og:image: http://journals.plos.org/ploscompbiol/article/figure/image?id=10.1371/journal.pcbi.1002888.g006&size=inline
citation_author_institution: Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
citation_title: Maximizing the Information Content of Experiments in Systems Biology
twitter:card: summary
keywords: Experimental design,AKT signaling cascade,Information entropy,Monte Carlo method,Entropy,Simulation and modeling,Mathematical models,Probability distribution
citation_reference: citation_title=The nature of systems biology;citation_author=F Bruggeman;citation_author=H Westerhoff;citation_journal_title=TRENDS in Microbiology;citation_volume=15;citation_number=15;citation_first_page=45;citation_last_page=50;citation_publication_date=2007;
citation_publisher: Public Library of Science
citation_journal_title: PLOS Computational Biology
description: Author Summary For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.
citation_date: 31.01.2013
title: Maximizing the Information Content of Experiments in Systems Biology
og:description: Author Summary For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.
twitter:image: http://journals.plos.org/ploscompbiol/article/figure/image?id=10.1371/journal.pcbi.1002888.g006&size=inline
citation_issn: 1553-7358
twitter:site: @ploscompbiol
dc:title: Maximizing the Information Content of Experiments in Systems Biology
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citation_pdf_url: http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002888&type=printable
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twitter:title: Maximizing the Information Content of Experiments in Systems Biology
og:type: article
citation_journal_abbrev: PLOS Computational Biology
og:title: Maximizing the Information Content of Experiments in Systems Biology
citation_author: Juliane Liepe
citation_issue: 1
citation_firstpage: e1002888
citation_doi: 10.1371/journal.pcbi.1002888
twitter:description: Author Summary For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.
dc.identifier: 10.1371/journal.pcbi.1002888
citation_volume: 9
og:url: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002888
Content-Language: en