An efficient high-throughput screening of high gentamicin-producing
mutants based on titer determination using an integrated computer-aided
vision technology and machine learning
Abstract
The ‘design-build-test-learn’ (DBTL) cycle has been adopted in rational
high-throughput screening for obtaining high-yield industrial strains.
However, the mismatch between build and test slows the DBTL cycle due to
the lack of high-throughput analytical technologies. In this study, a
highly-efficient, accurate, and non-invasive detection method of
gentamicin (GM) was developed, which can provide timely feedback for the
high-throughput screening of high-yield strains. Firstly, a self-made
tool was established to obtain datasets in 24-well based on the
coloring of cells. Subsequently, the random forest (RF) algorithm was
found to have the highest prediction accuracy with 98.5% for the
training and 91.3% for verification. Finally, a stable genetic
high-yield strain (998U/mL) was successfully screened out in 3005
mutants, which was verified to improve the titer by 72.7% in a 5 L
bioreactor. Moreover, the verified new datasets were updated to the
model database in order to improve learning ability of DBTL cycle.