| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 724062 | IFAC Proceedings Volumes | 2007 | 6 Pages |
Mathematical models of complex biological systems often consist of sets of ordinary differential equations which depend on several non measurable parameters that must be estimated by fitting the model to experimental data. However this fitting can be only accomplished for the cases that practical identifiability may be guaranteed.This work proposes an iterative optimal experimental design procedure, consisting of three main steps (identifiability analysis, ranking of parameters and the design of optimal dynamic experiments), so as to maximize identifiability, that is the ratio quantity/quality of information for model calibration. The applicability and advantages of using such procedure are illustrated by considering an example related to the modelling of a cell signaling cascade.
