Literature DB >> 16046441

Scale-freeness and biological networks.

Masanori Arita1.   

Abstract

The notion of scale-freeness and its prevalence in both natural and artificial networks have recently attracted much attention. The concept of scale-freeness is enthusiastically applied to almost any conceivable network, usually with affirmative conclusions. Well-known scale-free examples include the internet, electric lines among power plants, the co-starring of movie actors, the co-authorship of researchers, food webs, and neural, protein-protein interactional, genetic, and metabolic networks. The purpose of this review is to clarify the relationship between scale-freeness and power-law distribution, and to assess critically the previous related works, especially on biological networks. In addition, I will focus on the close relationship between power-law distribution and lognormal distribution to show that power-law distribution is not a special characteristic of natural selection.

Mesh:

Year:  2005        PMID: 16046441     DOI: 10.1093/jb/mvi094

Source DB:  PubMed          Journal:  J Biochem        ISSN: 0021-924X            Impact factor:   3.387


  17 in total

Review 1.  Protein interaction networks in plants.

Authors:  Joachim F Uhrig
Journal:  Planta       Date:  2006-03-31       Impact factor: 4.116

2.  Chromatin code, local non-equilibrium dynamics, and the emergence of transcription regulatory programs.

Authors:  A Benecke
Journal:  Eur Phys J E Soft Matter       Date:  2006-03-07       Impact factor: 1.890

3.  The strength of chemical linkage as a criterion for pruning metabolic graphs.

Authors:  Wanding Zhou; Luay Nakhleh
Journal:  Bioinformatics       Date:  2011-05-05       Impact factor: 6.937

4.  Scale-freeness of dominant and piecemeal perceptions during binocular rivalry.

Authors:  Fatemeh Bakouie; Morteza Pishnamazi; Roxana Zeraati; Shahriar Gharibzadeh
Journal:  Cogn Neurodyn       Date:  2017-03-25       Impact factor: 5.082

5.  Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.

Authors:  Ziynet Nesibe Kesimoglu; Serdar Bozdag
Journal:  PLoS One       Date:  2021-05-13       Impact factor: 3.240

6.  Modern Approaches for Transcriptome Analyses in Plants.

Authors:  Diego Mauricio Riaño-Pachón; Hector Fabio Espitia-Navarro; John Jaime Riascos; Gabriel Rodrigues Alves Margarido
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

7.  Hierarchical modularity of nested bow-ties in metabolic networks.

Authors:  Jing Zhao; Hong Yu; Jian-Hua Luo; Zhi-Wei Cao; Yi-Xue Li
Journal:  BMC Bioinformatics       Date:  2006-08-18       Impact factor: 3.169

8.  Current understanding of the formation and adaptation of metabolic systems based on network theory.

Authors:  Kazuhiro Takemoto
Journal:  Metabolites       Date:  2012-07-12

9.  Stability of metabolic correlations under changing environmental conditions in Escherichia coli--a systems approach.

Authors:  Jedrzej Szymanski; Szymon Jozefczuk; Zoran Nikoloski; Joachim Selbig; Victoria Nikiforova; Gareth Catchpole; Lothar Willmitzer
Journal:  PLoS One       Date:  2009-10-15       Impact factor: 3.240

10.  Relationship between gene regulation network structure and prediction accuracy in high dimensional regression.

Authors:  Yuichi Okinaga; Daisuke Kyogoku; Satoshi Kondo; Atsushi J Nagano; Kei Hirose
Journal:  Sci Rep       Date:  2021-06-01       Impact factor: 4.379

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