The mathematical model of cell signal transduction networks is highly nonlinear and involves a large number of variables and kinetics parameters due to its complex structure and enormous size. Recently, it has been one of the major problems to analyze the dynamic characteristics of cell signal transduction networks in the research fields of systems biology. Using NF-κB signaling pathway as an example, system analysis, optimal experimental design and model reduction have been done in this thesis and some marked progresses have been gained which will play important roles in practical use. Main jobs and contributions involved in this thesis are summarized as follows. 1 The state space model of NF-κB signal transduction networks is formed according to the system mechanism and the kinetics parameters. Local stability of the system and the robustness of the system output with respect to parameter variations are investigated. 2 For NF-κB signal transduction networks, the direct differential method (DDM) is utilized to analyze the impact of parameters with small disturbance on the system outputs. The sensitivity matrix is then used to address correlation analysis and identifiability assessment of parameters. 3 Global sensitivity analysis of system output with respect to parameter variations is studied using Latin hypercube sampling method and Morris method. The global sensitivity of parameters is computed and the critical reactions of cell signaling pathway can be found. 4 Optimal experimental design for the initial concentration of input signal IKK of NF-κB signaling pathway is studied through various criterions. The confidence interval of parameters is analyzed and optimal experiment for parameter estimation is proposed. 5 A model reduction strategy via hybrid inference method is proposed for signal transducion networks. The approach synthesizes metabolic control analysis, sensitivity analysis, principal component analysis, and flux analysis to reduce the dimensions of the model and to decrease the number of the biological reactions.
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