LAPSE:2018.0404
Published Article
LAPSE:2018.0404
Sequential Parameter Estimation for Mammalian Cell Model Based on In Silico Design of Experiments
Zhenyu Wang, Hana Sheikh, Kyongbum Lee, Christos Georgakis
August 28, 2018
Due to the complicated metabolism of mammalian cells, the corresponding dynamic mathematical models usually consist of large sets of differential and algebraic equations with a large number of parameters to be estimated. On the other hand, the measured data for estimating the model parameters are limited. Consequently, the parameter estimates may converge to a local minimum far from the optimal ones, especially when the initial guesses of the parameter values are poor. The methodology presented in this paper provides a systematic way for estimating parameters sequentially that generates better initial guesses for parameter estimation and improves the accuracy of the obtained metabolic model. The model parameters are first classified into four subsets of decreasing importance, based on the sensitivity of the model’s predictions on the parameters’ assumed values. The parameters in the most sensitive subset, typically a small fraction of the total, are estimated first. When estimating the remaining parameters with next most sensitive subset, the subsets of parameters with higher sensitivities are estimated again using their previously obtained optimal values as the initial guesses. The power of this sequential estimation approach is illustrated through a case study on the estimation of parameters in a dynamic model of CHO cell metabolism in fed-batch culture. We show that the sequential parameter estimation approach improves model accuracy and that using limited data to estimate low-sensitivity parameters can worsen model performance.
Keywords
Design of Experiments, Mammalian Cell Culture, parameter estimation, Pharmaceutical Processes, sensitivity analysis
Subject
Suggested Citation
Wang Z, Sheikh H, Lee K, Georgakis C. Sequential Parameter Estimation for Mammalian Cell Model Based on In Silico Design of Experiments. (2018). LAPSE:2018.0404
Author Affiliations
Wang Z: Department of Chemical and Biological Engineering and Systems Research Institute for Chemical and Biological Processes Tufts University, Medford, MA 02155, USA
Sheikh H: Department of Chemical and Biological Engineering and Systems Research Institute for Chemical and Biological Processes Tufts University, Medford, MA 02155, USA
Lee K: Department of Chemical and Biological Engineering and Systems Research Institute for Chemical and Biological Processes Tufts University, Medford, MA 02155, USA
Georgakis C: Department of Chemical and Biological Engineering and Systems Research Institute for Chemical and Biological Processes Tufts University, Medford, MA 02155, USA
[Login] to see author email addresses.
Journal Name
Processes
Volume
6
Issue
8
Article Number
E100
Year
2018
Publication Date
2018-07-24
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr6080100, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2018.0404
This Record
External Link

doi:10.3390/pr6080100
Publisher Version
Download
Files
[Download 1v1.pdf] (1.1 MB)
Aug 28, 2018
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
665
Version History
[v1] (Original Submission)
Aug 28, 2018
 
Verified by curator on
Aug 28, 2018
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2018.0404
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version