Stabilizing Control of Complex Biological Networks Based on Attractor-Specific Network Reduction

Abstract

Global stabilization of complex biological networks means controlling the network to converge to a desired stable state, or an attractor, irrespective of initial states by perturbing a set of targeted nodes. Global stabilization is of paramount importance in systems biology since attractors represent key phenotypes of biological systems. Most of the previous attempts resort to either near-brute force or analytical searching with huge computational burden. In this article, we propose a novel control scheme that can achieve global stabilization of complex biological networks by alleviating such issues. For this purpose, a Boolean network model of complex biological networks is considered. We first reduce the Boolean network using a simple coordinate transformation with respect to the desired attractor without loss of the information on connectivity needed for global stabilization. We then identify control inputs by searching for a minimum node set whose perturbation makes the reduced Boolean network acyclic. The proposed control scheme is remarkable since our network reduction procedure needs no structural conditions, and global stabilization is guaranteed with modest computational complexity and scalability. To evaluate its applicability, the proposed control scheme is applied to random Boolean networks as well as to a regulation influence network describing the metastatic process of cells, and the results are analyzed in comparison with other approaches.