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  Systematic Analysis of Stability Patterns in Plant Primary Metabolism

Girbig, D., Grimbs, S., & Selbig, J. (2012). Systematic Analysis of Stability Patterns in Plant Primary Metabolism. PLoS One, 7(4), e34686. doi:10.1371/journal.pone.0034686.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-1FBE-A Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-1FBF-8
Genre: Journal Article

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 Creators:
Girbig, D.1, 2, Author              
Grimbs, S.1, Author              
Selbig, J.1, Author              
Affiliations:
1BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753315              
2BioinformaticsCIG, Infrastructure Groups and Service Units, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753303              

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Free keywords: photosynthesis in-silico calvin-benson cycle numerical-simulation carbon networks starch sedoheptulose-1,7-bisphosphatase complex models growth
 Abstract: Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM) is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models.

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Language(s): eng - English
 Dates: 2012
 Publication Status: Published in print
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 Identifiers: ISI: ISI:000305341600061
DOI: 10.1371/journal.pone.0034686
ISSN: 1932-6203
URI: ://000305341600061 http://www.plosone.org/article/fetchObjectAttachment.action?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0034686&representation=PDF
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Title: PLoS One
Source Genre: Journal
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Pages: - Volume / Issue: 7 (4) Sequence Number: - Start / End Page: e34686 Identifier: -