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Profile likelihood in systems biology

  • Inferring knowledge about biological processes by a mathematical description is a major characteristic of Systems Biology. To understand and predict system's behavior the available experimental information is translated into a mathematical model. Since the availability of experimental data is often limited and measurements contain noise, it is essential to appropriately translate experimental uncertainty to model parameters as well as to model predictions. This is especially important in Systems Biology because typically large and complex models are applied and therefore the limited experimental knowledge might yield weakly specified model components. Likelihood profiles have been recently suggested and applied in the Systems Biology for as- sessing parameter and prediction uncertainty. In this article, the profile likelihood concept is reviewed and the potential of the approach is demonstrated for a model of the erythropoietin (EPO) receptor. This article is protected by copyright.Inferring knowledge about biological processes by a mathematical description is a major characteristic of Systems Biology. To understand and predict system's behavior the available experimental information is translated into a mathematical model. Since the availability of experimental data is often limited and measurements contain noise, it is essential to appropriately translate experimental uncertainty to model parameters as well as to model predictions. This is especially important in Systems Biology because typically large and complex models are applied and therefore the limited experimental knowledge might yield weakly specified model components. Likelihood profiles have been recently suggested and applied in the Systems Biology for as- sessing parameter and prediction uncertainty. In this article, the profile likelihood concept is reviewed and the potential of the approach is demonstrated for a model of the erythropoietin (EPO) receptor. This article is protected by copyright. All rights reserved.show moreshow less

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Metadaten
Author:Clemens Kreutz, Andreas RaueORCiDGND, Daniel Kaschek, Jens Timmer
URN:urn:nbn:de:bvb:384-opus4-1132385
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/113238
ISSN:1742-464XOPAC
ISSN:1742-4658OPAC
Parent Title (English):The FEBS Journal
Publisher:Wiley
Place of publication:Hoboken, NJ
Type:Article
Language:English
Year of first Publication:2013
Publishing Institution:Universität Augsburg
Release Date:2024/06/03
Volume:280
Issue:11
First Page:2564
Last Page:2571
DOI:https://doi.org/10.1111/febs.12276
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Modellierung und Simulation biologischer Prozesse
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):Deutsches Urheberrecht