10.17867/10000151C
Brewster, Robert
Robert
Brewster
https://orcid.org/0000-0002-7656-4086
University of Massachusetts Medical School
Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif
University of Dundee
2020
Image
FOS: Biological sciences
Ali, Md Zulfikar
Md Zulfikar
Ali
https://orcid.org/0000-0002-7054-0059
University of Massachusetts Medical School
Parisutham, Vinuselvi
Vinuselvi
Parisutham
https://orcid.org/0000-0002-0349-4072
University of Massachusetts Medical School
Choubey, Sandeep
Sandeep
Choubey
https://orcid.org/0000-0002-7387-6148
University of Massachusetts Medical School
2020-11-12
2020
en
10.7554/elife.56517
32808926
nd2
Creative Commons Attribution 4.0 International
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.
Studying the asymmetry between TF and target gene when the protein dilution rate is altered by growing cells in different media.
National Institutes of Health
https://doi.org/10.13039/100000002
R35GM128797