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Identification of a Topological Characteristic Responsible for the Biological Robustness of Regulatory Networks
Author(s) -
Yang-Le Wu,
Xiaomeng Zhang,
Jianglei Yu,
Qi Ouyang
Publication year - 2009
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1000442
Subject(s) - biological network , robustness (evolution) , systems biology , computer science , generality , schizosaccharomyces pombe , gene regulatory network , topology (electrical circuits) , network topology , entropy (arrow of time) , boolean network , biological system , saccharomyces cerevisiae , mathematics , computational biology , biology , yeast , gene , physics , genetics , gene expression , combinatorics , psychology , boolean function , algorithm , quantum mechanics , psychotherapist , operating system
Attribution of biological robustness to the specific structural properties of a regulatory network is an important yet unsolved problem in systems biology. It is widely believed that the topological characteristics of a biological control network largely determine its dynamic behavior, yet the actual mechanism is still poorly understood. Here, we define a novel structural feature of biological networks, termed ‘regulation entropy’, to quantitatively assess the influence of network topology on the robustness of the systems. Using the cell-cycle control networks of the budding yeast ( Saccharomyces cerevisiae ) and the fission yeast ( Schizosaccharomyces pombe ) as examples, we first demonstrate the correlation of this quantity with the dynamic stability of biological control networks, and then we establish a significant association between this quantity and the structural stability of the networks. And we further substantiate the generality of this approach with a broad spectrum of biological and random networks. We conclude that the regulation entropy is an effective order parameter in evaluating the robustness of biological control networks. Our work suggests a novel connection between the topological feature and the dynamic property of biological regulatory networks.

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