Control of complex processes is a major goal of network analyses. Unfortunately, deriving models accurate enough to be used for control is extremely difficult, especially for large networks of nonlinearly coupled nodes. However , system responses to perturbations are often easily measured. We show that the collection of such responses -a response surface- can be used for control. Analysis of model systems shows that response surfaces are smooth and  can be approximated using data on a small set of perturbations. The methodology, here validated on nonlinear electrical circuits , can prove useful in many contexts including in reprogramming cellular states and in the design of therapies for genetic diseases .
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 .
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Unfortunately, deriving models accurate enough to be used for control is extremely difficult, especially for large networks of nonlinearly coupled nodes. However
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
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that
how
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-a response surface-
(a response surface)
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control. Analysis
network control. Analyses
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shows
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hence
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data on
low order polynomials. Importantly, these approximations are largely insensitive to stochastic fluctuations in data or measurement errors. They can be used to compute how
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perturbations. The methodology, here validated on nonlinear electrical circuits
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
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and in the design of therapies for genetic diseases