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GeNGe: systematic generation of gene regulatory networks. Bioinformatics.

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50185

Hache,  Hendrik
Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50637

Wierling,  Christoph
Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50409

Lehrach,  Hans
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50202

Herwig,  Ralf
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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1205.pdf
(beliebiger Volltext), 256KB

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Zitation

Hache, H., Wierling, C., Lehrach, H., & Herwig, R. (2009). GeNGe: systematic generation of gene regulatory networks. Bioinformatics. Bioinformatics, 25(9), 1205-1207. doi:0.1093/bioinformatics/btp115.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7E05-0
Zusammenfassung
The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments