Small-scale high-throughput systems like the integrated robotic mini bioreactor platform at the Chair of Bioprocess Engineering (Haby et al., 2019) offer promising potential for characterising and discriminating a wide range of strains quickly and cost-effectively. However, the upscale process of bioprocess development shows a weak robustness to reproduce results from such systems on a production scale due to different technical influences.
Oscillatory control strategies such as pulse-based feeding can help to overcome this problem as they can mimic the gradients in larger reactors that significantly affect organism performance (Anane et al., 2018). In addition, Anane shows that mechanistic models allow a dynamic description of these gradients and accordingly the estimation of physiological parameters.
Building on his study, this project will develop an efficient applicability for Mini BioReactors (MBR). Here, the physiological parameters act as a key for the scale down by considering them as a comparative key figure within the different scales. Based on these new metrics, a system will be developed to condition the MBR control to a specific scale. The next step is to transform screening from a static to a dynamic method. The extrapolating property of a model is used to perform strain-specific screening with adaptive process control (Hans et al., 2020) in pre-defined boundary conditions.
This project will enable a faster, cheaper, more robust and optimised approach to strain discrimination in bioprocess development with relation to production scale.