Intervention and Control of Large-Scale Gene Regulatory Networks
Thursday, November 17, 2011 - 3:15pm - 4:15pm
We formulate the optimal intervention problem in genetic regulatory networks as a minimal-perturbation of the network in order to force it to converge to a desired steady-state distribution of gene regulation. We cast optimal intervention in gene regulation as a convex optimization problem, thus providing a globally optimal solution which can be efficiently computed using standard techniques for convex optimization. The criteria adopted for optimality is chosen to minimize potential adverse effects as a consequence of the intervention strategy. We consider a perturbation that minimizes (i) the overall energy of change between the original and controlled networks and (ii) the time needed to reach the desired steady-state of gene regulation. Moreover, we show that there is an inherent tradeoff between minimizing the energy of the perturbation and the convergence rate to the desired distribution. We further show that the optimal inverse perturbation control is robust to estimation errors in the original network. The proposed control is applied to the Human melanoma gene regulatory network.