Sensitivity analysis and parameter estimation of signal transduction pathways model

Due to the high nonlinearity in system models, the large number of kinetics parameters involved, the inadequate measurement data in experiments and the noise pollution, etc., parameter estimation is therefore a challenging problem in systems biology. In this work, sensitivity analysis of model output with respect to model parameters is evaluated using Latin hypercube sampling method. Then, a new objective function is proposed based on the probability density function (PDF) of the system output, and particle swarm optimization is used to optimize the objective function through particles' cooperation and evolution. Taking NF-kappaB signal pathways model as an example, this method is applied to rank importance of parameters and to estimate the unknown sensitive parameters for complex signal transduction pathways model. The simulation results show the effectiveness of this new algorithm.