Abstract
Glycosylation is one of the most common post-translational modifications of proteins, it has significant influence on stability, biological activity and clearance rate of protein therapeutics. However, glycoprotein production usually leads to a complex mixture of glycoforms, with variations in both the sites of glycosylation and oligosaccharide structures. This wide range of diversity in the resulting glycoproteins involving a large variety of glycosylation enzymes included in this process pose a challenging task in an attempt to simulate those processes in silico. Agent-based modeling (ABM) has emerged as an effective tool for simulating complex behaviour, especially in pattern formation and self-organizing systems. For glycosylation, ABM allows a detailed and dynamic representation of individual agents and their interactions within biological entities, making it possible to simulate glycan pattern formation. An available in silico representation of protein glycosylation by an ABM (Jetschni and Götz, 2023) captures the stochastic nature of the involved molecular diffusion, convection and biochemical reaction steps within the Golgi apparatus. Model verification towards reproducibility and statistical analysis of pattern formation shows, that the modeling approach is robust and suitable to predict the outcome of protein glycosylation.
