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.