Integrated modeling to capture the interaction of physiology and fluid dynamics in biopharmaceutical bioreactors. (2nd February 2017)

• Record Type: Journal Article
• Authors: Farzan, Parham; Ierapetritou, Marianthi G.
• Is Part Of: Computers & chemical engineering. Volume 97(2017)
• Page Start: 271
• Page End: 282
• Publication Date: 2017-02-02
• Subjects: Bioreactor modeling -- CFD -- Unstructured metabolic model -- Integrated modeling -- Dynamic process simulation; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.11.037
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Heterogeneities in environment and cell population are captured. Gas-liquid mass transfer and carbon dioxide dissolution kineticsare considered. The response of cells to environment is predicted using unstructured metabolic modeling. The CFD simulation and metabolic model are integrated using literature data. Bioreactor performance is expressed as a function of time and process parameters. Abstract: The performance of a bioreactor is sensitive to local gradients of chemical and physical stimuli. Thus, this work presents a model, which captures spatial heterogeneity and interactions of biotic and abiotic phases in animal cell cultures. A computational fluid dynamics simulation that includes gas-liquid mass transfer and kinetics of carbon dioxide dissolution is developed to capture the variations of environmental parameters. Unstructured modeling is implemented to integrate growth, viability and productivity of cells. While predictive accuracy is valuable, it is important to balance it with computational feasibility. In this work, evolutions of hydrodynamics and cell population are obtained sequentially. The outcome is a deterministic model with extended integration between physical and biological phenomena which is computationally tractable. The model calculates the bioreactor performance as a function of time and process parameters such as impeller rotation speed and gas sparging flow rate, which makes it useful for bioprocess design and scheduling.