Master's thesis: Simulation of Silica-Based Affinity Chromatography with a Mechanistic CADET Model

12.11.2025, Diplomarbeiten, Bachelor- und Masterarbeiten

Due to its high mechanical strength and easy manufacturing, silica is often used as a stationary phase in chromatographic processes. To achieve high-purity protein purification, silica resins can be combined with silica-binding amino acid tags. A novel octapeptide (RH)₄ tag, the PosH-Tag, has been developed in our group. Purifying PosH-tagged proteins with low-cost silica resins enables an economical and efficient affinity chromatography process. The next step is to purify industry-relevant proteins (e.g., Protein A variants), which are currently produced through complex and expensive purification schemes. Developing mechanistic models for silica-based affinity chromatography allows a deeper understanding of binding phenomena and provides predictive capabilities for process optimization. Using the CADET framework, adsorption and mass-transfer mechanisms can be described mathematically and validated with experimental data. The focus of this project lies on developing, extending, and refining the existing CADET-based mechanistic model to accurately describe protein binding on silica. These models will serve as a foundation for transferring the process from batch to continuous multi-column chromatography. The student will be responsible for implementing and parameterizing the models in CADET, analyzing simulation data, and integrating experimental results for model validation. This project is of high industrial relevance, as the shift from empirical experimentation to mechanistic, model-driven process development is one of the key goals in modern bioprocess engineering.

Research objectives • Refine the existing CADET model by integrating protein adsorption data. • Optimize binding and elution conditions for PosH-tagged proteins on silica. • Validate the refined model using experimental chromatography results.

Profile • Background in IBT, Bioprocess-, Chemical-, Mechanical, Data Engineering, computational science or similar. • Solid Python skills, experience with scientific libraries and interest in chromatographic process modeling.

Start: flexible, ASAP

Kontakt: a.riera@tum.de

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