iGFA: Improved Glycosylation Flux Analysis

In biopharmaceutical manufacturing of monoclonal antibodies (mAbs), asparagine (N)-linked glycosylation profile of these proteins is a critical quality attribute. We introduce improved Glycosylation Flux Analysis (iGFA), enhancing our previous GFA by: (1) reformulating constraint-based modeling using enzymatic kinetics to obtain biologically interpretable factors; and (2) implementing the analysis using Python's Pyomo modeling language, which not only reduces computational costs significantly compared to the MATLAB-based GFA, but also makes the iGFA an open-source package. When applied to data from Chinese Hamster Ovary (CHO) cell culture production of mAb under varying pH conditions, the analysis revealed both common and distinct dynamic trends across different pH. We identified galactosylation as the most impacted glycosylation processing by pH. Further, the estimated enzyme-related factors correlated more strongly with gene expression levels than with nucleotide sugar availability, suggesting that glycosylation regulation is predominantly controlled at the transcriptional and/or translational level. Overall, the iGFA is a powerful tool for analyzing glycosylation dynamics in mAb production.