Structure-based approach to identifying small sets of driver nodes in biological and biologically-inspired networks

In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) by node-state override forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be practically manipulated in a system. We developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the predictive power of three types of topological measures—centrality measures, propagation measures, and cycle-based measures. Every subset was evaluated on three dynamics-based measures: To Control, Away Control, and the Logical Domain of Influence. After examining an array of biological networks and ensembles of random Boolean networks that resemble biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can effectively control the network, indicating a structural underpinning to dynamically important nodes. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and our method provides a way to reliably identify effective FVS subsets without the knowledge of the network dynamics.