Zañudo et al. (FVS control)
Zañudo JGT, Yang G, Albert R
2017 · Proceedings of the National Academy of Sciences of the United States of America
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…
Abstract
From the original paper, Proceedings of the National Academy of Sciences of the United States of America · PubMed
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.
Summary
Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how Zañudo et al. (FVS control) relates to the cross-modality inverse-design framework of the review.
Why this level
Level 3 because candidate interventions enter an explicit forward operator and the predicted post-intervention outcome is what scores each candidate. Representation family is executable intervention model. Cited in §3.4 of the review. Editorial rationale pending review by the maintainer.