Dynamical and topological robustness of the mammalian cell cycle network: a reverse engineering approach.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: Elsevier Science Ireland Country of Publication: Ireland NLM ID: 0430773 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8324 (Electronic) Linking ISSN: 03032647 NLM ISO Abbreviation: Biosystems Subsets: MEDLINE
    • Publication Information:
      Publication: Limerick : Elsevier Science Ireland
      Original Publication: Amsterdam, North-Holland Pub. Co.
    • Subject Terms:
      Models, Biological*; Cell Cycle/*physiology ; Gene Regulatory Networks/*physiology ; Mammals/*physiology; Algorithms ; Animals ; Computational Biology/methods ; Computer Simulation ; Systems Biology/methods
    • Abstract:
      A common gene regulatory network model is the threshold Boolean network, used for example to model the Arabidopsis thaliana floral morphogenesis network or the fission yeast cell cycle network. In this paper, we analyze a logical model of the mammalian cell cycle network and its threshold Boolean network equivalent. Firstly, the robustness of the network was explored with respect to update perturbations, in particular, what happened to the attractors for all the deterministic updating schemes. Results on the number of different limit cycles, limit cycle lengths, basin of attraction size, for all the deterministic updating schemes were obtained through mathematical and computational tools. Secondly, we analyzed the topology robustness of the network, by reconstructing synthetic networks that contained exactly the same attractors as the original model by means of a swarm intelligence approach. Our results indicate that networks may not be very robust given the great variety of limit cycles that a network can obtain depending on the updating scheme. In addition, we identified an omnipresent network with interactions that match with the original model as well as the discovery of new interactions. The techniques presented in this paper are general, and can be used to analyze other logical or threshold Boolean network models of gene regulatory networks.
      (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)
    • Contributed Indexing:
      Keywords: Bees algorithm; Boolean networks; Gene regulatory networks; Threshold networks; Topology robustness; Update robustness
    • Publication Date:
      Date Created: 20131112 Date Completed: 20140811 Latest Revision: 20131220
    • Publication Date:
      20240829
    • Accession Number:
      10.1016/j.biosystems.2013.10.007
    • Accession Number:
      24212100