Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kB signaling pathway

Tools

Yue, Hong and Brown, Martin and Knowles, Joshua and Wang, Hong and Broomhead, David S. and Kell, Douglas B. (2006) Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kB signaling pathway. Molecular BioSystems, 2 (12). pp. 640-649. ISSN 1742-206X (https://doi.org/10.1039/b609442b)

[thumbnail of Mol_Bio_journal_print.pdf]
Preview
 PDF. Filename: Mol_Bio_journal_print.pdf
Final Published Version
Download (688kB)| Preview

Abstract

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 IB-NF-B 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-B 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.

ORCID iDs

Yue, Hong ORCID logoORCID: https://orcid.org/0000-0003-2072-6223, Brown, Martin, Knowles, Joshua, Wang, Hong, Broomhead, David S. and Kell, Douglas B.;
CORE (COnnecting REpositories)