  Identifying control strategies for biological networks is paramount for
practical applications that involve reprogramming a cell's fate, such as
disease therapeutics and stem cell reprogramming. Here we develop a novel
network control framework that integrates the structural and functional
information available for intracellular networks to predict control targets.
Formulated in a logical dynamic scheme, our approach drives any initial state
to the target state with 100% effectiveness and needs to be applied only
transiently for the network to reach and stay in the desired state. We
illustrate our method's potential to find intervention targets for cancer
treatment and cell differentiation by applying it to a leukemia signaling
network and to the network controlling the differentiation of helper T cells.
We find that the predicted control targets are effective in a broad dynamic
framework. Moreover, several of the predicted interventions are supported by
experiments.
