Computational Systems Biology

Our research in computational system biology focuses on the analysis of complex OMICS datasets in the context of networks, on the modeling and analysis of regulatory networks (in the context of cancer) and metabolic networks (in metabolic engineering). We primarily use approaches from graph theory for data integration and statistical learning.

 

Protein Subcellular Localization Prediction

Automatic annotation of subcellular localization of proteins is an important step torwards elucidating its interaction partners, function, and potential role(s) in the cellular machinery. We develop computational methods to predict the subcellular localization of eukaryotic proteins from the amino acid sequence. Our two most recent predictors, MultiLoc2 and SherLoc2, offer highly accurate predictions for whole genome annotations. Read more...


This work is supported LGFG: Promotionsverbund "Pflanzliche Sensorhistidinkinasen: Struktur, intrazelluläre Dynamik und Funktion".
 

Biological Networks

Modelling and analysing regulatory and metabolic networks is crucial to understand biological systems as well as in metabolic engineering.
In the context of the BN++ project, we offer several tools, libraries, and databases for analysing, modelling, and visualization of complex biochemical and regulatory networks. The Biochemical network database, BNDB, is a comprehensive and simple platform to access a variety of external data sources. BiNa, a platform-independent viewer provides a visualization and navigation of the data. The BN++ biochemical network library simplifies the implementation of tools for analyzing and visualizing complex networks and processes.
MetaRoute is a novel tool which finds relevant biotransformation routes from one metabolite to another.
 

Regulatory Mechanisms in Cancer

We are currently working on integrative approaches to unravel mechanisms involved in cancer development and immune escape of tumors. Disturbances in regulatory pathways are thought to be mainly responsible for the development of cancer. The complex regulatory pathways can be affected by different events, such as genetic mutations (e.g. SNPs, chromosomal aberrations, gene fusions) or post-transcriptional and post-translational modifications. Many decades of cell biology research opened numerous insights into affected mechanisms, however the broad understanding still remains elusive. Modern OMICS technologies enable high-throughput quantitative profiling of many genes, transcripts and proteins simultaneously. Systemic integration of these datasets is a highly promising strategy towards the identification of modulated pathways, which potentially can lead to new therapeutic agents for cancer treatment.

Our research is supported by the BMBF/ Quantpro and the SFB685 grant "Immuntherapie: Von den molekularen Grundlagen zur klinischen Anwendung".

 
 

Signaling in Human Platelets

The SARA consortium addresses the characterisation of ADP receptor signaling in human platelets by combining molecular biology, medicine, quantitative proteomics and bioinformatics. Changes in phosphorylation upon specific stimulation of platelets will be quantified by mass spectrometry and SH2-domain profiling. The generated data will be interpreted by bioinformatics to allow for the accurate modeling of the involved signaling pathways. The resulting model will vastly extend our understanding of platelet activation and additionally drug action thereon.

 

People working in this area:

Torsten Blum, Sebastian Briesemeister, Magdalena Feldhahn, Sven Nahnsen, Peter Niermann, Lars Nilse

 

Selected publications: