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Determining minimal output sets that ensure structural identifiability

The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: “Which experimental outputs should be measured to en...

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Detalles Bibliográficos
Autores principales: Joubert, D., Stigter, J. D., Molenaar, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231658/
https://www.ncbi.nlm.nih.gov/pubmed/30419074
http://dx.doi.org/10.1371/journal.pone.0207334
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author Joubert, D.
Stigter, J. D.
Molenaar, J.
author_facet Joubert, D.
Stigter, J. D.
Molenaar, J.
author_sort Joubert, D.
collection PubMed
description The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: “Which experimental outputs should be measured to ensure that unique model parameters can be calculated?”. Stated formally, we examine the topic of minimal output sets that guarantee a model’s structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times.
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spelling pubmed-62316582018-11-19 Determining minimal output sets that ensure structural identifiability Joubert, D. Stigter, J. D. Molenaar, J. PLoS One Research Article The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: “Which experimental outputs should be measured to ensure that unique model parameters can be calculated?”. Stated formally, we examine the topic of minimal output sets that guarantee a model’s structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times. Public Library of Science 2018-11-12 /pmc/articles/PMC6231658/ /pubmed/30419074 http://dx.doi.org/10.1371/journal.pone.0207334 Text en © 2018 Joubert et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Joubert, D.
Stigter, J. D.
Molenaar, J.
Determining minimal output sets that ensure structural identifiability
title Determining minimal output sets that ensure structural identifiability
title_full Determining minimal output sets that ensure structural identifiability
title_fullStr Determining minimal output sets that ensure structural identifiability
title_full_unstemmed Determining minimal output sets that ensure structural identifiability
title_short Determining minimal output sets that ensure structural identifiability
title_sort determining minimal output sets that ensure structural identifiability
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231658/
https://www.ncbi.nlm.nih.gov/pubmed/30419074
http://dx.doi.org/10.1371/journal.pone.0207334
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