  In network control theory, driving all the nodes in the Feedback Vertex Set
(FVS) forces the network into one of its attractors (long-term dynamic
behaviors). The FVS is often composed of more nodes than can be realistically
manipulated in a system; for example, only up to three nodes can be controlled
in intracellular networks, while their FVS may contain more than 10 nodes.
Thus, we developed an approach to rank subsets of the FVS on Boolean models of
intracellular networks using topological, dynamics-independent measures. We
investigated the use of seven topological prediction measures sorted into three
categories -- centrality measures, propagation measures, and cycle-based
measures. Using each measure every subset was ranked and then evaluated against
two dynamics-based metrics that measure the ability of interventions to drive
the system towards or away from its attractors: To Control and Away Control.
After examining an array of biological networks, we found that the FVS subsets
that ranked in the top according to the propagation metrics can most
effectively control the network. This result was independently corroborated on
a second array of different Boolean models of biological networks.
Consequently, overriding the entire FVS is not required to drive a biological
network to one of its attractors, and this method provides a way to reliably
identify effective FVS subsets without knowledge of the network's dynamics.
