Parameter estimation in dynamic biochemical systems based on adaptive Particle Swarm Optimization

We consider the problem of large-scale parameter estimations in nonlinear dynamic models of biochemical systems. In this work, the particle swarm optimization (PSO) method is adapted for estimation of model parameters in highly nonlinear, large-scale metabolic networks in systems biology. PSO is a recently developed novel metaheuristic optimization method. And with the modification of the essential parameters to a nonlinear changing strategy, the convergence speed of the proposed adaptive PSO has been accelerated. This project also describes the comparisons of different optimization methods' performances to understand how PSO may provide the best results.