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Computational tools for inversion and uncertainty estimation in respirometry
In many physiological systems, real-time endogeneous and exogenous signals in living organisms provide critical information and interpretations of physiological functions; however, these signals or variables of interest are not directly accessible and must be estimated from noisy, measured signals....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139500/ https://www.ncbi.nlm.nih.gov/pubmed/34019586 http://dx.doi.org/10.1371/journal.pone.0251926 |
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author | Cho, Taewon Pendar, Hodjat Chung, Julianne |
author_facet | Cho, Taewon Pendar, Hodjat Chung, Julianne |
author_sort | Cho, Taewon |
collection | PubMed |
description | In many physiological systems, real-time endogeneous and exogenous signals in living organisms provide critical information and interpretations of physiological functions; however, these signals or variables of interest are not directly accessible and must be estimated from noisy, measured signals. In this paper, we study an inverse problem of recovering gas exchange signals of animals placed in a flow-through respirometry chamber from measured gas concentrations. For large-scale experiments (e.g., long scans with high sampling rate) that have many uncertainties (e.g., noise in the observations or an unknown impulse response function), this is a computationally challenging inverse problem. We first describe various computational tools that can be used for respirometry reconstruction and uncertainty quantification when the impulse response function is known. Then, we address the more challenging problem where the impulse response function is not known or only partially known. We describe nonlinear optimization methods for reconstruction, where both the unknown model parameters and the unknown signal are reconstructed simultaneously. Numerical experiments show the benefits and potential impacts of these methods in respirometry. |
format | Online Article Text |
id | pubmed-8139500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81395002021-06-02 Computational tools for inversion and uncertainty estimation in respirometry Cho, Taewon Pendar, Hodjat Chung, Julianne PLoS One Research Article In many physiological systems, real-time endogeneous and exogenous signals in living organisms provide critical information and interpretations of physiological functions; however, these signals or variables of interest are not directly accessible and must be estimated from noisy, measured signals. In this paper, we study an inverse problem of recovering gas exchange signals of animals placed in a flow-through respirometry chamber from measured gas concentrations. For large-scale experiments (e.g., long scans with high sampling rate) that have many uncertainties (e.g., noise in the observations or an unknown impulse response function), this is a computationally challenging inverse problem. We first describe various computational tools that can be used for respirometry reconstruction and uncertainty quantification when the impulse response function is known. Then, we address the more challenging problem where the impulse response function is not known or only partially known. We describe nonlinear optimization methods for reconstruction, where both the unknown model parameters and the unknown signal are reconstructed simultaneously. Numerical experiments show the benefits and potential impacts of these methods in respirometry. Public Library of Science 2021-05-21 /pmc/articles/PMC8139500/ /pubmed/34019586 http://dx.doi.org/10.1371/journal.pone.0251926 Text en © 2021 Cho et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Cho, Taewon Pendar, Hodjat Chung, Julianne Computational tools for inversion and uncertainty estimation in respirometry |
title | Computational tools for inversion and uncertainty estimation in respirometry |
title_full | Computational tools for inversion and uncertainty estimation in respirometry |
title_fullStr | Computational tools for inversion and uncertainty estimation in respirometry |
title_full_unstemmed | Computational tools for inversion and uncertainty estimation in respirometry |
title_short | Computational tools for inversion and uncertainty estimation in respirometry |
title_sort | computational tools for inversion and uncertainty estimation in respirometry |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139500/ https://www.ncbi.nlm.nih.gov/pubmed/34019586 http://dx.doi.org/10.1371/journal.pone.0251926 |
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