LipidomicNet

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.

The complete name of the project: Lipid droplets as dynamic organelles of fat deposition and release: Translational research towards human disease

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

Partners:

University Regensburg (Prof. G. Schmitz, coordinator)

24 further partners

Project page: www.lipidomicnet.org

Achievements:

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.

Publications:

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). link

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

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).
link

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). link