Global sensitivity analysis for a perfusion bioreactor based on CFD modelling. (July 2022)

• Record Type: Journal Article
• Authors: Nașcu, Ioana; Sebastia-Saez, Daniel; Chen, Tao; Nașcu, Ioan; Du, Wenli
• Is Part Of: Computers & chemical engineering. Volume 163(2022)
• Publication Date: 2022-07
• Subjects: Tissue engineering -- Perfusion bioreactor -- CFD modeling -- High-dimensional model representation -- Global sensitivity analysis -- Relative gain array; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2022.107829
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A review and comparison of mathematical models for bioreactors. Development and implementation of a mathematical model for a perfusion bioreactor. Global sensitivity analysis is performed, and the results are analyzed for two different scenarios. A relative gain analysis is performed and interpreted. Abstract: Perfusion bioreactors are important tools in tissue engineering that are used for cell cultivation. Unfortunately these types of processes are not yet fully understood in literature and information about the model is scarce. Furthermore, mathematical models that are used for perfusion bioreactors have posed significant challenges. This work presents a concise overview and analysis of mathematical models for a perfusion bioreactor process. The comprehensive mathematical model of convection and diffusion in a perfusion bioreactor, combined with cell growth kinetics, is developed using Computational Fluid Dynamics. The model describes the spatio-temporal evolution of glucose concentration, oxygen concentration, lactate concentration and cell density within a polymeric scaffold. For an in-depth understanding of this type of processes, global sensitivity analysis and simulations is performed using the method of high-dimensional model representation (RS-HDMR). A quantitative analysis of the complex kinetic mechanisms using recently developed advanced mathematical approaches to global sensitivity and uncertainty analysis through RS-HDMR can be exploited to investigate the important features of the perfusion bioreactor process as well as possible factors underlying qualitative discrepancies. Moreover, for a further understanding of the process, a relative gain analysis is performed. The results will help us gain an in depth understanding of the process and will be used as the foundation for advanced control algorithms that will facilitate manufacturing for any type of cell culture using a continuous perfusion bioreactor thus paving the way towards Industry 4.0.