Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif

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Abstract

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.

Data availability

Data was deposited to the Image Data Resource (https://idr.openmicroscopy.org) under accession number idr0095. Code used to generate figures and simulation code is available on github at https://github.com/zulfikgp/Autoregulation.

The following data sets were generated

(2020) Microscopy data for Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif
Image Data Resource, idr0095.

https://idr.openmicroscopy.org/search/?query=Name:95

Article and author information

Author details

Md Zulfikar Ali

Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-7054-0059
Vinuselvi Parisutham

Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-0349-4072
Sandeep Choubey

Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-7387-6148
Robert C Brewster

Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States

For correspondence
Robert.brewster@umassmed.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-7656-4086

Funding

National Institute of General Medical Sciences (R35GM128797)

Md Zulfikar Ali
Vinuselvi Parisutham
Robert C Brewster

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.