Developing computational models for engineering glycosylation of biologics

Biologics are the most rapidly growing class of pharmaceuticals, with over 50% of the highest earning blockbuster drugs falling into this category. Glycosylation critically influences the function of most biologics. Yet the non-templated nature of glycan biosynthesis, which generates inherently heterogeneous glycosylation, prevents precise control of glycan biosynthesis in mammalian cells. This causes batch-to-batch variability, translating into safety concerns that ultimately generate a huge burden of cost and effort for companies that need a reliable source for their products. To better understand how the processing of glycans is governed by different distributions of the biosynthetic enzymes in the Golgi apparatus, we have recently developed and validated a computational model. This model uses experimentally-determined glycan profiles to establish the relative localization and activity of biosynthetic enzymes. This allows investigation, in mechanistic detail, of how alterations in the glycosylation machinery influence the glycan profile of cells. This PhD project, run in collaboration with the Biopharm Process Research division of GSK, will extend this model to investigate the relationship between the organization of the glycosylation machinery and the glycosylation of key biologics. The student will conduct both cutting edge computational modelling and wet lab experiments in an iterative fashion: experimental results will inform development of the model, while predictions of the model will be validated experimentally. Methods to be used include: stochastic modelling, approximate Bayesian computation, computer programming (Java or Python preferred), mass spectrometric glycan profiling, tissue culture, fluorescence microscopy, molecular cloning.