Picture of scraped petri dish

Scrape below the surface of Strathprints...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Explore world class Open Access research by researchers at Strathclyde, a leading technological university.


Model reduction and parameter sensitivity analysis of the TNFα-induced NF-κB signal transduction networks

Jia, J.F. and Liu, T.Y. and Yue, H. and Wang, H. (2007) Model reduction and parameter sensitivity analysis of the TNFα-induced NF-κB signal transduction networks. Journal of the Graduate School of the Chinese Academy of Sciences, 2007 (2). pp. 207-216.

Full text not available in this repository. (Request a copy from the Strathclyde author)


In order to study the impact of inner structure of biological systems and variations of correlative parameters on nuclear transcription fator-κappa B(NF-κB)signal transduction networks,it is vital to make an sensitivity analysis of system parameters and to reduce the mathematical model of NF-κB signal transduction networks,which includes 26 state variables and 64 parameters.Based on the analysis of the mathematical model of the TNFα-Induced NFκB signal transduction networks,the IκB Kinase complex(IKK)was chosen as the step input signal of the system and NF-κB nuclear(NF-κBn)as the measurable output signal.The direct differential method(DDM)was utilized to analyse sensitivity coefficients of the oscillatory signal NF-κBn with respect to 64 parameters.Then,9 parameters,which are less sensitive to the system output signal,were removed form mathematic model of NF-κB signaling system so as to suitably reduce the complexity of the system model.The simulation results show that the output signal NF-κBn of the reduced model has much the same oscillatory characteristic as that of the former model.On the other hand,it also can be found that the rest output signals of both models are similar on the whole.Therefore,the parameters sensitivity analysis and model reduction results can give new insights to analyse biological data,to build mathematical model and to design particular experiments.