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Understanding key features of bacterial restriction-modification systems through quantitative modeling

Overview of attention for article published in BMC Systems Biology, February 2017
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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2 Wikipedia pages

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Title
Understanding key features of bacterial restriction-modification systems through quantitative modeling
Published in
BMC Systems Biology, February 2017
DOI 10.1186/s12918-016-0377-x
Pubmed ID
Authors

Andjela Rodic, Bojana Blagojevic, Evgeny Zdobnov, Magdalena Djordjevic, Marko Djordjevic

Abstract

Restriction-modification (R-M) systems are rudimentary bacterial immune systems. The main components include restriction enzyme (R), which cuts specific unmethylated DNA sequences, and the methyltransferase (M), which protects the same DNA sequences. The expression of R-M system components is considered to be tightly regulated, to ensure successful establishment in a naïve bacterial host. R-M systems are organized in different architectures (convergent or divergent) and are characterized by different features, i.e. binding cooperativities, dissociation constants of dimerization, translation rates, which ensure this tight regulation. It has been proposed that R-M systems should exhibit certain dynamical properties during the system establishment, such as: i) a delayed expression of R with respect to M, ii) fast transition of R from "OFF" to "ON" state, iii) increased stability of the toxic molecule (R) steady-state levels. It is however unclear how different R-M system features and architectures ensure these dynamical properties, particularly since it is hard to address this question experimentally. To understand design of different R-M systems, we computationally analyze two R-M systems, representative of the subset controlled by small regulators called 'C proteins', and differing in having convergent or divergent promoter architecture. We show that, in the convergent system, abolishing any of the characteristic system features adversely affects the dynamical properties outlined above. Moreover, an extreme binding cooperativity, accompanied by a very high dissociation constant of dimerization, observed in the convergent system, but absent from other R-M systems, can be explained in terms of the same properties. Furthermore, we develop the first theoretical model for dynamics of a divergent R-M system, which does not share any of the convergent system features, but has overlapping promoters. We show that i) the system dynamics exhibits the same three dynamical properties, ii) introducing any of the convergent system features to the divergent system actually diminishes these properties. Our results suggest that different R-M architectures and features may be understood in terms of constraints imposed by few simple dynamical properties of the system, providing a unifying framework for understanding these seemingly diverse systems. We also provided predictions for the perturbed R-M systems dynamics, which may in future be tested through increasingly available experimental techniques, such as re-engineering R-M systems and single-cell experiments.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 17%
Student > Bachelor 11 16%
Student > Master 9 13%
Researcher 6 9%
Student > Doctoral Student 3 4%
Other 6 9%
Unknown 23 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 31%
Agricultural and Biological Sciences 7 10%
Medicine and Dentistry 4 6%
Immunology and Microbiology 4 6%
Computer Science 2 3%
Other 7 10%
Unknown 24 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 February 2024.
All research outputs
#7,721,360
of 25,282,542 outputs
Outputs from BMC Systems Biology
#265
of 1,131 outputs
Outputs of similar age
#113,728
of 318,165 outputs
Outputs of similar age from BMC Systems Biology
#5
of 28 outputs
Altmetric has tracked 25,282,542 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,131 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 318,165 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.