Getting the Right Clones in an Automated Manner: An Alternative to Sophisticated Colony-Picking Robotics
In recent years, the design-build-test-learn (DBTL) cycle has become a key concept in strain engineering. Modern biofoundries enable automated DBTL cycling using robotic devices. However, both highly automated facilities and semi-automated facilities encounter bottlenecks in clone selection and screening. While fully automated biofoundries can take advantage of expensive commercially available colony pickers, semi-automated facilities have to fall back on affordable alternatives. Therefore, our clone selection method is particularly well-suited for academic settings, requiring only the basic infrastructure of a biofoundry. The automated liquid clone selection (ALCS) method represents a straightforward approach for clone selection. Similar to sophisticated colony-picking robots, the ALCS approach aims to achieve high selectivity. Investigating the time analogue of five generations, the model-based set-up reached a selectivity of 98 ± 0.2% for correctly transformed cells. Moreover, the method is robust to variations in cell numbers at the start of ALCS. Beside <i>Escherichia coli</i>, promising chassis organisms, such as <i>Pseudomonas putida</i> and <i>Corynebacterium glutamicum</i>, were successfully applied. In all cases, ALCS enables the immediate use of the selected strains in follow-up applications. In essence, our ALCS approach provides a 'low-tech' method to be implemented in biofoundry settings without requiring additional devices.
Authors
- Ralf Takors (ORCID: https://orcid.org/0000-0001-5837-6906)
- Brian F. Pfleger (ORCID: https://orcid.org/0000-0002-9232-9959)
- Lorena Hägele (ORCID: https://orcid.org/0009-0001-6624-3457)
Institutions
- University of Stuttgart (DE)
- University of Wisconsin–Madison (US)
Publication Details
- Journal
- Bioengineering
- Published
- 2024-09-03
- DOI
- https://doi.org/10.3390/bioengineering11090892
- Citations
- 1
- Primary Topic
- Modular Robots and Swarm Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.21
Funders
- Deutsche Forschungsgemeinschaft
- Universität Stuttgart