Joint State and Parameter Estimation For Biochemical Dynamic Pathways With Iterative Extended Kalman Filter: Comparison With Dual State and Parameter Estimation

Abstract

A biochemical dynamic pathway is usually modeled as a nonlinear system described by a set of nonlinear ODEs. In most cases, only partial states can be measured. Moreover, the system parameters, reaction rates, may be unknown or poorly known. Therefore, it is of significance to estimate the states and parameters, for analyzing the biochemical dynamic pathway. Due to the limitation of some traditional parameter estimation approaches, it is natural to choose sequential methods such as extended Kalman filter to do the parameter estimation for biochemical dynamic pathways. In this paper, dual/joint state and parameter estimation with iterative extended Kalman filter (EKF) are investigated to obtain state and parameter estimates for a biochemical pathway simultaneously. The simulated results between two methods are compared to show the validity of parameter estimation for a biochemical dynamic pathway. It has shown that, for the nonlinear biochemical system, the joint state and parameter estimation with EKF, can give desirable convergence and estimation performance.