinformatics 16, 200 (2015).

Koschmann, J., Bhar, A., Stegmaier,P., Kel, A.E. and Wingender, E.:
“Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data
Microarrays 4, 270-286 (2015).


The Department of Bioinformatics is participating in the Heart Research Center Göttingen (HRGC, a partner in the German Center for Cardiovascular Research / Deutsches Zentrum für Herz-Kreislauf-Forschung, DZHK).

The frame of the collaboration comprises several topics. Current research is focused on the evaluation of the DNA microarray experiments carried out at the Department of Cardiology and Pneumology, at the Department of Pharmacology, as well as by other participants of the HRCG. The immense recent growth in knowledge in the systems biology and network analysis has established new benchmarks in the analysis of microarray data. In the frame of the collaboration, we aim at an interdisciplinary integration of the partner's experimental knowledge in tissue engineering with case-tailored statistical and computational methodologies in order to help experimenters in analyzing their data by bringing together input from all aspects of theoretical molecular biology.

These sources comprise the wealth of knowledge contained in the TRANSPATH and TRANSFAC libraries on signal transduction and transcription factor binding sites, combined with a unique tool set designed to evaluate the TRANSFAC data for the benefit of binding site prediction. Moreover, we envisage to incorporate the state of the art in computational statistics, dealing with its considerable challenges from multiple testing and high-dimensionality with intricate dependency structures.


Zeidler, S., Meckbach, C., Tacke, R., Raad, F. S., Roa, A., Uchida, S., Zimmermann, W. H., Wingender, E. and Gültas, M.:
Computational detection of stage-specific transcription factor clusters during heart development
Front. Genet. 7, 33 (2016).
doi: 10.3389/fgene.2016.00033


Full Title of the project: Lipid droplets as dynamic organelles of fat deposition and release: Translational research towards human disease

The project aims to exploit the recent developments in lipidomics technology to establish high-throughput methods, to define druggable targets and novel biomarkers related to lipid droplet (LD) composition. It focuses on lipid protein interactions and investigates the dynamics of fat deposition and release in relevant cells as a hallmark of energy overload diseases with major health care impact in Europe.

Funded by the European Commission within FP7, under the thematic area "High throughput analysis of lipid and lipid-protein interactions", contract number HEALTH 2007-2.1.1-6


University Regensburg (Prof. G. Schmitz, coordinator)

24 further partners

Project page:


In the course of this project, Bioinformatics/UMG has significantly updated the EndoNet database on intercellular signaling pathways, especially by pathways that are relevant to control human lipid metabolism. The EndoNet database was equipped with a new user interface, and its structure was largely redesigned and enriched by new contents, relations, and functions. EndoNet was integrated under the BioUML platform of partner P28 (Institute for Systems Biology, Novosibirsk, Russia)

Also the connected Cytomer ontology was revised and updated. To facilitate re-use of this and other ontologies, a novel tool for embedding the contents of ontologies in other applications was deviced (OBA, Ontology Based Answers) and made publicly available.


Dönitz, J. and Wingender, E.:
The ontology-based answers (OBA) service: A connector for embedded usage of ontologies in applications
Front. Gene. 3, 197 (2012).

Wingender, E., Schoeps, T. and Dönitz, J.:
TFClass: An expandable hierarchical classification of human transcription factors
Nucleic Acids Res. 41, D165-D170 (2013).

Li, J., Hua, X., Haubrock, M., Wang, J. and Wingender, E.:
The architecture of the gene regulatory networks of different tissues
Bioinformatics 28, i509-514 (2012).

Potapov, A. P., Goemann, B. and Wingender, E.:
The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks
BMC Bioinformatics 9, 227 (2008).


Acronym: MetastaSys – investigating the systems biology of metastasis

Full title of the project: “Analysis of Molecular Markers and Pathways in Cancer Cells and Microenviroment that determine the Fate and Localization of Tumor Metastases”

The aim of the project is to identify molecular markers and pathways in cancer cells and their microenvironment that govern the fate and localization of tumor metastases.

The project is funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the call e:bio.

Funding period: 01.02.2013-31.01.2015

Prof. Dr. T. Beissbarth, Department for Medical Statistics, UMG
8 partners from UMG (Göttingen) and DKFZ (Heidelberg)

Project web site:

Wlochowitz, D., Haubrock, M., Arackal, J., Bleckmann, A., Wolff, A., Beissbarth, T., Wingender, E. and Gültas, M.:
Computational identification of key regulators in two different colorectal cancer cell lines
Front. Genet. 7, 42 (2016).
doi: 10.3389/fgene.2016.00042

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