Given the network of interactions underlying a complex system, what can we learn about controlling such a system solely from its structure? Over a century of research in control theory has given us tools to answer this question, which were widely applied in science and engineering. Yet the current tools do not always consider the inherently nonlinear dynamics of real systems and the naturally occurring system states in their definition of "control", a term whose interpretation varies across disciplines. Here we use a new mathematical framework for structure-based control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This  framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors and which are guaranteed to be effective regardless of the dynamic details and parameters of the underlying system . We use this framework on several real networks, compare its predictions to those of classical  control theory, and identify the topological characteristics that underlie the commonalities and differencesbetween these frameworks . Finally, we illustrate the applicability of this new frameworkin the field of dynamic models by demonstrating its success in two models of a gene regulatory network and identifying the nodes whose override is necessary for control in the general case, but not in specific model instances.
What can we learn about controlling a system solely from its underlying network structure? Here we use a 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 dynamic details and system parameters . We use this framework on several real networks, compare its predictions to those of classical structural control theory, and identify the topological characteristics that underlie the observed differences . 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.
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Given the network of interactions underlying a complex system, what
What
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such
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structure? Over a century of research in control theory has given us tools to answer this question, which were widely applied in science and engineering. Yet the current tools do not always consider the inherently nonlinear dynamics of real systems and the naturally occurring system states in their definition of "control", a term whose interpretation varies across disciplines.
underlying network structure?
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new mathematical framework for structure-based
framework for
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feedback-based
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and which are guaranteed to be effective
,
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parameters of the underlying system
system parameters
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structural
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commonalities and differencesbetween these frameworks
observed differences
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illustrate the applicability of this new frameworkin the field of dynamic models by demonstrating its success in two models of a gene regulatory network and identifying the
demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify