Imperial College London
Portrait Picture

Professor Cleo Kontoravdi

Faculty of Engineering, Department of Chemical Engineering

Professor of Biological Systems Engineering
 
 
 
//

Contact

 

+44 (0)20 7594 6655cleo.kontoravdi98 Website

 
 
//

Location

 

310ACE ExtensionSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Kotidis:2019:10.3390/pr7030174,
author = {Kotidis, P and Kontoravdi, C},
doi = {10.3390/pr7030174},
journal = {Processes},
title = {Strategic framework for parameterization of cell culture models},
url = {http://dx.doi.org/10.3390/pr7030174},
volume = {7},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development.
AU  - Kotidis,P
AU  - Kontoravdi,C
DO  - 10.3390/pr7030174
PY  - 2019///
SN  - 2227-9717
TI  - Strategic framework for parameterization of cell culture models
T2  - Processes
UR  - http://dx.doi.org/10.3390/pr7030174
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000464443100003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/69676
VL  - 7
ER  -
Download