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Paper: The relationship between modularity and robustness in signalling networks

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  • Robert Karl Stonjek
    The relationship between modularity and robustness in signalling networks Tien-Dzung Tran1,2 and Yung-Keun Kwon1 1Complex Systems Computing Lab, School of
    Message 1 of 1 , Sep 19, 2013
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      The relationship between modularity and robustness in signalling networks

      Tien-Dzung Tran1,2 and Yung-Keun Kwon1
       
      1Complex Systems Computing Lab, School of Computer Engineering and Information Technology, University of Ulsan, 93, Daehak-ro, Nam-gu, Ulsan 680-749, South Korea
      2Department of Computer Science, Faculty of Information Technology, Hanoi University of Industry, Hanoi, Vietnam
      e-mail: kwonyk@...

      Abstract

      Many biological networks tend to have a high modularity structural property and the dynamic characteristic of high robustness against perturbations. However, the relationship between modularity and robustness is not well understood. To investigate this relationship, we examined real signalling networks and conducted simulations using a random Boolean network model. As a result, we first observed that the network robustness is negatively correlated with the network modularity. In particular, this negative correlation becomes more apparent as the network density becomes sparser. Even more interesting is that, the negative relationship between the network robustness and the network modularity occurs mainly because nodes in the same module with the perturbed node tend to be more sensitive to the perturbation than those in other modules. This result implies that dynamically similar nodes tend to be located in the same module of a network. To support this, we show that a pair of genes associated with the same disease or a pair of functionally similar genes is likely to belong to the same module in a human signalling network.

      Source: The Royal Society
      http://rsif.royalsocietypublishing.org/content/10/88/20130771.abstract.html?etoc

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      Robert Karl Stonjek

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