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

Identifying competition phenotypes in synthetic biochemical circuits

View ORCID ProfileM. Ali Al-Radhawi, View ORCID ProfileDomitilla Del Vecchio, View ORCID ProfileEduardo D. Sontag
doi: https://doi.org/10.1101/2022.03.22.485420
M. Ali Al-Radhawi
1Departments of Electrical & Computer Engineering, and Bioengineering, Northeastern University, Boston, MA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for M. Ali Al-Radhawi
Domitilla Del Vecchio
2Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Domitilla Del Vecchio
Eduardo D. Sontag
1Departments of Electrical & Computer Engineering, and Bioengineering, Northeastern University, Boston, MA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Eduardo D. Sontag
  • For correspondence: e.sontag@northeastern.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Synthetic gene circuits require cellular resources, which are often limited. This leads to competition for resources by different genes, which alter a synthetic genetic circuit’s behavior. However, the manner in which competition impacts behavior depends on the identity of the “bottleneck” resource which might be difficult to discern from input-output data. In this paper, we aim at classifying the mathematical structures of resource competition in biochemical circuits. We find that some competition structures can be distinguished by their response to different competitors or resource levels. Specifically, we show that some response curves are always linear, convex, or concave. Furthermore, high levels of certain resources protect the behavior from low competition, while others do not. We also show that competition phenotypes respond differently to various interventions. Such differences can be used to eliminate candidate competition mechanisms when constructing models based on given data. On the other hand, we show that different networks can display mathematically equivalent competition phenotypes.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Emails: malirdwi{at}northeastern.edu

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-ND 4.0 International license.
Back to top
PreviousNext
Posted March 23, 2022.
Download PDF
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.
Identifying competition phenotypes in synthetic biochemical circuits
(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
Identifying competition phenotypes in synthetic biochemical circuits
M. Ali Al-Radhawi, Domitilla Del Vecchio, Eduardo D. Sontag
bioRxiv 2022.03.22.485420; doi: https://doi.org/10.1101/2022.03.22.485420
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Identifying competition phenotypes in synthetic biochemical circuits
M. Ali Al-Radhawi, Domitilla Del Vecchio, Eduardo D. Sontag
bioRxiv 2022.03.22.485420; doi: https://doi.org/10.1101/2022.03.22.485420

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 (4227)
  • Biochemistry (9105)
  • Bioengineering (6751)
  • Bioinformatics (23944)
  • Biophysics (12088)
  • Cancer Biology (9493)
  • Cell Biology (13739)
  • Clinical Trials (138)
  • Developmental Biology (7616)
  • Ecology (11661)
  • Epidemiology (2066)
  • Evolutionary Biology (15479)
  • Genetics (10616)
  • Genomics (14296)
  • Immunology (9462)
  • Microbiology (22789)
  • Molecular Biology (9078)
  • Neuroscience (48884)
  • Paleontology (355)
  • Pathology (1479)
  • Pharmacology and Toxicology (2565)
  • Physiology (3823)
  • Plant Biology (8308)
  • Scientific Communication and Education (1467)
  • Synthetic Biology (2290)
  • Systems Biology (6171)
  • Zoology (1297)