Abstract：The studies of biomedicine show that the expression level of p53 (tumor suppressor gene) is related with the tumor forming. Researches of the p53 related signal transduction networks would provide new ideas for revealing the pathogenesis of cancer or tumor and looking for the treatment method. As the p53-Mdm2 negative feedback network plays core roles, the study of the p53-Mdm2 regulation net work is of great significance. In this paper, based on the gene expression time series data of human leukemia cells after ionizing radiation, the nonlinear dynamic continuous random biological networks with time delay were established, then continuous-discrete extended Kalman filter (EKF) algorithm was used to simulate the dynamic regulation of p53 and Mdm2. Meanwhile, the accuracy of the model established in this paper was validated and we found that the error rate of the model was only 0.85%. Results showed that the algorithm thatis convergent could predict gene expression level at any time and simulate accurately the damped oscillation process of p53-Mdm2 regulatory networks after ionizing radiation. The response process was consistent with biological experiment results. The algorithm proposed in this paper provided an effective method for biological experiment modeling, and supplied the foundation for the research of system dynamics.
姜 莉, 李玉榕. 基于连续离散扩展卡尔曼建模的p53-Mdm2调控关系研究[J]. 中国生物医学工程学报, 2017, 36(2): 180-186.
Jiang Li, Li Yurong. Research of p53-Mdm2 Regulatory Networks Based on Modeling by Continuous-Discrete Extended Kalman Filter. Chinese Journal of Biomedical Engineering, 2017, 36(2): 180-186.
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