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On definition and inference of nonlinear Boolean dynamic networks

Yue, Zuogong ; Thunberg, Johan LU ; Ljung, Lennart and Goncalves, Jorge (2017) 56th IEEE Annual Conference on Decision and Control, CDC 2017 p.429-434
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
pages
429 - 434
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
56th IEEE Annual Conference on Decision and Control, CDC 2017
conference location
Melbourne, Australia
conference dates
2017-12-12 - 2017-12-15
external identifiers
  • scopus:85046124230
ISBN
978-1-5386-0457-1
DOI
10.1109/CDC.2017.8263702
language
English
LU publication?
no
id
b57b31c7-2f6e-43d5-9da2-7b0322e97b30
date added to LUP
2024-09-05 15:04:20
date last changed
2025-04-04 15:08:26
@inproceedings{b57b31c7-2f6e-43d5-9da2-7b0322e97b30,
  abstract     = {{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.}},
  author       = {{Yue, Zuogong and Thunberg, Johan and Ljung, Lennart and Goncalves, Jorge}},
  booktitle    = {{2017 IEEE 56th Annual Conference on Decision and Control (CDC)}},
  isbn         = {{978-1-5386-0457-1}},
  language     = {{eng}},
  pages        = {{429--434}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{On definition and inference of nonlinear Boolean dynamic networks}},
  url          = {{http://dx.doi.org/10.1109/CDC.2017.8263702}},
  doi          = {{10.1109/CDC.2017.8263702}},
  year         = {{2017}},
}