Parameter Estimation of Signal Transduction Pathways Using Probability Density Function of Measurement

Author

Liu, Taiyuan ; Jia, Jianfang ; Wang, Hong ; Yue, Hong

Author_Institution

Inst. of Autom., Chinese Acad. of Sci. Beijing, Beijing

fYear

2007

fDate

6-8 July 2007

Firstpage

456

Lastpage

459

Abstract

Parameter estimation of signal transduction pathway models is a challenging task as such models are normally nonlinear, high dimensional, and the measurement data is limited and corrupted by noise. In this paper, a novel method for parameter estimation is proposed, in which the distance between the probability density function (PDF) of the model output and the PDF of the measurement data is minimized. This method has been applied to estimate unknown parameters of a TNFalpha- mediated NF-kappaB signal transduction pathway model. The simulation results show the effectiveness of this new algorithm.

Keywords

neural nets; neurophysiology; parameter estimation; TNFalpha-mediated NF-kappaB signal transduction pathway model; algorithm; noise; parameter estimation; probability density function; Automation; Control systems; Density measurement; Electric variables measurement; Mathematical model; Noise measurement; Parameter estimation; Predictive models; Probability density function; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on

Conference_Location

Wuhan

Print_ISBN

1-4244-1120-3

Type

conf

DOI

10.1109/ICBBE.2007.120

Filename

4272604