(498d) Modeling CHO Cell Glycosylation Process Using Dynamic Kriging
AIChE Annual Meeting
Tuesday, November 17, 2020 - 8:45am to 9:00am
In this study, we build a dynamic kriging model to replace the first-principle model, which is used to capture the dynamic behavior of cell growth and simulate protein glycosylation with high computational efficiency. First, an unstructured kinetic model is built to capture the CHO cell culture process in fed-batch bioreactor and predict viable cell, glucose, lactate, and protein concentration. This model is coupled to a structured single-cell glycosylation model to determine the secreted glycoprotein fractions. Then a series of simulations are performed under different operating conditions based on a full factorial design, which provides inputs and outputs under all the time points to build the dynamic kriging surrogate model. By varying pH and the concentration of media supplements (MnCl2), the dynamic kriging model is able to capture time dependent responds of the system, including viable cell density, glucose, lactate concentrations and glycan fractions by solving the kriging iteratively at each time step [8, 10]. This model is used to find the optimal operating conditions for specific product specifications and integrate to a bioreactor model to achieve on-line product prediction and control.
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