Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models.
Biotechnol Prog
; 33(5): 1278-1293, 2017 Sep.
Article
in En
| MEDLINE
| ID: mdl-28675693
ABSTRACT
The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 331278-1293, 2017.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Research Design
/
Biotechnology
/
Models, Biological
Type of study:
Prognostic_studies
Language:
En
Journal:
Biotechnol Prog
Journal subject:
BIOTECNOLOGIA
Year:
2017
Document type:
Article
Affiliation country: