The mathematical model of cell signal transduction networks is highly complex and strongly nonlinear since it includes a lot of dynamic parameters and reaction species.Due to the impact of the noise

the experimental data are incomplete and uncertain.The extended Kalman filter method is presented for parameters identification of signal transduction networks model.The unknown parameters are improved iteratively as the state vector of a random process so that the Kalman filter equations can be formulated to perform parameter identification.Using TNFα induced NF-κB signal transduction network as an example

this method was applied to estimate the unknown parameters of the model.The simulation results demonstrate that the algorithm can well estimate the unknown parameters under the disturbing of the noise and it provides an efficient theoretical tool for solving the parameters' uncertainty effects of biological pathways.