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Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles
Dynamic behavior of biological systems is commonly represented by non-linear models such as ordinary differential equations. A frequently encountered task in such systems is the estimation of model parameters based on measurement of biochemical compounds. Non-linear models require special techniques...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906444/ https://www.ncbi.nlm.nih.gov/pubmed/35281278 http://dx.doi.org/10.3389/fmolb.2022.800856 |
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author | Litwin, Tim Timmer, Jens Kreutz, Clemens |
author_facet | Litwin, Tim Timmer, Jens Kreutz, Clemens |
author_sort | Litwin, Tim |
collection | PubMed |
description | Dynamic behavior of biological systems is commonly represented by non-linear models such as ordinary differential equations. A frequently encountered task in such systems is the estimation of model parameters based on measurement of biochemical compounds. Non-linear models require special techniques to estimate the uncertainty of the obtained model parameters and predictions, e.g. by exploiting the concept of the profile likelihood. Model parameters with significant uncertainty associated with their estimates hinder the interpretation of model results. Informing these model parameters by optimal experimental design minimizes the additional amount of data and therefore resources required in experiments. However, existing techniques of experimental design either require prior parameter distributions in Bayesian approaches or do not adequately deal with the non-linearity of the system in frequentist approaches. For identification of optimal experimental designs, we propose a two-dimensional profile likelihood approach, providing a design criterion which meaningfully represents the expected parameter uncertainty after measuring data for a specified experimental condition. The described approach is implemented into the open source toolbox Data2Dynamics in Matlab. The applicability of the method is demonstrated on an established systems biology model. For this demonstration, available data has been censored to simulate a setting in which parameters are not yet well determined. After determining the optimal experimental condition from the censored ones, a realistic evaluation was possible by re-introducing the censored data point corresponding to the optimal experimental condition. This provided a validation that our method is feasible in real-world applications. The approach applies to, but is not limited to, models in systems biology. |
format | Online Article Text |
id | pubmed-8906444 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89064442022-03-10 Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles Litwin, Tim Timmer, Jens Kreutz, Clemens Front Mol Biosci Molecular Biosciences Dynamic behavior of biological systems is commonly represented by non-linear models such as ordinary differential equations. A frequently encountered task in such systems is the estimation of model parameters based on measurement of biochemical compounds. Non-linear models require special techniques to estimate the uncertainty of the obtained model parameters and predictions, e.g. by exploiting the concept of the profile likelihood. Model parameters with significant uncertainty associated with their estimates hinder the interpretation of model results. Informing these model parameters by optimal experimental design minimizes the additional amount of data and therefore resources required in experiments. However, existing techniques of experimental design either require prior parameter distributions in Bayesian approaches or do not adequately deal with the non-linearity of the system in frequentist approaches. For identification of optimal experimental designs, we propose a two-dimensional profile likelihood approach, providing a design criterion which meaningfully represents the expected parameter uncertainty after measuring data for a specified experimental condition. The described approach is implemented into the open source toolbox Data2Dynamics in Matlab. The applicability of the method is demonstrated on an established systems biology model. For this demonstration, available data has been censored to simulate a setting in which parameters are not yet well determined. After determining the optimal experimental condition from the censored ones, a realistic evaluation was possible by re-introducing the censored data point corresponding to the optimal experimental condition. This provided a validation that our method is feasible in real-world applications. The approach applies to, but is not limited to, models in systems biology. Frontiers Media S.A. 2022-02-23 /pmc/articles/PMC8906444/ /pubmed/35281278 http://dx.doi.org/10.3389/fmolb.2022.800856 Text en Copyright © 2022 Litwin, Timmer and Kreutz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Litwin, Tim Timmer, Jens Kreutz, Clemens Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles |
title | Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles |
title_full | Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles |
title_fullStr | Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles |
title_full_unstemmed | Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles |
title_short | Optimal Experimental Design Based on Two-Dimensional Likelihood Profiles |
title_sort | optimal experimental design based on two-dimensional likelihood profiles |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906444/ https://www.ncbi.nlm.nih.gov/pubmed/35281278 http://dx.doi.org/10.3389/fmolb.2022.800856 |
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