Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 114, Issue 28, Pages 7234-7239Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1617387114
Keywords
network control; nonlinear dynamics; biological networks; complex networks
Categories
Funding
- National Science Foundation [PHY 1205840, 1545832, IIS 1160995]
- SU2C-V Foundation Convergence Scholar Award
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1161007] Funding Source: National Science Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [1545832, 1205840] Funding Source: National Science Foundation
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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 toward 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.
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