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Identification of Nonlinear State-Space Systems from Heterogeneous Datasets
Imperial College London, London, U.K..
Huazhong University of Science and Technology, Wuhan, China.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
University of Cambridge, Cambridge, U.K..
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2018 (English)In: IEEE Transactions on Control of Network Systems, ISSN 2372-2533, Vol. 5, no 2, p. 737-747Article in journal (Refereed) Published
Abstract [en]

This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through 1) replicate measurements from the same biological system or 2) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed heterogeneous data. To enable scale up to large number of features, the resulting nonconvex optimization problem is relaxed to a reweighted Group Lasso problem using a convex-concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalized eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise.

Place, publisher, year, edition, pages
IEEE, 2018. Vol. 5, no 2, p. 737-747
Keywords [en]
biological system modeling, system identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-161502DOI: 10.1109/TCNS.2017.2758966ISI: 000435505900005Scopus ID: 2-s2.0-85030779537OAI: oai:DiVA.org:liu-161502DiVA, id: diva2:1367392
Available from: 2019-11-03 Created: 2019-11-03 Last updated: 2019-11-08Bibliographically approved

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Ljung, Lennart

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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