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:	Juliane Liepe, Sarah Filippi, Michał Komorowski, Michael P H Stumpf
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/7f9c002c367f44b9bddf38ba27308a88
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spelling	oai:doaj.org-article:7f9c002c367f44b9bddf38ba27308a882021-11-18T05:52:29ZMaximizing the information content of experiments in systems biology.1553-734X1553-735810.1371/journal.pcbi.1002888https://doaj.org/article/7f9c002c367f44b9bddf38ba27308a882013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23382663/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Our 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.Juliane LiepeSarah FilippiMichał KomorowskiMichael P H StumpfPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 1, p e1002888 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Juliane Liepe Sarah Filippi Michał Komorowski Michael P H Stumpf Maximizing the information content of experiments in systems biology.
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	article
author	Juliane Liepe Sarah Filippi Michał Komorowski Michael P H Stumpf
author_facet	Juliane Liepe Sarah Filippi Michał Komorowski Michael P H Stumpf
author_sort	Juliane Liepe
title	Maximizing the information content of experiments in systems biology.
title_short	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_sort	maximizing the information content of experiments in systems biology.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/7f9c002c367f44b9bddf38ba27308a88
work_keys_str_mv	AT julianeliepe maximizingtheinformationcontentofexperimentsinsystemsbiology AT sarahfilippi maximizingtheinformationcontentofexperimentsinsystemsbiology AT michałkomorowski maximizingtheinformationcontentofexperimentsinsystemsbiology AT michaelphstumpf maximizingtheinformationcontentofexperimentsinsystemsbiology
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Maximizing the information content of experiments in systems biology.

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