Mutations or alterations in the expression of elements of cellular signaling networks can lead to incorrect behavioral decisions that could result in tumor development or metastasis. Thus, mitigation of the cascading effects of such dysregulations is an important control objective. My group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems. Over the years we found that discrete dynamic modeling is very useful in molding qualitative interaction information into a predictive model. The attractors of these models can be directly related to the real systemâ€™s behaviors, and various interventions are straightforward to implement. We recently developed an efficient method to predict interventions that can drive the system toward a desired attractor or away from an undesired one . This method is based on an integration of the signal transduction network and the regulatory logic into an expanded network, and the identification of a specific type of strongly connected component, called stable motif, of this expanded network. Each stable motif corresponds to a point of no return in the dynamics of the system, and each attractor corresponds to a successive stabilization of a small set of stable motifs. Control of these stable motifs (by imposing a sustained state for a subset of their nodes) drives any initial condition of the system into the desired attractor. The predicted control sets were validated experimentally in two different systems.