  Control of complex processes is a major goal of network analyses. Most
approaches to control nonlinearly coupled systems require the network topology
and/or network dynamics. Unfortunately, neither the full set of participating
nodes nor the network topology is known for many important systems. On the
other hand, system responses to perturbations are often easily measured. We
show how the collection of such responses (a response surface) can be used for
network control. Analyses of model systems show that response surfaces are
smooth and hence can be approximated using low order polynomials. Importantly,
these approximations are largely insensitive to stochastic fluctuations in data
or measurement errors. They can be used to compute how a small set of nodes
need to be altered in order to direct the network close to a pre-specified
target state. These ideas, illustrated on a nonlinear electrical circuit, can
prove useful in many contexts including in reprogramming cellular states.
