Options
Applying mechanistic models in bioprocess development
Publikationstyp
Journal Article
Date Issued
2013-08-30
Sprache
English
Author(s)
Fernandes, Rita Lencastre
Bodla, Vijaya Krishna
Carlquist, Magnus
Lantz, Anna Eliasson
Volume
132
Start Page
137
End Page
166
Citation
Advances in Biochemical Engineering/Biotechnology 132: 137-166 (2013-08-30)
Publisher DOI
Scopus ID
PubMed ID
23307292
Publisher
Springer
The available knowledge on the mechanisms of a bioprocess system is central to process analytical technology. In this respect, mechanistic modeling has gained renewed attention, since a mechanistic model can provide an excellent summary of available process knowledge. Such a model therefore incorporates process-relevant input (critical process variables)-output (product concentration and product quality attributes) relations. The model therefore has great value in planning experiments, or in determining which critical process variables need to be monitored and controlled tightly. Mechanistic models should be combined with proper model analysis tools, such as uncertainty and sensitivity analysis. When assuming distributed inputs, the resulting uncertainty in the model outputs can be decomposed using sensitivity analysis to determine which input parameters are responsible for the major part of the output uncertainty. Such information can be used as guidance for experimental work; i.e., only parameters with a significant influence on model outputs need to be determined experimentally. The use of mechanistic models and model analysis tools is demonstrated in this chapter. As a practical case study, experimental data from Saccharomyces cerevisiae fermentations are used. The data are described with the well-known model of Sonnleitner and Käppeli (Biotechnol Bioeng 28:927-937, 1986) and the model is analyzed further. The methods used are generic, and can be transferred easily to other, more complex case studies as well.
Subjects
Fermentation
Identifiability
Modeling
Monte Carlo
PAT
Saccharomyces cerevisiae
Sensitivity
Uncertainty
DDC Class
570: Life Sciences, Biology