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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...

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Autores principales: Liepe, Juliane, Filippi, Sarah, Komorowski, Michał, Stumpf, Michael P. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
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.
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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
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