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Inferring dynamic properties of biochemical reaction networks from structural knowledge

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50384

Klipp,  Edda
Independent Junior Research Groups (OWL), 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;

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Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-893A-7
Zusammenfassung
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.