Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site

Optimization of gene
expression levels is an essential part of
the organism design process. Fine control of this process can be achieved
by engineering transcription and translation control elements, including
the ribosome binding site (RBS). Unfortunately, the design of specific
genetic parts remains challenging because of the lack of reliable
design methods. To address this problem, we have created a machine
learning guided Design–Build–Test–Learn (DBTL)
cycle for the experimental design of bacterial RBSs to demonstrate
how small genetic parts can be reliably designed using relatively
small, high-quality data sets. We used Gaussian Process Regression
for the Learn phase of the cycle and the Upper Confidence Bound multiarmed
bandit algorithm for the Design of genetic variants to be tested in
vivo. We have integrated these machine learning algorithms with laboratory
automation and high-throughput processes for reliable data generation.
Notably, by Testing a total of 450 RBS variants in four DBTL cycles,
we have experimentally validated RBSs with high translation initiation
rates equaling or exceeding our benchmark RBS by up to 34%. Overall,
our results show that machine learning is a powerful tool for designing
RBSs, and they pave the way toward more complicated genetic devices.