TY - GEN
T1 - Optimal perturbation control of gene regulatory networks
AU - Bouaynaya, Nidhal
AU - Shterenberg, Roman
AU - Schonfeld, Dan
PY - 2010/12/1
Y1 - 2010/12/1
N2 - We formulate the control problem in gene regulatory networks as an inverse perturbation problem, which provides the feasible set of perturbations that force the network to transition from an undesirable steady-state distribution to a desirable one. We derive a general characterization of such perturbations in an appropriate basis representation. We subsequently consider the optimal perturbation, which minimizes the overall energy of change between the original and controlled (perturbed) networks. The "energy" of change is characterized by the Euclidean-norm of the perturbation matrix. We cast the optimal control problem as a semi-definite programming (SDP) problem, thus providing a globally optimal solution which can be efficiently computed using standard SDP solvers. We apply the proposed control to the Human melanoma gene regulatory network and show that the steady-state probability mass is shifted from the undesirable high metastatic states to the chosen steady-state probability mass.
AB - We formulate the control problem in gene regulatory networks as an inverse perturbation problem, which provides the feasible set of perturbations that force the network to transition from an undesirable steady-state distribution to a desirable one. We derive a general characterization of such perturbations in an appropriate basis representation. We subsequently consider the optimal perturbation, which minimizes the overall energy of change between the original and controlled (perturbed) networks. The "energy" of change is characterized by the Euclidean-norm of the perturbation matrix. We cast the optimal control problem as a semi-definite programming (SDP) problem, thus providing a globally optimal solution which can be efficiently computed using standard SDP solvers. We apply the proposed control to the Human melanoma gene regulatory network and show that the steady-state probability mass is shifted from the undesirable high metastatic states to the chosen steady-state probability mass.
UR - http://www.scopus.com/inward/record.url?scp=79952797250&partnerID=8YFLogxK
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U2 - 10.1109/GENSIPS.2010.5719672
DO - 10.1109/GENSIPS.2010.5719672
M3 - Conference contribution
AN - SCOPUS:79952797250
SN - 9781612847924
T3 - 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
BT - 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
T2 - 2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
Y2 - 10 November 2010 through 12 November 2010
ER -