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Identifying Optimal Models to Represent Biochemical Systems
Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885518/ https://www.ncbi.nlm.nih.gov/pubmed/24416170 http://dx.doi.org/10.1371/journal.pone.0083664 |
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author | Apri, Mochamad de Gee, Maarten van Mourik, Simon Molenaar, Jaap |
author_facet | Apri, Mochamad de Gee, Maarten van Mourik, Simon Molenaar, Jaap |
author_sort | Apri, Mochamad |
collection | PubMed |
description | Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an “optimal” reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model. |
format | Online Article Text |
id | pubmed-3885518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38855182014-01-10 Identifying Optimal Models to Represent Biochemical Systems Apri, Mochamad de Gee, Maarten van Mourik, Simon Molenaar, Jaap PLoS One Research Article Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an “optimal” reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model. Public Library of Science 2014-01-08 /pmc/articles/PMC3885518/ /pubmed/24416170 http://dx.doi.org/10.1371/journal.pone.0083664 Text en © 2014 Apri 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Apri, Mochamad de Gee, Maarten van Mourik, Simon Molenaar, Jaap Identifying Optimal Models to Represent Biochemical Systems |
title | Identifying Optimal Models to Represent Biochemical Systems |
title_full | Identifying Optimal Models to Represent Biochemical Systems |
title_fullStr | Identifying Optimal Models to Represent Biochemical Systems |
title_full_unstemmed | Identifying Optimal Models to Represent Biochemical Systems |
title_short | Identifying Optimal Models to Represent Biochemical Systems |
title_sort | identifying optimal models to represent biochemical systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885518/ https://www.ncbi.nlm.nih.gov/pubmed/24416170 http://dx.doi.org/10.1371/journal.pone.0083664 |
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