Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network t...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2018-09, Vol.115 (37), p.9300-9305
Main Authors: Wang, Shuo, Herzog, Erik D., Kiss, István Z., Schwartz, William J., Bloch, Guy, Sebek, Michael, Granados-Fuentes, Daniel, Wang, Liang, Li, Jr-Shin
Format: Article
Language:English
Subjects:
Biochemistry
Biological Sciences
Brain
Circadian rhythm
Circadian rhythms
Decoding
Electrical networks
Electrochemistry
Inverse problems
Mice
Models, Theoretical
Network topologies
Neural networks
Nonlinear equations
Nonlinear systems
Nonlinearity
Organic chemistry
Oscillators
Physical Sciences
Social organization
Synchronism
Synchronization
Topology
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Summary:Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1721286115