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Genedata and FRISYS Turn Over new Leaf in Systems Biology Collaboration
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Genedata and FRISYS Turn Over new Leaf in Systems Biology Collaboration

Genedata and FRISYS Turn Over new Leaf in Systems Biology Collaboration
News

Genedata and FRISYS Turn Over new Leaf in Systems Biology Collaboration

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The long standing collaboration between bioinformatics provider Genedata and the University of Freiburg, supported by the initiative of excellence of the German Federal Government and States, is being taken to the next level with the latest development at the Freiburg Initiative for Systems Biology (FRISYS).

Within the FRISYS framework, Genedata`s Phylosopher research data management and analysis system has been applied to integrate public, experimental and in-licensed data, enabling systems biology for various model organisms like the moss Physcomitrella patens.

As systems biology projects rely on the completely reconstructed part list of molecular entities encoded in the genome, deciphering the genome and its capabilities is a prerequisite to reconstruct and model the living cell towards systems biology.

Earlier in 2008, after 8 years of work, the International Moss Sequencing Consortium had reported the draft genome sequence of Physcomitrella patens, a moss with impressive survival qualities and important source to improve cultivated plants in biotech applications.

“A milestone for systems biology and moss research,” said PD Dr. Stefan Rensing, Head of the Core Facility Data Management at FRISYS and first author of the genome sequence publication. “This genome is of high scientific relevance: Physcomitrella helps to understand the transition of photosynthetic organisms from the aquatic environment to land. The adaptation included e.g. enhanced osmoregulation and -protection, desiccation and freezing tolerance, heat resistance, and the development of protective ‘sunscreens’.”

Genedata Phylosopher’s GeneIndex and its pathway-centric analysis environment integrates public, in-licensed and proprietary experimental omics data and thus streamlines the iterative process of systems biology.

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