关键词: 基因调控网络, 参数学习, 常微分方程, 非线性, 无迹粒子滤波

Abstract: The recent researches on estimation of parameters on Gene Regulatory Networks（GRN） by differential equations are generally based on Kalman Filtering Model（KFM）.It makes assumptions that the system analyzed is linear.However，GRN is obviously non-linear system，so great deviation error will happen.Here a method is presented to estimate the parameters and hidden variables of GRN based on Unscented Particle Filtering（UPF）.It makes better fitness than KFM due to free of the premise that the model is linear.By comparison of the estimation of the hidden variables and parameters of Repressilator between UPF and Unscented Kalman Filter（UKF），advantage of this method on reduction of estimation error is validated.The effect of the amount of particles on result is simultaneously analyzed.UPF is more accurate than UKF in estimating the parameters of GRN.Deficiency or overabundance particles both will weaken accuracy of estimation，so selection on the quantity of particles is significant.

Key words: gene regulatory network, parameters learning, ordinary differential equations, non-linear, unscented particle filtering