Master Thesis: Mechanistic Model-Oriented Experimental Design for Silica- Based Affinity Chromatography

12.11.2025, Abschlussarbeiten, 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. These models will serve as a foundation for transferring the process from batch to continuous multi-column chromatography.
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. The work involves performing the chromatographic experiments that provide the basis for the mechanistic model.

Research objectives
• Generate experimental data on protein binding/elution in silica-based affinity chromatography.
• Characterize key process parameters relevant for model input.
• Support/validate mechanistic modeling by comparison between experimental results and CADET simulations.

Profile
• Background in IBT, Bioprocess-, Chemical Engineering, Biochemistry, etc.
• Prior experience with chromatography is of advantage.
• Team player and independent researcher.

Start flexible / ASAP

Kontakt: a.riera@tum.de

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