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Observability of Complex Systems: Finding the Gap
For a reconstruction of state and parameter values in a dynamic system model, first the question whether these values can be uniquely determined from the data must be answered. This structural model property is known as observability or, in case of parameter calibration only, identifiability. Testin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707395/ https://www.ncbi.nlm.nih.gov/pubmed/29185491 http://dx.doi.org/10.1038/s41598-017-16682-x |
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author | Stigter, J. D. Joubert, D. Molenaar, J. |
author_facet | Stigter, J. D. Joubert, D. Molenaar, J. |
author_sort | Stigter, J. D. |
collection | PubMed |
description | For a reconstruction of state and parameter values in a dynamic system model, first the question whether these values can be uniquely determined from the data must be answered. This structural model property is known as observability or, in case of parameter calibration only, identifiability. Testing a given model for observability is a well studied problem in the systems and control sciences. However, it is increasingly difficult, if not impossible, to address this property for large size models that, nowadays, are frequently used. We demonstrate the application of a recently developed algorithm that overcomes this problem and is remarkably efficient. As an illustration we show how an observability analysis for a Chinese Hamster Ovary Cell model (34 states, 117 parameters), a JAKSTAT signalling model (31 states, 51 parameters), and a MAP Kinase model (100 states, 88 parameters) can be established in a very short time. |
format | Online Article Text |
id | pubmed-5707395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57073952017-12-06 Observability of Complex Systems: Finding the Gap Stigter, J. D. Joubert, D. Molenaar, J. Sci Rep Article For a reconstruction of state and parameter values in a dynamic system model, first the question whether these values can be uniquely determined from the data must be answered. This structural model property is known as observability or, in case of parameter calibration only, identifiability. Testing a given model for observability is a well studied problem in the systems and control sciences. However, it is increasingly difficult, if not impossible, to address this property for large size models that, nowadays, are frequently used. We demonstrate the application of a recently developed algorithm that overcomes this problem and is remarkably efficient. As an illustration we show how an observability analysis for a Chinese Hamster Ovary Cell model (34 states, 117 parameters), a JAKSTAT signalling model (31 states, 51 parameters), and a MAP Kinase model (100 states, 88 parameters) can be established in a very short time. Nature Publishing Group UK 2017-11-29 /pmc/articles/PMC5707395/ /pubmed/29185491 http://dx.doi.org/10.1038/s41598-017-16682-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Stigter, J. D. Joubert, D. Molenaar, J. Observability of Complex Systems: Finding the Gap |
title | Observability of Complex Systems: Finding the Gap |
title_full | Observability of Complex Systems: Finding the Gap |
title_fullStr | Observability of Complex Systems: Finding the Gap |
title_full_unstemmed | Observability of Complex Systems: Finding the Gap |
title_short | Observability of Complex Systems: Finding the Gap |
title_sort | observability of complex systems: finding the gap |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707395/ https://www.ncbi.nlm.nih.gov/pubmed/29185491 http://dx.doi.org/10.1038/s41598-017-16682-x |
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