Benchmarks for identification of ordinary differential equations from time series data.

ABSTRACT:

Motivation

In recent years, the biological literature has seen a significant increase of reported methods for identifying both structure and parameters of ordinary differential equations (ODEs) from time series data. A natural way to evaluate the performance of such methods is to try them on a sufficient number of realistic test cases. However, weak practices in specifying identification problems and lack of commonly accepted benchmark problems makes it difficult to evaluate and compare different methods.

Results

To enable better evaluation and comparisons between different methods, we propose how to specify identification problems as optimization problems with a model space of allowed reactions (e.g. reaction kinetics like Michaelis-Menten or S-systems), ranges for the para

SUBMITTER: Gennemark P 

PROVIDER: S-EPMC2654804 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature