Cargando…
Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site
[Image: see text] 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 g...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295160/ https://www.ncbi.nlm.nih.gov/pubmed/35704784 http://dx.doi.org/10.1021/acssynbio.2c00015 |
_version_ | 1784750002621382656 |
---|---|
author | Zhang, Mengyan Holowko, Maciej Bartosz Hayman Zumpe, Huw Ong, Cheng Soon |
author_facet | Zhang, Mengyan Holowko, Maciej Bartosz Hayman Zumpe, Huw Ong, Cheng Soon |
author_sort | Zhang, Mengyan |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-9295160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-92951602022-07-20 Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site Zhang, Mengyan Holowko, Maciej Bartosz Hayman Zumpe, Huw Ong, Cheng Soon ACS Synth Biol [Image: see text] 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. American Chemical Society 2022-06-15 2022-07-15 /pmc/articles/PMC9295160/ /pubmed/35704784 http://dx.doi.org/10.1021/acssynbio.2c00015 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Zhang, Mengyan Holowko, Maciej Bartosz Hayman Zumpe, Huw Ong, Cheng Soon Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site |
title | Machine Learning Guided Batched Design of a Bacterial
Ribosome Binding Site |
title_full | Machine Learning Guided Batched Design of a Bacterial
Ribosome Binding Site |
title_fullStr | Machine Learning Guided Batched Design of a Bacterial
Ribosome Binding Site |
title_full_unstemmed | Machine Learning Guided Batched Design of a Bacterial
Ribosome Binding Site |
title_short | Machine Learning Guided Batched Design of a Bacterial
Ribosome Binding Site |
title_sort | machine learning guided batched design of a bacterial
ribosome binding site |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295160/ https://www.ncbi.nlm.nih.gov/pubmed/35704784 http://dx.doi.org/10.1021/acssynbio.2c00015 |
work_keys_str_mv | AT zhangmengyan machinelearningguidedbatcheddesignofabacterialribosomebindingsite AT holowkomaciejbartosz machinelearningguidedbatcheddesignofabacterialribosomebindingsite AT haymanzumpehuw machinelearningguidedbatcheddesignofabacterialribosomebindingsite AT ongchengsoon machinelearningguidedbatcheddesignofabacterialribosomebindingsite |