Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Machine learning guided batched design of a bacterial Ribosome Binding Site

Mengyan Zhang, View ORCID ProfileMaciej Bartosz Holowko, Huw Hayman Zumpe, View ORCID ProfileCheng Soon Ong
doi: https://doi.org/10.1101/2022.01.05.475140
Mengyan Zhang
1Machine Learning and Artificial Intelligence Future Science Platform, CSIRO
2Department of Computer Science, Australian National University
3Data61, CSIRO
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Maciej Bartosz Holowko
4Synthetic Biology Future Science Platform, CSIRO
5Land and Water, CSIRO
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Maciej Bartosz Holowko
Huw Hayman Zumpe
4Synthetic Biology Future Science Platform, CSIRO
5Land and Water, CSIRO
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cheng Soon Ong
1Machine Learning and Artificial Intelligence Future Science Platform, CSIRO
2Department of Computer Science, Australian National University
3Data61, CSIRO
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Cheng Soon Ong
  • For correspondence: cheng-soon.ong@data61.csiro.au
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

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.

Footnotes

  • https://github.com/mholowko/Solaris

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted January 06, 2022.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Machine learning guided batched design of a bacterial Ribosome Binding Site
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Machine learning guided batched design of a bacterial Ribosome Binding Site
Mengyan Zhang, Maciej Bartosz Holowko, Huw Hayman Zumpe, Cheng Soon Ong
bioRxiv 2022.01.05.475140; doi: https://doi.org/10.1101/2022.01.05.475140
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Machine learning guided batched design of a bacterial Ribosome Binding Site
Mengyan Zhang, Maciej Bartosz Holowko, Huw Hayman Zumpe, Cheng Soon Ong
bioRxiv 2022.01.05.475140; doi: https://doi.org/10.1101/2022.01.05.475140

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Synthetic Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4369)
  • Biochemistry (9545)
  • Bioengineering (7068)
  • Bioinformatics (24767)
  • Biophysics (12559)
  • Cancer Biology (9923)
  • Cell Biology (14297)
  • Clinical Trials (138)
  • Developmental Biology (7929)
  • Ecology (12074)
  • Epidemiology (2067)
  • Evolutionary Biology (15954)
  • Genetics (10903)
  • Genomics (14705)
  • Immunology (9843)
  • Microbiology (23582)
  • Molecular Biology (9454)
  • Neuroscience (50691)
  • Paleontology (369)
  • Pathology (1535)
  • Pharmacology and Toxicology (2674)
  • Physiology (3997)
  • Plant Biology (8638)
  • Scientific Communication and Education (1505)
  • Synthetic Biology (2388)
  • Systems Biology (6415)
  • Zoology (1344)