Motivation and Goals of this position
Refolding of proteins from Inclusion Bodies (IBs) poses a viable alternative to soluble expression of proteins, since the IBs consist of the target protein with high purity, high concentrations and high stability due to steric separation from proteases. Recently, these benefits outweighed the difficulties introduced with the low yields of necessary solubilization and subsequent refolding operations.
Model based approaches using Digital Twins are a promising method to gather process knowledge in the form of process models and to enable a controlled and yield oriented refolding process. It is crucial to have measurements of different protein conformations available in real-time (HPLC, enzymatic assay, spectroscopic methods etc.). However, data generated from these methods need to be timely and reproducibly processed to be utilizable by the Digital Twin.
The thesis aims to:
- Develop an evaluation algorithm to automate the chromatographic evaluation with offline chromatographic data
- Deploy the evaluation algorithm on a webserver to transfer the processed data to the process control software in real-time
- Verify the functionality by performing a refolding process with online HPLC measurements
- Experience in the numerical programming language Python® and willingness to intensify these skills
- Experience in standard lab work is advantageous
- Curiosity to test developments within real experiments
- Work in a multidisciplinary team on a future-oriented topic
- Connect with industry and learn their language and thinking
- Intensification of skills in process system engineering and data science
Time-frame and salary
This work ideally starts in Summer 2021 and is scheduled for 6 months. A small compensation (€ 1800 = 6 x € 300) upon successful completion of the thesis is possible. Possibility to be done part-time for 9 or 12 months