Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CDC.2017.8263702 | 2017 IEEE 56th Annual Conference on Decision and Control (CDC) |
Keywords | Field | DocType |
nonlinear Boolean dynamic networks,network reconstruction,systems biology,nonlinear dynamical systems,robust inference method,Millar-10 model,causal networks,linear network identifiability,mRNA,eukaryotic circadian clocks | Mathematical optimization,Causality,Nonlinear system,Linear system,Computer science,Inference,Identifiability,Systems biology,Theoretical computer science,Ground truth,System identification | Conference |
ISSN | ISBN | Citations |
0743-1546 | 978-1-5090-2874-0 | 1 |
PageRank | References | Authors |
0.47 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zuogong Yue | 1 | 6 | 1.06 |
| Johan Thunberg | 2 | 138 | 19.15 |
| Lennart Ljung | 3 | 1993 | 270.89 |
| Goncalves, J. | 4 | 404 | 42.24 |