Dynamical and topological robustness of the mammalian cell cycle network: A reverse engineering approach
Ruz
G
A
author
Goles
E
author
Montalva
M
author
Fogel
G
B
author
2014
English
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. (C) 2013 Elsevier Ireland Ltd. All rights reserved.
Gene regulatory networks
Boolean networks
Threshold networks
Update robustness
Topology robustness
Bees algorithm
WOS:000330500100004
exported from refbase (http://ficpubs.uai.cl/show.php?record=350), last updated on Thu, 27 Feb 2014 05:40:06 +0000
text
http://ficpubs.uai.cl/files/316_Ruz_etal2013.pdf
10.1016/j.biosystems.2013.10.007
Ruz_etal2014
Biosystems
Biosystems
2014
Elsevier Sci Ltd
continuing
periodical
academic journal
115
23
32
0303-2647