Former Projects


A knowledge base for generating patient-specific pathways for individualized treatment decicions in clinical applications

Molecular biomarkers play an increasing role for the diagnosis and prediction of progression or therapy response in complex diseases such as cancer. In modern Systems Medicine approaches the aim is to look at increasingly complex interactions of complete signaling pathways in order to get a more holistic view for individualized treatment decisions. Individualized treatment decisions and newly developed specialized drugs warrant the need to broaden the focus in individualized medicine from singular biomarkers to pathways. Furthermore, the Omics-era enables research to incorporate whole genome, transcriptome and proteome views of the patient status. On the one hand genomics technologies allow the parallel measurement of many different components of the system. On the other hand pathway databases offer vast amounts of knowledge on biological networks, freely available and encoded in semi-structured formats. However, the vast amount of published data on molecular interactions makes it increasingly challenging for life science researchers to find and extract the most relevant information. Currently, the tools to use this information and integrate it in a clinical context are still lacking.

This project aims at providing more efficient data use in Systems Medicine by integrating patient clinical and genomics data with pathway knowledge. The goal is to present the most relevant, meaningful and interpretable patient-specific pathways to clinicians and researchers in order to enable further medical and pharmaceutical insights. In particular our approach will generate a knowledge base and methods to generate context-specific pathways, i.e. patient-specific, disease-specific or cohort-specific pathways. The project will deliver condensed knowledge of molecular networks in order to stratify and analyze sample groups in a clinical research environment. The knowledge base will utilize an innovative Software-as-a- Service architecture to receive, store and deliver data. The module-based architecture facilitates an inter- play with the local clinical information system, the cancer registry and the geneXplain platform for bioinformatics analyses. Internally, a nanopublication-inspired metadata store will be created to dynamically link data of multiple knowledge domains. This project systematically integrates established ontologies, databases, tools and unstructured patient data using metadata annotations in order to offer a refined view on patient-specific pathway knowledge. Specifically our aims are:

  • Link clinical information systems with patient-specific Omics data and generate a tool for data integration and easy access in a clinical environment.
  • Collect information from public pathway and literature databases and develop methods and tools to generate context-specific pathways from individual patient data.
  • Apply the new tools and methods to data on colorectal and metastatic cancer to test the utility in clinical practice and the benefit in a clinical research setting.
  • Integrate the developed tools into the geneXplain platform to make them available to a broader community of scientists and clinicians and to test them in practical applications.

With this we aim to reduce the gap between patient-centered routine documentation and ontology-driven pathway and gene annotation. Thus, we establish a seamless data-flow from single patient data to Systems Medicine as a clinical resource and as a tool for knowledge discovery. We will implement and validate a software that offers a consistent, intuitive annotation of this data for the following user groups:

  1. Systems Medicine researchers, as a tool to annotate data and interpret data from clinical cohorts.
  2. Medical doctor with research focus, as a tool to enhance interaction with bioinformatics researchers.
  3. Medical doctor in patient care, as a tool to improve clinical diagnosis and decision making.

The developed software will be used and validated in projects with a clinical research aim first. Here the goal will be to test the developed tools in a clinical setting on data from patients with colorectal carcinomas collected and annotated within the clinical research group 179 (KFO179) and on data from patients with metastases collected in the MetastaSys project. In the long term we aim to establish this as a tool that can be used in clinical research as well as in clinical routine. For example, the tumor conference, a local board where the specific data and indications of individual cancer patients are discussed, would greatly benefit from this tool. This vision would be a first step on the way towards providing services similar to the online service Patients like me1, where patients can enter searches like “show me a summary of the existing data and tell me what it means in the context of the current literature”.

Funding period: 01.04.2016 - 31.12. 2021

The project was funded by the Federal Ministry of Education and Research (BMBF) within the initiative “i:DSem – Integrative Datensemantik in der Systemmedizin”.


Prof. Dr. Tim Beißbarth
Director of the Institut Medical Bioinformatics , University Medical Center Göttingen 

Prof. Dr. Frank Kramer
IT Infrastructure for Translational Medical Research, University of Augsburg

Prof. Dr. Ulrich Sax
Department of Medical Informatics, University Medical Center Göttingen 

Prof. Dr. Edgar Wingender
Former Head of the Institute of Bioinformatics, University Medical Center Göttingen, Now: Chief Executive Officer geneXplain GmbH Wolfenbüttel 

Dr. Annalen Bleckmann
Clinic for Hematology an Oncology, University Medical Center Göttingen 

PD Dr. Jochen Gaedcke
Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen 

Dr. Alexander Kel
Chief Scientifc Officer at geneXplain GmbH Wolfenbüttel 

For more information, publications, resources see the official website: 

ExITox/ ExlTox2

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.

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 (HRCG

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


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.

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).


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.

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 was 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)

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

KFO 179

Biological Basis of Individual Tumour Response in Patients with Rectal Cancer

General summary and background

The Clinical Research Group 179 (KFO 179) with the topic "Biological Basis of Individual Tumor Response in Patients with Rectal Cancer" represents a collaboration of clinicians and scientists from various fields: surgery, gastroenterology, oncology, molecular oncology, radiation therapy/radio-oncology, pharmacology, pathology, human genetics, nuclear medicine, biochemistry, medical (bio)statistics, medical informatics, and medical ethics. In Europe and the USA, colorectal cancer accounts for over 15% of all cancer cases, with a rising trend. In Germany, colorectal cancer, with over 60,000 new cases per year regardless of gender, ranks as the second most common malignant tumor disease, with over half of the patients succumbing to its consequences. Approximately 30% of these cases occur in the last section of the intestine, the rectum. Until a few years ago, the treatment for this tumor disease consisted of immediate surgery followed by radiation and chemotherapy. However, recent scientific findings have led to a change where treatment now begins with radiation and chemotherapy, followed by surgery. Although this therapy approach (neoadjuvant pre-therapy) offers significant advantages, treating physicians still face the dilemma that patients react very differently to this treatment: Some tumors respond very well to it, while others show little or no change. Furthermore, some patients suffer considerable side effects from this pre-therapy. This is where the work of the Clinical Research Group 179 comes in. The goal of this interdisciplinary research group was to understand why patients with rectal carcinoma respond differently to the standard therapy, thereby advancing the development of a therapy tailored to the individual patient (for better chances of recovery and higher quality of life). This involves both the response of the tumor to the therapy and the occurrence of side effects. The consortium consisted of seven (first funding period) or nine (second funding period) independent subprojects with the participation of international partners. The basis was the genetic/molecular biological as well as clinical analysis of different patient groups.

Subproject SP8: Development of statistical and computational methods, tools, and infrastructures as well as data analysis, data management, and support for clinical researchers

Prof. Dr. Tim Beißbarth (Department of Medical Bioinformatics, UMG)

Prof. Dr. Ulrich Sax (Department of Medical Informatics, UMG)

The first aim of this subproject was to establish a professional IT-Infrastructure according to GCP guidelines and German privacy law. Two databases were implemented: one for clinical data and one for biomaterial data. In the course of the two funding periods, five different projects were implemented in the clinical database (one validation study) with overall 3.375 patients and 100 users entering the data. 52.710 biomaterial samples were stored in the database. Together with the Department of Bioinformatics, the implementation of these databases enabled the researchers to perform different data queries for different data analyzes but also for data quality assurance. Along with the IT-Infrastructure, a data protection concept was written and subsequently approved by the local data protection officer. To combine the different data from the two databases, i.e. for combined data analyses, the query tool i2b2 was tested together with a couple of researchers from other subgroups.

In order to assess the options to reuse health care data in this clinical research setting, the implementation of a research-focused form in the Göttingen clinical workspace (electronic patient record) was piloted, and a customized form to report quality specific events was set-up. Unfortunately, it turned out that the currently used IT System for the clinical workspace did support the form, but did not offer a sufficient reporting tool to actually use the data. A new approach will be followed based on the KFO 179 experience as soon as an improved electronic health record will be in place at the UMG.

The second aim of this subproject was to provide biostatistics and bioinformatics support for the entire KFO 179. Clinical data and data from laboratory research were collected and analyzed with appropriate statistical and bioinformatics methodology. Major aims were to test for biomarkers that can predict outcome of CRT or disease progression. Different types of molecular data were utilized here. Lowdimensional data from immunohistochemistry as well as basic clinical data from the time of biopsy or the time of surgery were used to predict progression-free survival. High-dimensional data from gene expression microarrays or methylation were used to predict outcome after CRT. Further analysis of more targeted experiments in cell lines were used to analyze the basic mechanisms and pathways involved in CRT-resistance. The collaborative efforts led to many discoveries of relevant biomarkers and disease mechanisms in rectal cancer, which resulted in a multitude of peer-reviewed publications and several clinical trials.

Besides the aim to provide support in data analysis for the KFO 179, another aim of this subproject was to establish the required biostatistics and bioinformatics methodology on campus and to test and improve methodology. The methodological research focused on three topics:

1. Analytic methods for microarray data
2. Methods of risk prediction for cancer patients
3. Reconstruction and analysis of signaling pathways

Specifically, aims for the KFO 179 were the following: 1
1. Develop classification methods for building disease signatures from multiple data.
2. Implement reconstruction methods for pathways and regulation mechanisms involved in colorectal cancer.
3. Develop and enhance classification procedures that allow multiple or ordinal responses.

A number of novel methodological developments were achieved in this subproject:
1. A new method to assign multiple testing adjusted confidence intervals on high-dimensional gene expression fold-changes was developed (Jung et al. BMC Bioinformatics 2011).
2. Methods for global tests were implemented and improved (Jung et al. Bioinformatics 2011; Jung et al. Bioinformatics 2014).
3. A new sensitivity preferred strategy to build classifiers was developed (Jung et al. Computer Methods and Programs in Biomedicine 2010).
4. It was demonstrated that for high-dimensional data interim analysis without additional testing correction is valid (Leha et al. BMC Bioinformatics 2011).
5. A new method to train ordinal response classifiers on high-dimensional data was developed and compared with existing strategies (Leha et al. Proc. of the German Conference on Bioinformatics 2013). 6. Several methods to fuse miRNA and mRNA expression data were developed (Artmann et al. PLoS One 2012; Fuchs et al. Computer Methods and Programs in Biomedicine 2013; Gade et al. Bioinformatics 2011).

Funded by the DFG (2007 - 2014)

More information: DFG 

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