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Bioinformatics Advance Access originally published online on February 19, 2008
Bioinformatics 2008 24(7):987-994; doi:10.1093/bioinformatics/btn060
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics

Yung-Keun Kwon 1,2 and Kwang-Hyun Cho 1,*

1Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea and 2School of Computer Science and Information Technology, University of Ulsan, San 29, Mugeo 2-dong, Nam-gu, Ulsan 680-749, Republic of Korea

*To whom correspondence should be addressed.


  Abstract

Motivation: It has been widely reported that biological networks are robust against perturbations such as mutations. On the contrary, it has also been known that biological networks are often fragile against unexpected mutations. There is a growing interest in these intriguing observations and the underlying design principle that causes such robust but fragile characteristics of biological networks. For relatively small networks, a feedback loop has been considered as an important motif for realizing the robustness. It is still, however, not clear how a number of coupled feedback loops actually affect the robustness of large complex biological networks. In particular, the relationship between fragility and feedback loops has not yet been investigated till now.

Results: Through extensive computational experiments, we found that networks with a larger number of positive feedback loops and a smaller number of negative feedback loops are likely to be more robust against perturbations. Moreover, we found that the nodes of a robust network subject to perturbations are mostly involved with a smaller number of feedback loops compared with the other nodes not usually subject to perturbations. This topological characteristic eventually makes the robust network fragile against unexpected mutations at the nodes not previously exposed to perturbations.

Contact: ckh{at}kaist.ac.kr

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Thomas Lengauer

Received on October 10, 2007; revised on February 11, 2008; accepted on February 11, 2008

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