Literature DB >> 25215776

Metabolic networks are almost nonfractal: a comprehensive evaluation.

Kazuhiro Takemoto1.   

Abstract

Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.

Mesh:

Year:  2014        PMID: 25215776     DOI: 10.1103/PhysRevE.90.022802

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  2 in total

1.  Effects of random rewiring on the degree correlation of scale-free networks.

Authors:  Jing Qu; Sheng-Jun Wang; Marko Jusup; Zhen Wang
Journal:  Sci Rep       Date:  2015-10-20       Impact factor: 4.379

Review 2.  Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks.

Authors:  Sandrien Desmet; Marlies Brouckaert; Wout Boerjan; Kris Morreel
Journal:  Comput Struct Biotechnol J       Date:  2020-12-03       Impact factor: 7.271

  2 in total

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