UMMS Affiliation

Program in Systems Biology; Department of Microbiology and Physiological Systems

Publication Date

2020-08-18

Document Type

Article Postprint

Disciplines

Computational Biology | Genetic Phenomena | Systems Biology

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.

Keywords

E. coli, computational biology, systems biology, gene expression, network architecture

Rights and Permissions

© 2020, Ali et al. 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.

DOI of Published Version

10.7554/eLife.56517

Source

Ali MZ, Parisutham V, Choubey S, Brewster RC. Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif. Elife. 2020 Aug 18;9:e56517. doi: 10.7554/eLife.56517. Epub ahead of print. PMID: 32808926. Link to article on publisher's site

Journal/Book/Conference Title

eLife

Related Resources

Link to Article in PubMed

PubMed ID

32808926

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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