A kinetically constrained dynamic flux balance analysis model predicts diverse CHO-cell culture process modes and...

Bioreactor process conditions have a significant effect on Chinese Hamster Ovary (CHO) cell metabolism. However, there exists very limited literature on incorporating process conditions in mathematical models of CHO cell metabolism. To address this limitation, guided by recently published experimental data, we have curated a compact stoichiometric network, including 19 canonical amino acids, and formulated phenotype-driven kinetic expressions to develop a kinetically constrained dynamic flux balance analysis (dFBA) model. The dFBA model incorporates Critical Process Parameters (CPPs), notably bioreactor pH, basal and feed media nutrient composition, feeding times, and inoculation cell densities to predict metrics of bioreactor performance: cell growth rates, antibody titers, and nutrient and metabolite profiles. The dFBA model was trained on diverse fed-batch data of the CHO VRC01 cell line to regress the kinetic parameters. The model's utility was demonstrated through experimentally validated model predictions of CHO-cell performance in intensified fed-batch cultures, perfusion cultures, and cultures with different media. Experimentally validated predictions of a culture with high initial cell density and increased feed addition (intensified fed-batch culture) showed that mAb titers similar to fed-batch culture can be achieved with shorter culture duration. Similarly, experimentally validated predictions of perfusion bioreactor performance showed that coupling historical fed-batch data with computational tools can be leveraged to predict continuous biomanufacturing performance. We thus demonstrate that the developed mathematical model can simulate culture performance across multiple operating modes and process conditions beyond those used for parameter regression for the CHO VRC01 cell line used in this study.