Hierarchical optimization for the efficient parametrization of ODE models.

ABSTRACT:

Motivation

Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems.

Results

In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimizat

SUBMITTER: Loos C 

PROVIDER: S-EPMC6289139 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature