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Global sensitivity analysis challenges in biological systems modeling / A. Kiparissides in Industrial & engineering chemistry research, Vol. 48 N° 15 (Août 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 15 (Août 2009) . - pp. 7168–7180 Titre : Global sensitivity analysis challenges in biological systems modeling Type de document : texte imprimé Auteurs : A. Kiparissides, Auteur ; S. S. Kucherenko, Auteur ; A. Mantalaris, Auteur Année de publication : 2009 Article en page(s) : pp. 7168–7180 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Biological  systems  modeling  Sensitivity  analysis  techniques Résumé : Mammalian cell culture systems produce high-value biologics, such as monoclonal antibodies, which are increasingly being used clinically. A complete framework that interlinks model-based design of experiments (DOE) and model-based control and optimization to the actual industrial bioprocess could assist experimentation, hence reducing costs. However, high fidelity models have the inherent characteristic of containing a large number of parameters, which is further complicated by limitations in the current analytical techniques, thus resulting in the experimental validation of merely a small number of parameters. Sensitivity analysis techniques can provide valuable insight into model characteristics. Traditionally, the application of sensitivity analysis on models of biological systems has been treated more or less as a black box operation. In the present work, we elucidate the aspects of sensitivity analysis and identify, with reasoning, the most suitable group of sensitivity analysis methods for application to highly nonlinear dynamic models in the context of biological systems. Specifically, we perform computational experiments on antibody-producing mammalian cell culture models of different complexities and identify, as well as address, problems associated with such “real life” models. In conclusion, a novel global screening method (derivative based global sensitivity measures, DGSM) is proven to be the most time-efficient and robust alternative to the established variance-based Monte Carlo methods. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900139x [article] Global sensitivity analysis challenges in biological systems modeling [texte imprimé] / A. Kiparissides, Auteur ; S. S. Kucherenko, Auteur ; A. Mantalaris, Auteur . - 2009 . - pp. 7168–7180. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 15 (Août 2009) . - pp. 7168–7180 Mots-clés : Biological  systems  modeling  Sensitivity  analysis  techniques Résumé : Mammalian cell culture systems produce high-value biologics, such as monoclonal antibodies, which are increasingly being used clinically. A complete framework that interlinks model-based design of experiments (DOE) and model-based control and optimization to the actual industrial bioprocess could assist experimentation, hence reducing costs. However, high fidelity models have the inherent characteristic of containing a large number of parameters, which is further complicated by limitations in the current analytical techniques, thus resulting in the experimental validation of merely a small number of parameters. Sensitivity analysis techniques can provide valuable insight into model characteristics. Traditionally, the application of sensitivity analysis on models of biological systems has been treated more or less as a black box operation. In the present work, we elucidate the aspects of sensitivity analysis and identify, with reasoning, the most suitable group of sensitivity analysis methods for application to highly nonlinear dynamic models in the context of biological systems. Specifically, we perform computational experiments on antibody-producing mammalian cell culture models of different complexities and identify, as well as address, problems associated with such “real life” models. In conclusion, a novel global screening method (derivative based global sensitivity measures, DGSM) is proven to be the most time-efficient and robust alternative to the established variance-based Monte Carlo methods. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900139x