Abstract
Optimisation of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved through engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, design of specific genetic parts can still be challenging due to 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 show 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 cycle and the Upper Confidence Bound multi-armed 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 experimentally validated RBSs with high translation initiation rates equalling 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 towards more complicated genetic devices.
Competing Interest Statement
The authors have declared no competing interest.
