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On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods
The mathematical models used in predictive microbiology contain parameters that must be estimated based on experimental data. Due to experimental uncertainty and variability, they cannot be known exactly and must be reported with a measure of uncertainty (usually a standard deviation). In order to i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6711534/ https://www.ncbi.nlm.nih.gov/pubmed/31454353 http://dx.doi.org/10.1371/journal.pone.0220683 |
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author | Garre, Alberto Peñalver-Soto, Jose Lucas Esnoz, Arturo Iguaz, Asunción Fernandez, Pablo S. Egea, Jose A. |
author_facet | Garre, Alberto Peñalver-Soto, Jose Lucas Esnoz, Arturo Iguaz, Asunción Fernandez, Pablo S. Egea, Jose A. |
author_sort | Garre, Alberto |
collection | PubMed |
description | The mathematical models used in predictive microbiology contain parameters that must be estimated based on experimental data. Due to experimental uncertainty and variability, they cannot be known exactly and must be reported with a measure of uncertainty (usually a standard deviation). In order to increase precision (i.e. reduce the standard deviation), it is usual to add extra sampling points. However, recent studies have shown that precision can also be increased without adding extra sampling points by using Optimal Experiment Design, which applies optimization and information theory to identify the most informative experiment under a set of constraints. Nevertheless, to date, there has been scarce contributions to know a priori whether an experimental design is likely to provide the desired precision in the parameter estimates. In this article, two complementary methodologies to predict the parameter precision for a given experimental design are proposed. Both approaches are based on in silico simulations, so they can be performed before any experimental work. The first one applies Monte Carlo simulations to estimate the standard deviation of the model parameters, whereas the second one applies the properties of the Fisher Information Matrix to estimate the volume of the confidence ellipsoids. The application of these methods to a case study of dynamic microbial inactivation, showing how they can be used to compare experimental designs and assess their precision, is illustrated. The results show that, as expected, the optimal experimental design is more accurate than the uniform design with the same number of data points. Furthermore, it is demonstrated that, for some heating profiles, the uniform design does not ensure that a higher number of sampling points increases precision. Therefore, optimal experimental designs are highly recommended in predictive microbiology. |
format | Online Article Text |
id | pubmed-6711534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67115342019-09-10 On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods Garre, Alberto Peñalver-Soto, Jose Lucas Esnoz, Arturo Iguaz, Asunción Fernandez, Pablo S. Egea, Jose A. PLoS One Research Article The mathematical models used in predictive microbiology contain parameters that must be estimated based on experimental data. Due to experimental uncertainty and variability, they cannot be known exactly and must be reported with a measure of uncertainty (usually a standard deviation). In order to increase precision (i.e. reduce the standard deviation), it is usual to add extra sampling points. However, recent studies have shown that precision can also be increased without adding extra sampling points by using Optimal Experiment Design, which applies optimization and information theory to identify the most informative experiment under a set of constraints. Nevertheless, to date, there has been scarce contributions to know a priori whether an experimental design is likely to provide the desired precision in the parameter estimates. In this article, two complementary methodologies to predict the parameter precision for a given experimental design are proposed. Both approaches are based on in silico simulations, so they can be performed before any experimental work. The first one applies Monte Carlo simulations to estimate the standard deviation of the model parameters, whereas the second one applies the properties of the Fisher Information Matrix to estimate the volume of the confidence ellipsoids. The application of these methods to a case study of dynamic microbial inactivation, showing how they can be used to compare experimental designs and assess their precision, is illustrated. The results show that, as expected, the optimal experimental design is more accurate than the uniform design with the same number of data points. Furthermore, it is demonstrated that, for some heating profiles, the uniform design does not ensure that a higher number of sampling points increases precision. Therefore, optimal experimental designs are highly recommended in predictive microbiology. Public Library of Science 2019-08-27 /pmc/articles/PMC6711534/ /pubmed/31454353 http://dx.doi.org/10.1371/journal.pone.0220683 Text en © 2019 Garre 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 Garre, Alberto Peñalver-Soto, Jose Lucas Esnoz, Arturo Iguaz, Asunción Fernandez, Pablo S. Egea, Jose A. On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods |
title | On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods |
title_full | On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods |
title_fullStr | On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods |
title_full_unstemmed | On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods |
title_short | On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods |
title_sort | on the use of in-silico simulations to support experimental design: a case study in microbial inactivation of foods |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6711534/ https://www.ncbi.nlm.nih.gov/pubmed/31454353 http://dx.doi.org/10.1371/journal.pone.0220683 |
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