Reference : On definition and inference of nonlinear Boolean dynamic networks

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/35052

Title :	On definition and inference of nonlinear Boolean dynamic networks
Language :	English
Author, co-author :	Yue, Zuogong [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Life Science Research Unit >]
	Thunberg, Johan [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Ljung, Lennart []
	Goncalves, Jorge [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Publication date :	Dec-2017
Main document title :	On definition and inference of nonlinear Boolean dynamic networks
Publisher :	IEEE
Peer reviewed :	Yes
Audience :	International
ISBN :	978-1-5090-2873-3
Event name :	56th IEEE Conference on Decision and Control
Event date :	from 12-12-2017 to 15-12-2017
Event place (city) :	Melbourne
Event country :	Australia
Keywords :	[en] system identification ; network inference ; systems biology
Abstract :	[en] Network reconstruction has become particularly important in systems biology, and is now expected to deliver information on causality. Systems in nature are inherently nonlinear. However, for nonlinear dynamical systems with hidden states, how to give a useful definition of dynamic networks is still an open question. This paper presents a useful definition of Boolean dynamic networks for a large class of nonlinear systems. Moreover, a robust inference method is provided. The well-known Millar-10 model in systems biology is used as a numerical example, which provides the ground truth of causal networks for key mRNAs involved in eukaryotic circadian clocks. In addition, as second contribution of this paper, we suggest definitions of linear network identifiability, which helps to unify the available work on network identifiability.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group)
Funders :	Fonds National de la Recherche - FnR
Permalink :	http://hdl.handle.net/10993/35052
DOI :	10.1109/CDC.2017.8263702
Other URL :	https://www.researchgate.net/publication/320084159_On_Definition_and_Inference_of_Nonlinear_Boolean_Dynamic_Networks

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