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Chemical Basis of Metabolic Network Organization

Overview of attention for article published in PLoS Computational Biology, October 2011
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Title
Chemical Basis of Metabolic Network Organization
Published in
PLoS Computational Biology, October 2011
DOI 10.1371/journal.pcbi.1002214
Pubmed ID
Authors

Qiang Zhu, Tao Qin, Ying-Ying Jiang, Cong Ji, De-Xin Kong, Bin-Guang Ma, Hong-Yu Zhang

Abstract

Although the metabolic networks of the three domains of life consist of different constituents and metabolic pathways, they exhibit the same scale-free organization. This phenomenon has been hypothetically explained by preferential attachment principle that the new-recruited metabolites attach preferentially to those that are already well connected. However, since metabolites are usually small molecules and metabolic processes are basically chemical reactions, we speculate that the metabolic network organization may have a chemical basis. In this paper, chemoinformatic analyses on metabolic networks of Kyoto Encyclopedia of Genes and Genomes (KEGG), Escherichia coli and Saccharomyces cerevisiae were performed. It was found that there exist qualitative and quantitative correlations between network topology and chemical properties of metabolites. The metabolites with larger degrees of connectivity (hubs) are of relatively stronger polarity. This suggests that metabolic networks are chemically organized to a certain extent, which was further elucidated in terms of high concentrations required by metabolic hubs to drive a variety of reactions. This finding not only provides a chemical explanation to the preferential attachment principle for metabolic network expansion, but also has important implications for metabolic network design and metabolite concentration prediction.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 9%
Portugal 1 1%
Singapore 1 1%
Brazil 1 1%
Japan 1 1%
Spain 1 1%
Unknown 64 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 37%
Student > Ph. D. Student 19 25%
Student > Master 6 8%
Professor 5 7%
Professor > Associate Professor 4 5%
Other 9 12%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 49%
Biochemistry, Genetics and Molecular Biology 10 13%
Computer Science 7 9%
Engineering 4 5%
Mathematics 3 4%
Other 6 8%
Unknown 9 12%