Biochemical systems identification by a random drift particle swarm optimization approach.

ABSTRACT:

Background

Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem.

Results

This paper presents an effective search str

SUBMITTER: Sun J 

PROVIDER: S-EPMC4158603 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature