TY - GEN
T1 - Reconstruction and update robustness of the mammalian cell cycle network
AU - Ruz, Gonzalo A.
AU - Goles, Eric
PY - 2012
Y1 - 2012
N2 - Given the input-output data of the mammalian cell cycle network under a parallel updating scheme, an attempt to construct a threshold Boolean network with the same dynamics is presented. To accomplish this, mutual information is used to find the network structure, then a swarm intelligence optimization technique called the bees algorithm is used to find the weights and thresholds for the network. It is shown that out of the ten regulatory elements (nodes) of the network, only nine can be modeled as a single threshold function, thus, the resulting network is almost a threshold Boolean network with the exception of the CycA protein which remains with its logical rules instead. The robustness of the network is explored with respect to update perturbations, in particular, what happens to the limit cycle attractors when changing from parallel to a sequential updating scheme. Results shows that the network is not robust since different limit cycles of different lengths appear.
AB - Given the input-output data of the mammalian cell cycle network under a parallel updating scheme, an attempt to construct a threshold Boolean network with the same dynamics is presented. To accomplish this, mutual information is used to find the network structure, then a swarm intelligence optimization technique called the bees algorithm is used to find the weights and thresholds for the network. It is shown that out of the ten regulatory elements (nodes) of the network, only nine can be modeled as a single threshold function, thus, the resulting network is almost a threshold Boolean network with the exception of the CycA protein which remains with its logical rules instead. The robustness of the network is explored with respect to update perturbations, in particular, what happens to the limit cycle attractors when changing from parallel to a sequential updating scheme. Results shows that the network is not robust since different limit cycles of different lengths appear.
KW - Attractors
KW - Boolean networks
KW - Gene regulatory networks
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=84864068053&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2012.6217257
DO - 10.1109/CIBCB.2012.6217257
M3 - Conference contribution
AN - SCOPUS:84864068053
SN - 9781467311892
T3 - 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
SP - 397
EP - 403
BT - 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
T2 - 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
Y2 - 9 May 2012 through 12 May 2012
ER -