Monoclonal antibodies are complex biological products, and glycosylation and other critical quality attributes (CQAs) can significantly influence their efficacy, stability, and pharmacokinetics. These attributes are sensitive to the metabolic state of the production cell and bioprocess conditions, making their prediction and control an important challenge in biomanufacturing. This project will develop mechanistic and data-driven models to predict mAb glycosylation and CQAs from cellular metabolism and process variables.

Therapeutic proteins exhibit complex structural heterogeneity arising from glycosylation, post-translational modifications, sequence variants, and product-related impurities. This project will develop computational methods for interpretation of high-resolution mass spectrometry data to characterize these critical quality attributes. The research will integrate machine learning, statistical analysis, and computational mass spectrometry to enable robust and scalable analytical workflows for biopharmaceutical development.

The aim is to understand the molecular elasticity of biopolymers with potential engineering applications. The first example is Spider Dragline Silk, which may be several times stronger than steel (after normalizing the density). The work involves experimental, computational and theoretical analyses of the molecular structure of the biopolymer system. We will also take recourse to applying mathemtical and conceptual principles of Polymer Physics.