Applying Mechanistic Models in Bioprocess Development
(2013) In Advances in Biochemical Engineering, Biotechnology 132. p.137-166- Abstract
- 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... (More)
- 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 Kappeli (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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3987164
- author
- Fernandes, Rita Lencastre ; Bodla, Vijaya Krishna ; Carlquist, Magnus LU ; Heins, Anna-Lena ; Lantz, Anna Eliasson ; Sin, Guerkan and Gernaey, Krist V.
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Fermentation, Identifiability, Modeling, Monte Carlo, PAT, Saccharomyces, cerevisiae, Sensitivity, Uncertainty
- in
- Advances in Biochemical Engineering, Biotechnology
- volume
- 132
- pages
- 137 - 166
- publisher
- Springer
- external identifiers
-
- wos:000321113100007
- scopus:84882996157
- pmid:23307292
- ISSN
- 0724-6145
- DOI
- 10.1007/10_2012_166
- language
- English
- LU publication?
- yes
- id
- 3372b676-ade5-4f31-a7df-5ea7210e9cb7 (old id 3987164)
- date added to LUP
- 2016-04-01 14:35:02
- date last changed
- 2022-01-28 01:24:50
@article{3372b676-ade5-4f31-a7df-5ea7210e9cb7,
abstract = {{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 Kappeli (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.}},
author = {{Fernandes, Rita Lencastre and Bodla, Vijaya Krishna and Carlquist, Magnus and Heins, Anna-Lena and Lantz, Anna Eliasson and Sin, Guerkan and Gernaey, Krist V.}},
issn = {{0724-6145}},
keywords = {{Fermentation; Identifiability; Modeling; Monte Carlo; PAT; Saccharomyces; cerevisiae; Sensitivity; Uncertainty}},
language = {{eng}},
pages = {{137--166}},
publisher = {{Springer}},
series = {{Advances in Biochemical Engineering, Biotechnology}},
title = {{Applying Mechanistic Models in Bioprocess Development}},
url = {{http://dx.doi.org/10.1007/10_2012_166}},
doi = {{10.1007/10_2012_166}},
volume = {{132}},
year = {{2013}},
}