  What can we learn about controlling a system solely from its underlying
network structure? Here we adapt a recently developed framework for control of
networks governed by a broad class of nonlinear dynamics that includes the
major dynamic models of biological, technological, and social processes. This
feedback-based framework provides realizable node overrides that steer a system
towards any of its natural long term dynamic behaviors, regardless of the
specific functional forms and system parameters. We use this framework on
several real networks, identify the topological characteristics that underlie
the predicted node overrides, and compare its predictions to those of
structural controllability in control theory. Finally, we demonstrate this
framework's applicability in dynamic models of gene regulatory networks and
identify nodes whose override is necessary for control in the general case, but
not in specific model instances.
