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

Computational design of Small Transcription Activating RNAs (STARs) for versatile and dynamic gene regulation

View ORCID ProfileJames Chappell, View ORCID ProfileAlexandra Westbrook, View ORCID ProfileMatthew Verosloff, Julius B. Lucks
doi: https://doi.org/10.1101/169391
James Chappell
1Department of Chemical and Biological Engineering, Northwestern University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for James Chappell
Alexandra Westbrook
2Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Alexandra Westbrook
Matthew Verosloff
3Interdisciplinary Biological Sciences Graduate Program, Northwestern University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Matthew Verosloff
Julius B. Lucks
1Department of Chemical and Biological Engineering, Northwestern University
3Interdisciplinary Biological Sciences Graduate Program, Northwestern University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

A longstanding goal of synthetic biology has been the programmable control of cellular functions. Central to this goal is the creation of versatile regulatory toolsets that allow for programmable control of gene expression. Of the many regulatory molecules available, RNA regulators offer the intriguing possibility of de novo design – allowing for the bottom-up molecular-level design of genetic control systems. Here we present a computational design approach for the creation of a bacterial regulator called Small Transcription Activating RNAs (STARs) and create a library of high-performing and orthogonal STARs that achieve up to ∼9000-fold gene activation. We then demonstrate the versatility of RNA-based transcription control by showing the broad utility of STARs – from acting synergistically with existing constitutive and inducible regulators, to reprogramming cellular phenotypes and controlling multigene metabolic pathway expression. Finally, we combine these new STARs with themselves and CRISPRi transcriptional repressors to deliver new types of RNA-based genetic circuitry that allow for sophisticated and temporal control of gene expression.

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 July 28, 2017.
Download PDF

Supplementary Material

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.
Computational design of Small Transcription Activating RNAs (STARs) for versatile and dynamic gene regulation
(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
Computational design of Small Transcription Activating RNAs (STARs) for versatile and dynamic gene regulation
James Chappell, Alexandra Westbrook, Matthew Verosloff, Julius B. Lucks
bioRxiv 169391; doi: https://doi.org/10.1101/169391
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Computational design of Small Transcription Activating RNAs (STARs) for versatile and dynamic gene regulation
James Chappell, Alexandra Westbrook, Matthew Verosloff, Julius B. Lucks
bioRxiv 169391; doi: https://doi.org/10.1101/169391

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 (3483)
  • Biochemistry (7336)
  • Bioengineering (5305)
  • Bioinformatics (20219)
  • Biophysics (9990)
  • Cancer Biology (7713)
  • Cell Biology (11280)
  • Clinical Trials (138)
  • Developmental Biology (6426)
  • Ecology (9927)
  • Epidemiology (2065)
  • Evolutionary Biology (13294)
  • Genetics (9353)
  • Genomics (12564)
  • Immunology (7686)
  • Microbiology (18979)
  • Molecular Biology (7426)
  • Neuroscience (40937)
  • Paleontology (300)
  • Pathology (1226)
  • Pharmacology and Toxicology (2132)
  • Physiology (3145)
  • Plant Biology (6849)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1893)
  • Systems Biology (5306)
  • Zoology (1086)