Motivation: A grand challenge in the modeling of biological systems is the identification of key variables which can act as targets for intervention. Good intervention targets are the "key players" in a system and have significant influence over other variables; in other words, in the context of diseases such as cancer, targeting these variables with treatments and interventions will provide the greatest effects because of their direct and indirect control over other parts of the system. Boolean networks are among the simplest of models, yet they have been shown to adequately model many of the complex dynamics of biological systems. Often ignored in the Boolean network model, however, are the so called basins of attraction. As the attractor states alone have been shown to correspond to cellular phenotypes, it is logical to ask which variables are most responsible for triggering a path through a basin to a particular attractor.
Results: This work claims that logic minimization (i.e. classical circuit design) of the collections of states in Boolean network basins of attraction reveals key players in the network. Furthermore, we claim that the key players identified by this method are often excellent targets for intervention given a network modeling a biological system, and more importantly, that the key players identified are not apparent from the attractor states alone, from existing Boolean network measures, or from other network measurements. We demonstrate these claims with a well-studied yeast cell cycle network and with a WNT5A network for melanoma, computationally predicted from gene expression data.