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Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons86157

Conradi,  C.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86167

Flockerzi,  D.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86212

Raisch,  J.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
TU Berlin;

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Zitation

Conradi, C., Flockerzi, D., Raisch, J., & Stelling, J. (2007). Subnetwork analysis reveals dynamic features of complex (bio)chemical networks. PNAS, 104(49), 19175-19180. doi:10.1073/pnas.0705731104.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-96EB-8
Zusammenfassung
In analyzing and mathematical modeling of complex (bio)chemical reaction networks, formal methods that connect network structure and dynamic behavior are needed because often, quantitative knowledge of the networks is very limited. This applies to many important processes in cell biology. Chemical reaction network theory allows for the classification of the potential network behavior—for instance, with respect to the existence of multiple steady states—but is computationally limited to small systems. Here, we show that by analyzing subnetworks termed elementary flux modes, the applicability of the theory can be extended to more complex networks. For an example network inspired by cell cycle control in budding yeast, the approach allows for model discrimination, identification of key mechanisms for multistationarity, and robustness analysis. The presented methods will be helpful in modeling and analyzing other complex reaction networks. Copyright © 2013 National Academy of Sciences [access 2013 June 14th]