Parameter estimation in biochemical systems models with alternating regression.

ABSTRACT:

Background

The estimation of parameter values continues to be the bottleneck of the computational analysis of biological systems. It is therefore necessary to develop improved methods that are effective, fast, and scalable.

Results

We show here that alternating regression (AR), applied to S-system models and combined with methods for decoupling systems of differential equations, provides a fast new tool for identifying parameter values from time series data. The key feature of AR is that it dissects the nonlinear inverse problem of estimating parameter values into iterative steps of linear regression. We show with several artificial examples that the method works well in many cases. In cases of no convergence, it is feasible to dedicate some computational effort to identifyi

SUBMITTER: Chou IC 

PROVIDER: S-EPMC1586003 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature