生化网络模型参数估计的滤波技术

he mathematic model of a signal transduction networks can be used to analysis the system properties and give the future predict of the dynamic process. Thus, to effectively estimate model parameters is very important for developing appropriate models. It is still difficult to estimate the parameters of the system model because of laboratory constraints like observable players, sample size, noise level, and stimulation options. Inspired by the application in the control field, the extended Kalman filter technology was proposed to estimate the parameter of the biochemical networks in addition to the unobserved state variables. For this purpose, the TNF-α introduced NF-κB signal transduction pathway model is considered, and simulations show the effectiveness of filter technology.