Assessing the role of initial conditions in the local structural identifiability of large dynamic models

Abstract Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input–output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the...

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Autores principales:	Dominique Joubert, J. D. Stigter, Jaap Molenaar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aa7a1380e98b433ab79704b964600be8
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spelling	oai:doaj.org-article:aa7a1380e98b433ab79704b964600be82021-12-02T17:08:23ZAssessing the role of initial conditions in the local structural identifiability of large dynamic models10.1038/s41598-021-96293-92045-2322https://doaj.org/article/aa7a1380e98b433ab79704b964600be82021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96293-9https://doaj.org/toc/2045-2322Abstract Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input–output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the model development process. The story however, often ends with a structurally unidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model’s unidentifiability: problematic initial conditions. It is well-known that certain initial values may result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model’s identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the “unidentifiable model” problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied to get the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structural unidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.Dominique JoubertJ. D. StigterJaap MolenaarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dominique Joubert J. D. Stigter Jaap Molenaar Assessing the role of initial conditions in the local structural identifiability of large dynamic models
description	Abstract Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input–output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the model development process. The story however, often ends with a structurally unidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model’s unidentifiability: problematic initial conditions. It is well-known that certain initial values may result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model’s identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the “unidentifiable model” problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied to get the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structural unidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.
format	article
author	Dominique Joubert J. D. Stigter Jaap Molenaar
author_facet	Dominique Joubert J. D. Stigter Jaap Molenaar
author_sort	Dominique Joubert
title	Assessing the role of initial conditions in the local structural identifiability of large dynamic models
title_short	Assessing the role of initial conditions in the local structural identifiability of large dynamic models
title_full	Assessing the role of initial conditions in the local structural identifiability of large dynamic models
title_fullStr	Assessing the role of initial conditions in the local structural identifiability of large dynamic models
title_full_unstemmed	Assessing the role of initial conditions in the local structural identifiability of large dynamic models
title_sort	assessing the role of initial conditions in the local structural identifiability of large dynamic models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aa7a1380e98b433ab79704b964600be8
work_keys_str_mv	AT dominiquejoubert assessingtheroleofinitialconditionsinthelocalstructuralidentifiabilityoflargedynamicmodels AT jdstigter assessingtheroleofinitialconditionsinthelocalstructuralidentifiabilityoflargedynamicmodels AT jaapmolenaar assessingtheroleofinitialconditionsinthelocalstructuralidentifiabilityoflargedynamicmodels
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Assessing the role of initial conditions in the local structural identifiability of large dynamic models

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