Document Detail


Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kappaB signalling pathway.
MedLine Citation:
PMID:  17216045     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Mathematical modelling offers a variety of useful techniques to help in understanding the intrinsic behaviour of complex signal transduction networks. From the system engineering point of view, the dynamics of metabolic and signal transduction models can always be described by nonlinear ordinary differential equations (ODEs) following mass balance principles. Based on the state-space formulation, many methods from the area of automatic control can conveniently be applied to the modelling, analysis and design of cell networks. In the present study, dynamic sensitivity analysis is performed on a model of the IkappaB-NF-kappaB signal pathway system. Univariate analysis of the Euclidean-form overall sensitivities shows that only 8 out of the 64 parameters in the model have major influence on the nuclear NF-kappaB oscillations. The sensitivity matrix is then used to address correlation analysis, identifiability assessment and measurement set selection within the framework of least squares estimation and multivariate analysis. It is shown that certain pairs of parameters are exactly or highly correlated to each other in terms of their effects on the measured variables. The experimental design strategy provides guidance on which proteins should best be considered for measurement such that the unknown parameters can be estimated with the best statistical precision. The whole analysis scheme we describe provides efficient parameter estimation techniques for complex cell networks.
Authors:
Hong Yue; Martin Brown; Joshua Knowles; Hong Wang; David S Broomhead; Douglas B Kell
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2006-10-23
Journal Detail:
Title:  Molecular bioSystems     Volume:  2     ISSN:  1742-206X     ISO Abbreviation:  Mol Biosyst     Publication Date:  2006 Dec 
Date Detail:
Created Date:  2007-01-11     Completed Date:  2007-03-01     Revised Date:  2009-11-19    
Medline Journal Info:
Nlm Unique ID:  101251620     Medline TA:  Mol Biosyst     Country:  England    
Other Details:
Languages:  eng     Pagination:  640-9     Citation Subset:  IM    
Affiliation:
School of Chemistry, University of Manchester, Sackville St, Manchester, UK. h.yue@manchester.ac.uk
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MeSH Terms
Descriptor/Qualifier:
I-kappa B Kinase / metabolism
Least-Squares Analysis
Mathematics
Models, Biological*
Multivariate Analysis
NF-kappa B / metabolism*
Signal Transduction*
Chemical
Reg. No./Substance:
0/NF-kappa B; EC 2.7.11.10/I-kappa B Kinase

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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