A geometrical approach to control and controllability of nonlinear dynamical networks

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control ob...

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Bibliographic Details
Published in:Nature communications 2016-04, Vol.7 (1), p.11323-11323, Article 11323
Main Authors: Wang, Le-Zhi, Su, Ri-Qi, Huang, Zi-Gang, Wang, Xiao, Wang, Wen-Xu, Grebogi, Celso, Lai, Ying-Cheng
Format: Article
Language:English
Subjects:
631/553/2710
631/67
Cell Survival
Chemotactic Factors - pharmacology
Computer Simulation
Gene Regulatory Networks
Humanities and Social Sciences
Humans
Models, Genetic
multidisciplinary
Nonlinear Dynamics
Protein Interaction Mapping
Science
Science (multidisciplinary)
Signal Transduction
T-Lymphocytes - cytology
T-Lymphocytes - drug effects
T-Lymphocytes - immunology
T-Lymphocytes - metabolism
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Summary:In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control. Complex networks, including physical, biological and social systems are ubiquitous, but understanding of how to control them is elusive. Here Wang et al . develop a framework based on the concept of attractor networks to facilitate the study of controllability of nonlinear dynamics in complex systems.
ISSN:2041-1723
2041-1723
DOI:10.1038/ncomms11323