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Easy parameter identifiability analysis with COPASI.
Schaber, Jörg.
Afiliación
  • Schaber J; Institute for Experimental Internal Medicine, Medical Faculty, Otto von Guericke University, Magdeburg, Germany. schaber@med.ovgu.de
Biosystems ; 110(3): 183-5, 2012 Dec.
Article en En | MEDLINE | ID: mdl-23041463
BACKGROUND AND SCOPE: Differential equation systems modeling biochemical reaction networks can only give quantitative predictions, when they are in accordance with experimental data. However, even if a model can well recapitulate given data, it is often the case that some of its kinetic parameters can be arbitrarily chosen without significantly affecting the simulation results. This indicates a lack of appropriate data to determine those parameters. In this case, the parameter is called to be practically non-identifiable. Well-identified parameters are paramount for reliable quantitative predictions and, therefore, identifiability analysis is an important topic in modeling of biochemical reaction networks. Here, we describe a hidden feature of the free modeling software COPASI, which can be exploited to easily and quickly conduct a parameter identifiability analysis of differential equation systems by calculating likelihood profiles. The proposed combination of an established method for parameter identifiability analysis with the user-friendly features of COPASI offers an easy and rapid access to parameter identifiability analysis even for non-experts. AVAILABILITY: COPASI is freely available for academic use at http://www.copasi.org.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Programas Informáticos / Biología de Sistemas / Modelos Biológicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Biosystems Año: 2012 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Irlanda

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Programas Informáticos / Biología de Sistemas / Modelos Biológicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Biosystems Año: 2012 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Irlanda