A New Particle Swarm Evolutionary Optimization for Parameter Estimation of Biological Models

The development of reliable biological models has become an important issue in systems biology. These models are constructed using differential algebraic equations to represent the dynamic perturbation of the biochemical quantities within the cells. However, these models heavily depend on the set of parameters that signify the physiology of the systems such as reaction rates and kinetic constants. These parameters are commonly difficult to be obtained using the experimental measurements. Due to the uncertainty of the measurements and the nonlinearity of the systems, advanced optimization methods are often necessary. In this paper, a new hybrid optimization method is introduced. The method, called Particle Swarm Evolutionary Optimization (PSEO), is proposed based on the combination of Particle Swarm Optimization (PSO) and Differential Evolution (DE) methods. The effectiveness of the proposed PSEO method on the parameter estimation problem is evaluated using two biological models, namely synthetic oscillator and microbial lactose operon models. The experimental results showed that the performances in term of better fitness value and computational speed of the proposed method have outperformed those produced by the existing methods like Particle Swarm Optimization (PSO), Differential Evolution (DE) and recently proposed hybrid Local Evolutionary PSO (LEPSO) methods.