system identification; network inference; systems biology
Résumé :
[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.
Centre de recherche :
- Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group)
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
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)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
On definition and inference of nonlinear Boolean dynamic networks
Date de publication/diffusion :
décembre 2017
Nom de la manifestation :
56th IEEE Conference on Decision and Control
Lieu de la manifestation :
Melbourne, Australie
Date de la manifestation :
from 12-12-2017 to 15-12-2017
Manifestation à portée :
International
Titre de l'ouvrage principal :
On definition and inference of nonlinear Boolean dynamic networks
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