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  Mass-balanced randomization of metabolic networks

Basler, G., Ebenhoeh, O., Selbig, J., & Nikoloski, Z. (2011). Mass-balanced randomization of metabolic networks. Bioinformatics, 27(10), 1397-1403. doi:10.1093/bioinformatics/btr145.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-2271-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-BC31-A
Genre: Journal Article

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 Creators:
Basler, G.1, Author              
Ebenhoeh, O.2, Author              
Selbig, J.3, Author              
Nikoloski, Z.1, Author              
Affiliations:
1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              
2Mathematical Modelling and Systems Biology, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753341              
3BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753315              

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Free keywords: simple building-blocks complex networks escherichia-coli protein networks biology motifs reconstruction organization annotation centrality
 Abstract: Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem. Results: We first review the shortcomings of the existing generic sampling scheme-switch randomization-and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties.

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Language(s): eng - English
 Dates: 2011-03-232011
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
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 Identifiers: ISI: ISI:000290334500009
DOI: 10.1093/bioinformatics/btr145
ISSN: 1367-4803
URI: ://000290334500009http://bioinformatics.oxfordjournals.org/content/27/10/1397.full.pdf
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 27 (10) Sequence Number: - Start / End Page: 1397 - 1403 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991