2024/08/30 by Yingjie Ma, Jing Guo, Ma, Yingjie +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · #FOS: Mathematics #Glycosylation and Glycoproteins Research #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Protein purification and stability #Viral Infectious Diseases and Gene Expression in Insects
paper · pdf · doi:10.48550/arxiv.2409.00281
openalex publication_date 2024/08/30 · openalex created_date 2024/09/29 · openalex updated_date 2026/07/30
Glycosylation is a critical quality attribute for monoclonal antibody (mAb) production, influenced by both process conditions and cellular mechanisms. Multiscale mechanistic models, spanning from the bioreactor to the Golgi apparatus, have been proposed for analyzing the glycosylation process. However, these models are computationally intensive to solve when using traditional methods, making optimization and control challenging. In this work, we propose a quasi-steady-state (QSS) approach for efficiently solving the multiscale glycosylation model. By introducing the QSS assumption and assuming negligible nucleotide sugar donor (NSD) flux for glycosylation in the Golgi, the large-scale partial differential algebraic equation system is converted into a series of independent differential algebraic equation systems. Based on that representation, we develop a three-step QSS simulation method and further reduce computational time through parallel computing and nonuniform time grid strategies. Case studies in simulation, parameter estimation, and dynamic optimization demonstrate that the QSS approach can be more than 300-fold faster than the method of lines, with less than 1.6% relative errors. This work establishes a solid foundation for multiscale model-based optimization and control of the glycosylation process, supporting the implementation of quality by design.