Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.

ABSTRACT:

Background

Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems.

Results

In this manuscript, we propose two new methods for solving optimization problems with steady

SUBMITTER: Fiedler A 

PROVIDER: S-EPMC4994295 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature