Reference : Robust dynamical network reconstruction
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/10993/20349
Robust dynamical network reconstruction
English
Yuan, Y. mailto [> >]
Stan, G. B. [> >]
Warnick, S. [> >]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2010
The proceedings of the 49th IEEE Conference on Decision and Control (CDC)
IEEE
810 - 815
Yes
978-1-4244-7745-6
49th IEEE Conference on Decision and Control (CDC)
December 15-17, 2010
Atlanta, GA
USA
[en] Motivated by biological applications, this paper addresses the problem of network reconstruction from data. Previous work has shown necessary and sufficient conditions for network reconstruction of noise-free LTI systems. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). Algorithms are therefore proposed to reconstruct dynamical (Boolean) network structure from time-series (steady-state) data respectively in presence of noise and nonlinearities. In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular Boolean structures. Information criteria and optimisation technique balancing such distance and model complexity are introduced to search for the true structure. We conclude with biologically-inspired network reconstruction examples which include noise and nonlinearities.
http://hdl.handle.net/10993/20349
10.1109/CDC.2010.5717657

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