↓ Skip to main content

Distinguishing the rates of gene activation from phenotypic variations

Overview of attention for article published in BMC Systems Biology, June 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
16 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Distinguishing the rates of gene activation from phenotypic variations
Published in
BMC Systems Biology, June 2015
DOI 10.1186/s12918-015-0172-0
Pubmed ID
Authors

Ye Chen, Cheng Lv, Fangting Li, Tiejun Li

Abstract

Stochastic genetic switching driven by intrinsic noise is an important process in gene expression. When the rates of gene activation/inactivation are relatively slow, fast, or medium compared with the synthesis/degradation rates of mRNAs and proteins, the variability of protein and mRNA levels may exhibit very different dynamical patterns. It is desirable to provide a systematic approach to identify their key dynamical features in different regimes, aiming at distinguishing which regime a considered gene regulatory network is in from their phenotypic variations. We studied a gene expression model with positive feedbacks when genetic switching rates vary over a wide range. With the goal of providing a method to distinguish the regime of the switching rates, we first focus on understanding the essential dynamics of gene expression system in different cases. In the regime of slow switching rates, we found that the effective dynamics can be reduced to independent evolutions on two separate layers corresponding to gene activation and inactivation states, and the transitions between two layers are rare events, after which the system goes mainly along deterministic ODE trajectories on a particular layer to reach new steady states. The energy landscape in this regime can be well approximated by using Gaussian mixture model. In the regime of intermediate switching rates, we analyzed the mean switching time to investigate the stability of the system in different parameter ranges. We also discussed the case of fast switching rates from the viewpoint of transition state theory. Based on the obtained results, we made a proposal to distinguish these three regimes in a simulation experiment. We identified the intermediate regime from the fact that the strength of cellular memory is lower than the other two cases, and the fast and slow regimes can be distinguished by their different perturbation-response behavior with respect to the switching rates perturbations. We proposed a simulation experiment to distinguish the slow, intermediate and fast regimes, which is the main point of our paper. In order to achieve this goal, we systematically studied the essential dynamics of gene expression system when the switching rates are in different regimes. Our theoretical understanding provides new insights on the gene expression experiments.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 6%
Unknown 15 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 38%
Student > Ph. D. Student 3 19%
Other 1 6%
Student > Doctoral Student 1 6%
Professor 1 6%
Other 3 19%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 19%
Biochemistry, Genetics and Molecular Biology 3 19%
Computer Science 2 13%
Medicine and Dentistry 2 13%
Arts and Humanities 1 6%
Other 4 25%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 June 2015.
All research outputs
#14,229,946
of 22,813,792 outputs
Outputs from BMC Systems Biology
#544
of 1,142 outputs
Outputs of similar age
#135,893
of 264,477 outputs
Outputs of similar age from BMC Systems Biology
#15
of 30 outputs
Altmetric has tracked 22,813,792 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 264,477 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.