Media formulation optimization based on multi-scale modeling of heterogeniety in mammalian cell culture process

Conference Dates

May 6-11, 2018


A multi-scale cell culture model is developed to identify critical media components that impact cell growth cycle and thus predicts productivity. Laboratory based media optimization requires an extensive DOE whereas models can perform the same optimization with less experimentation saving resources. Conventionally, cell culture models are categorized into intracellular or extracellular. Intracellular models are average cell models capturing metabolic pathway reactions, this means they must assume all cells in the culture are following the same metabolic pathway. Extracellular models use population balance models (PBM) to account for cell cycle propagation, however, they cannot explicitly consider intracellular metabolism. Here, a multi-scale modeling approach is adopted to unify the understanding of intracellular metabolism and the probabilistic nature of cell heterogeneity due to the cell cycle. The culture dynamics are described using an unstructured model encompassing cell growth, cell death, nutrient consumption, metabolite, and protein production and their dependency on media composition. A one-dimensional, volume-based PBM is formulated for three identifiable cell cycle phases G1/G0, S, and G2/M simultaneously in the culture. The cell metabolism for each cell cycle phase is modeled differently to account for the cell heterogeneity and cycle specific intracellular activities. By the cell-cycle phase specific metabolism, the cell growth and cell density in the high productivity cycle phase could be controlled by media composition. Future work will involve the collection of data from AMBIC (Advanced Mammalian Biomanufacturing Innovation Center) cell lines in order to parameterize and validate the developed model. This work fits well within the “Computational strategies to enhance bioprocess performance: From systems biology to process modeling” session, specifically looking at mathematical modeling of bioprocess upstream.

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