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Improving data fitting of a signal transduction model by global sensitivity analysis

Jin, Y. and Yue, H. and Brown, M. and Liang, Y. and Kell, D.B. (2007) Improving data fitting of a signal transduction model by global sensitivity analysis. In: Proceedings of the 2007 American Control Conference. UNSPECIFIED, pp. 2708-2713.

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Based on a simplified model of the (TNF-α mediated) IκBα-NF-κB signal transduction pathway, global sensitivity analysis has been performed to identify those parameters that exert significant control on the system outputs. The permutation operation in Morris method is modified to work for log-uniform sampling parameters. The identified sensitive parameters are then estimated using multivariable search such that the output of the model matches experimental data representing the nuclear concentration of NF-κB. Such parameter tuning leads to much better agreement between the model and the experimental time series relative to those previously published. This shows the importance of global sensitivity analysis in Systems Biology models