TY - JOUR
T1 - Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks
AU - Wang, Shuo
AU - Herzog, Erik D.
AU - Kiss, István Z.
AU - Schwartz, William J.
AU - Bloch, Guy
AU - Sebek, Michael
AU - Granados-Fuentes, Daniel
AU - Wang, Liang
AU - Li, Jr Shin
N1 - Publisher Copyright:
© 2018 National Academy of Sciences. All Rights Reserved.
PY - 2018/9/11
Y1 - 2018/9/11
N2 - 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.
AB - 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.
KW - Circadian rhythms
KW - Complex networks
KW - Dynamic topology
KW - Network inference
KW - Social synchronization
UR - http://www.scopus.com/inward/record.url?scp=85052988393&partnerID=8YFLogxK
U2 - 10.1073/pnas.1721286115
DO - 10.1073/pnas.1721286115
M3 - Article
C2 - 30150403
AN - SCOPUS:85052988393
SN - 0027-8424
VL - 115
SP - 9300
EP - 9305
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 37
ER -