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

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.