Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Industriell bioteknik -- Biokatalys och enzymteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Industrial Biotechnology -- Biocatalysis and Enzyme Technology (hsv//eng)

Nyckelord

biocatalysis

curvature

Fisher Information Matrix

robust model-based optimal experimental design

ω-transaminase

Publikations- och innehållstyp

art (ämneskategori)

ref (ämneskategori)