An efficient procedure to assist in the re-parametrization of structurally unidentifiable models
An efficient method that assists in the re-parametrization of structurally unidentifiable models is introduced. It significantly reduces computational demand by combining numerical and symbolic identifiability calculations. This hybrid approach facilitates the re-parametrization of large unidentifiable ordinary differential equation models, including models where state transformations are required. A model is first assessed numerically, to discover potential structurally unidentifiable parameters. We then use symbolic calculations to confirm the numerical results, after which we describe the algebraic relationships between the unidentifiable parameters. Finally, the unidentifiable parameters are substituted with new parameters and simplification ensures that all the unidentifiable parameters are eliminated from the original model structure. The novelty of this method is its utilisation of numerical results, which notably reduces the number of symbolic calculations required. We illustrate our procedure and the detailed re-parametrization process in 5 examples: (1) an immunological model, (2) a microbial growth model, (3) a lung cancer model, (4) a JAK/STAT model, and (5) a small linear model with a non-scalable re-parametrization.
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Format: | Article/Letter to editor biblioteca |
Language: | English |
Subjects: | Correlated parameter sets, Re-parametrization, State transformation, Structural identifiability, Systems biology, |
Online Access: | https://research.wur.nl/en/publications/an-efficient-procedure-to-assist-in-the-re-parametrization-of-str |
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dig-wur-nl-wurpubs-5638272024-10-02 Joubert, D. Stigter, J.D. Molenaar, J. Article/Letter to editor Mathematical Biosciences 323 (2020) ISSN: 0025-5564 An efficient procedure to assist in the re-parametrization of structurally unidentifiable models 2020 An efficient method that assists in the re-parametrization of structurally unidentifiable models is introduced. It significantly reduces computational demand by combining numerical and symbolic identifiability calculations. This hybrid approach facilitates the re-parametrization of large unidentifiable ordinary differential equation models, including models where state transformations are required. A model is first assessed numerically, to discover potential structurally unidentifiable parameters. We then use symbolic calculations to confirm the numerical results, after which we describe the algebraic relationships between the unidentifiable parameters. Finally, the unidentifiable parameters are substituted with new parameters and simplification ensures that all the unidentifiable parameters are eliminated from the original model structure. The novelty of this method is its utilisation of numerical results, which notably reduces the number of symbolic calculations required. We illustrate our procedure and the detailed re-parametrization process in 5 examples: (1) an immunological model, (2) a microbial growth model, (3) a lung cancer model, (4) a JAK/STAT model, and (5) a small linear model with a non-scalable re-parametrization. en application/pdf https://research.wur.nl/en/publications/an-efficient-procedure-to-assist-in-the-re-parametrization-of-str 10.1016/j.mbs.2020.108328 https://edepot.wur.nl/521042 Correlated parameter sets Re-parametrization State transformation Structural identifiability Systems biology https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/ Wageningen University & Research |
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Correlated parameter sets Re-parametrization State transformation Structural identifiability Systems biology Correlated parameter sets Re-parametrization State transformation Structural identifiability Systems biology |
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Correlated parameter sets Re-parametrization State transformation Structural identifiability Systems biology Correlated parameter sets Re-parametrization State transformation Structural identifiability Systems biology Joubert, D. Stigter, J.D. Molenaar, J. An efficient procedure to assist in the re-parametrization of structurally unidentifiable models |
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An efficient method that assists in the re-parametrization of structurally unidentifiable models is introduced. It significantly reduces computational demand by combining numerical and symbolic identifiability calculations. This hybrid approach facilitates the re-parametrization of large unidentifiable ordinary differential equation models, including models where state transformations are required. A model is first assessed numerically, to discover potential structurally unidentifiable parameters. We then use symbolic calculations to confirm the numerical results, after which we describe the algebraic relationships between the unidentifiable parameters. Finally, the unidentifiable parameters are substituted with new parameters and simplification ensures that all the unidentifiable parameters are eliminated from the original model structure. The novelty of this method is its utilisation of numerical results, which notably reduces the number of symbolic calculations required. We illustrate our procedure and the detailed re-parametrization process in 5 examples: (1) an immunological model, (2) a microbial growth model, (3) a lung cancer model, (4) a JAK/STAT model, and (5) a small linear model with a non-scalable re-parametrization. |
format |
Article/Letter to editor |
topic_facet |
Correlated parameter sets Re-parametrization State transformation Structural identifiability Systems biology |
author |
Joubert, D. Stigter, J.D. Molenaar, J. |
author_facet |
Joubert, D. Stigter, J.D. Molenaar, J. |
author_sort |
Joubert, D. |
title |
An efficient procedure to assist in the re-parametrization of structurally unidentifiable models |
title_short |
An efficient procedure to assist in the re-parametrization of structurally unidentifiable models |
title_full |
An efficient procedure to assist in the re-parametrization of structurally unidentifiable models |
title_fullStr |
An efficient procedure to assist in the re-parametrization of structurally unidentifiable models |
title_full_unstemmed |
An efficient procedure to assist in the re-parametrization of structurally unidentifiable models |
title_sort |
efficient procedure to assist in the re-parametrization of structurally unidentifiable models |
url |
https://research.wur.nl/en/publications/an-efficient-procedure-to-assist-in-the-re-parametrization-of-str |
work_keys_str_mv |
AT joubertd anefficientproceduretoassistinthereparametrizationofstructurallyunidentifiablemodels AT stigterjd anefficientproceduretoassistinthereparametrizationofstructurallyunidentifiablemodels AT molenaarj anefficientproceduretoassistinthereparametrizationofstructurallyunidentifiablemodels AT joubertd efficientproceduretoassistinthereparametrizationofstructurallyunidentifiablemodels AT stigterjd efficientproceduretoassistinthereparametrizationofstructurallyunidentifiablemodels AT molenaarj efficientproceduretoassistinthereparametrizationofstructurallyunidentifiablemodels |
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