Repository for parsing, plotting and analysis of an Escherichia coli strain library producing glucose dehydrogenase from Bacillus subtilis (_Bs_GDH) as Catalytically active Inclusion Bodies (CatIBs). The overall goal is to perform consecutive screening experiments using a Bayesian process model and Thompson Sampling as a policy to select candidates.
This projects provides the raw data and data analysis notebooks for the manuscript "High-Throughput Screening of Catalytically Active Inclusion Bodies Using Laboratory Automation and Bayesian Optimization" (2023) by Laura M. Helleckes*, Kira Küsters*, Christian Wagner, Rebecca Hamel, Ronja Saborowski, Wolfgang Wiechert, Marco Oldiges.
*These authors contributed equally.
Raw data can be found in the data folder. Data analysis is conducted in notebooks, where plots for the accompanying paper can be found as well.
The results for individual experiments can be found by a unique idetentifier, their so-called Run ID.
The following runs were conducted:
| Preculture ID | Main Culture ID | Assay ID | Description |
|---|---|---|---|
| D15DTK | D19YYZ | D1X8DM | TS Round 1 |
| D95YC6 | D9A1YM | D9XCLX | TS Round 2 |
| DAFT9R | DAMZ19 | DB984D | TS Round 3 |
| DDA79K | DDEBDB | DE3MB4 | Manual Round 4 |
Thompson Sampling on the reaction rates provided by the process model was performed in three rounds, leading to >95 % certainty in identifying the best performer from the library. The fourth screening round was designed manually to screen the top-10 CatIB variants as well as two previously unseen variants.
This repository and the corresponding Python package for data analysis (catibts) is licensed under the GNU Affero General Public License v3.0.
Head over to Zenodo to generate a BibTeX citation for the latest release.