Metaheuristic Optimization Methods for the Parameter Estimation of Nonlinear Dynamic Biological Systems

摘要

Developing suitable dynamic models of biochemical pathways is a key issue in systems biology. This paper considers the problem of parameter estimation in nonlinear dynamic models of biological systems. Due to inherent characteristics of the systems, many traditional methods failed. As a result, we applied metaheuristic methods to improve the methodology. In the optimization area, there are two main metaheuristic optimization methods which are Ant Colony System (ACS) and Particle Swarm Optimization (PSO). Our work is to use PSO to solve the global optimization problem of DAE constraints in reduced computational cost. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The metaheuristic algorithms are aggregates of concepts which can define large scale heuristic methods for different problems. It only needs relative few modifications and can be applied in different optimization problems. The metaheuristic method presented has advantages in ensuring the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values.