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  Inferring dynamic properties of biochemical reaction networks from structural knowledge

Klipp, E., Liebermeister, W., & Wierling, C. (2004). Inferring dynamic properties of biochemical reaction networks from structural knowledge. Genome Informatics Series: Proceedings of the Workshop on Genome Informatics, 15(1), 125-137.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-893A-7 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-893B-5
Genre: Journal Article
Alternative Title : Genome Inform Ser Workshop Genome Inform

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 Creators:
Klipp, Edda1, Author              
Liebermeister, Wolfram, Author
Wierling, Christoph2, Author              
Affiliations:
1Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433554              
2Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479656              

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 Abstract: Functional properties of biochemical networks depend on both the network structure and the kinetic parameters. Extensive data on metabolic network topologies have been collected in databases, but much less information is available about the kinetic constants or metabolite concentrations. Depending on the values of these parameters, metabolic fluxes and control coefficients may vary within a wide range. Nevertheless, some of the parameters may have little influence on the observables of interest. We address the question whether, despite uncertainty about kinetic parameters, probabilistic statements can be made about dynamic network features. To this end, we perform a variability analysis of the parameters: assuming that the parameters follow statistical distributions, we compute the resulting distributions of the network properties like metabolic fluxes, concentrations, or control coefficients by Monte Carlo simulation. In this manner, we study systematically the possible distributions arising from typical topologies of biochemical networks such as linear chains, branched networks, and signaling and gene expression cascades. This analysis reveals how much information about dynamic behavior can be drawn from structural knowledge.

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Language(s): eng - English
 Dates: 2004
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: eDoc: 230607
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Title: Genome Informatics Series: Proceedings of the Workshop on Genome Informatics
  Alternative Title : Genome Inform Ser Workshop Genome Inform
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 15 (1) Sequence Number: - Start / End Page: 125 - 137 Identifier: ISSN: 0919-9454