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The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons97175

Grimbs,  S.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97409

Selbig,  J.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Grimbs, S., Selbig, J., Bulik, S., Holzhuetter, H. G., & Steuer, R. (2007). The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks. Molecular Systems Biology, 3, 146. doi:10.1038/msb4100186.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-2922-6
Abstract
The dynamic behavior of metabolic networks is governed by numerous regulatory mechanisms, such as reversible phosphorylation, binding of allosteric effectors or temporal gene expression, by which the activity of the participating enzymes can be adjusted to the functional requirements of the cell. For most of the cellular enzymes, such regulatory mechanisms are at best qualitatively known, whereas detailed enzyme-kinetic models are lacking. To explore the possible dynamic behavior of metabolic networks in cases of lacking or incomplete enzyme-kinetic information, we present a computational approach based on structural kinetic modeling. We derive statistical measures for the relative impact of enzyme-kinetic parameters on dynamic properties (such as local stability) and apply our approach to the metabolism of human erythrocytes. Our findings show that allosteric enzyme regulation significantly enhances the stability of the network and extends its potential dynamic behavior. Moreover, our approach allows to differentiate quantitatively between metabolic states related to senescence and metabolic collapse of the human erythrocyte. We think that the proposed method represents an important intermediate step on the long way from topological network analysis to detailed kinetic modeling of complex metabolic networks.