TY - GEN

T1 - Optimal perturbation control of gene regulatory networks

AU - Bouaynaya, Nidhal

AU - Shterenberg, Roman

AU - Schonfeld, Dan

PY - 2010

Y1 - 2010

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

UR - http://www.scopus.com/inward/citedby.url?scp=79952797250&partnerID=8YFLogxK

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 -