Property-Driven Statistics of Biological Networks

  • Pierre-Yves Bourguignon
  • Vincent Danos
  • François Képes
  • Serge Smidtas
  • Vincent Schächter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4220)


An analysis of heterogeneous biological networks based on randomizations that preserve the structure of component subgraphs is introduced and applied to the yeast protein-protein interaction and transcriptional regulation network. Shuffling this network, under the constraint that the transcriptional and protein-protein interaction subnetworks are preserved reveals statistically significant properties with potential biological relevance. Within the population of networks which embed the same two original component networks, the real one exhibits simultaneously higher bi-connectivity (the number of pairs of nodes which are connected using both subnetworks), and higher distances. Moreover, using restricted forms of shuffling that preserve the interface between component networks, we show that these two properties are independent: restricted shuffles tend to be more compact, yet do not lose any bi-connectivity.

Finally, we propose an interpretation of the above properties in terms of the signalling capabilities of the underlying network.


Random Graph Degree Distribution Biological Network Protein Interaction Network Network Motif 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pierre-Yves Bourguignon
    • 1
  • Vincent Danos
    • 2
  • François Képes
    • 3
  • Serge Smidtas
    • 1
  • Vincent Schächter
    • 1
  1. 1.Genoscope 
  2. 2.CNRS & Université Paris VII 
  3. 3.CNRS 

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