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 -