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Maximizing the Information Content of Experiments in Systems Biology
Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3561087/ https://www.ncbi.nlm.nih.gov/pubmed/23382663 http://dx.doi.org/10.1371/journal.pcbi.1002888 |
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author | Liepe, Juliane Filippi, Sarah Komorowski, Michał Stumpf, Michael P. H. |
author_facet | Liepe, Juliane Filippi, Sarah Komorowski, Michał Stumpf, Michael P. H. |
author_sort | Liepe, Juliane |
collection | PubMed |
description | Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior. |
format | Online Article Text |
id | pubmed-3561087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35610872013-02-04 Maximizing the Information Content of Experiments in Systems Biology Liepe, Juliane Filippi, Sarah Komorowski, Michał Stumpf, Michael P. H. PLoS Comput Biol Research Article Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior. Public Library of Science 2013-01-31 /pmc/articles/PMC3561087/ /pubmed/23382663 http://dx.doi.org/10.1371/journal.pcbi.1002888 Text en © 2013 Liepe 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 Liepe, Juliane Filippi, Sarah Komorowski, Michał Stumpf, Michael P. H. Maximizing the Information Content of Experiments in Systems Biology |
title | Maximizing the Information Content of Experiments in Systems Biology |
title_full | Maximizing the Information Content of Experiments in Systems Biology |
title_fullStr | Maximizing the Information Content of Experiments in Systems Biology |
title_full_unstemmed | Maximizing the Information Content of Experiments in Systems Biology |
title_short | Maximizing the Information Content of Experiments in Systems Biology |
title_sort | maximizing the information content of experiments in systems biology |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3561087/ https://www.ncbi.nlm.nih.gov/pubmed/23382663 http://dx.doi.org/10.1371/journal.pcbi.1002888 |
work_keys_str_mv | AT liepejuliane maximizingtheinformationcontentofexperimentsinsystemsbiology AT filippisarah maximizingtheinformationcontentofexperimentsinsystemsbiology AT komorowskimichał maximizingtheinformationcontentofexperimentsinsystemsbiology AT stumpfmichaelph maximizingtheinformationcontentofexperimentsinsystemsbiology |