Systematic development of predictive mathematical models for animal cell cultures
File(s)
Accepted version
Author(s)
Kontoravdi, Cleo
Pistikopoulos, Efstratios N
Mantalaris, Athanasios
Type
Journal Article
Abstract
Fed-batch cultures are used in producing monoclonal antibodies industrially. Existing protocols are developed empirically. Model-based tools aiming to improve productivity are useful with model reliability and computational demand being important. Herein, a systematic framework for developing predictive models is presented comprising of model development, global sensitivity analysis, optimal experimental design for parameter estimation, and predictive capability checking. Its efficacy and validity are demonstrated using a fed-batch structured/unstructured model of antibody-secreting hybridoma cultures. Global sensitivity analysis is first used to identify sensitive model parameters (initial values estimated from batch cultures). Information-rich data from an optimally designed fed-batch experiment are then used to estimate these parameters, resulting in good agreement between simulation and experimental results. Finally, the model's predictive capability is confirmed by comparison with an independent set of fed-batch cultures. This approach systematises the process of developing predictive cell culture models at a minimum experimental cost, enabling model-based control and optimisation.
Date Issued
2010-08-09
Citation
Computers & Chemical Engineering, 2010, 34 (8), pp.1192-1198
ISSN
0098-1354
Publisher
Elsevier
Start Page
1192
End Page
1198
Journal / Book Title
Computers & Chemical Engineering
Volume
34
Issue
8
Copyright Statement
Copyright © 2010 Elsevier Ltd. All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Chemical Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Chemical Engineering, 34(8), 2010. DOI:10.1016/j.compchemeng.2010.03.012
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000279658700003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Publication Status
Published