Applied Bioinformatics Group

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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.

Data integration  

Data Integration

For data integration and analysis we develop UniPAX, a framework for graph-based analyses of measured omics data from multiple omics. UniPAX offers an integrated set of biological networks as the topological base for data analysis. Measured data can be easily imported into the system and mapped onto affected networks. This allows the application of graph algorithms to the weighted networks, e.g. to find lightest paths. UniPAX is accessable via a RESTful webserver interface that offers stepwise consecutive analysis options with intermediate result inspection. Unipax permits easy software integration via its provided c++ library or the REST interface. UniPAX will be supported by the Biological Network Analyzer (BiNA) application (from version 3 on) which offers a graphical user interface with many data analysis options. The UniPAX project is funded by SPP1335 (Scalable Visual Analytics). 

MetaRoute is a tool which finds relevant biotransformation routes between selected metabolites in metabolic networks. In the future it will be available via the UniPAX framework, too.


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. Currentyl, we offer three highly accurate prediction methods as web service. YLoc, the most recent predictor offers interpretable predictions which include textual explanations why a prediction was made. In contrast, 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".


Visualization is an essential tool to make sense of the vast amounts of data that are common in biology and to obtain an understanding of the underlying connections within a system. This is of particular importance when studying complex metabolic, regulatory, or signaling networks. BiNA, a platform-independent viewer, solves these problems by providing visualization and navigation of the data. Increasingly, this challenge is become more complex through the availability of multiple omics datasets for the same system. In addition to new visualization challenges the problem of integrating the different datasets has to be solved.


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".



People working in this area:

Fabian Aicheler, Andreas FriedrichErhan Kenar, Peter Niermann, Marc Rurik, Mathias Walzer, Sebastian Winkler

Selected publications: