Journal Article

Hub genes with positive feedbacks function as master switches in developmental gene regulatory networks

Chang H. Seo, Jeong-Rae Kim, Man-Sun Kim and Kwang-Hyun Cho

in Bioinformatics

Volume 25, issue 15, pages 1898-1904
Published in print August 2009 | ISSN: 1367-4803
Published online May 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp316
Hub genes with positive feedbacks function as master switches in developmental gene regulatory networks

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Motivation: Spatio-temporal regulation of gene expression is an indispensable characteristic in the development processes of all animals. ‘Master switches’, a central set of regulatory genes whose states (on/off or activated/deactivated) determine specific developmental fate or cell-fate specification, play a pivotal role for whole developmental processes. In this study on genome-wide integrative network analysis the underlying design principles of developmental gene regulatory networks are examined.

Results: We have found an intriguing design principle of developmental networks: hub nodes, genes with high connectivity, equipped with positive feedback loops are prone to function as master switches. This raises the important question of why the positive feedback loops are frequently found in these contexts. The master switches with positive feedback make the developmental signals more decisive and robust such that the overall developmental processes become more stable. This finding provides a new evolutionary insight: developmental networks might have been gradually evolved such that the master switches generate digital-like bistable signals by adopting neighboring positive feedback loops. We therefore propose that the combined presence of positive feedback loops and hub genes in regulatory networks can be used to predict plausible master switches.

Contact: ckh@kaist.ac.kr

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5757 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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