Structure-based control of complex networks with nonlinear dynamics
This work provides a practical control method for nonlinear network dynamics, relevant to biological, technological, and social systems, but the approach is an adaptation of existing theory rather than a fundamental breakthrough.
The authors adapt a feedback-based control framework for complex networks with nonlinear dynamics, enabling node overrides that steer systems toward desired behaviors without requiring specific functional forms or parameters. They identify topological characteristics underlying these overrides and demonstrate applicability in gene regulatory network models.
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.