Sparse biologically-constrained optimal perturbation of gene regulatory networks

This paper derives a sparse optimal perturbation of gene regulatory networks by determining the optimal perturbation of the minimal number of individual genes that force the network to settle into desired equilibrium states. Previous efforts have led to intervention in gene regulatory networks by deriving the optimal perturbation of the state probability transition matrix. Current technology in molecular biology, however, is limited to perturbation of the state of individual genes, not the state probability transition matrix. Our computer simulation experiments on the Human melanoma gene regulatory network demonstrate the superiority of the proposed approach to gene regulation in comparison to the previous methods based on the marginal of the optimal perturbation of the probability transition matrix of the network.