↓ Skip to main content

RACIPE: a computational tool for modeling gene regulatory circuits using randomization

Overview of attention for article published in BMC Systems Biology, June 2018
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

twitter
5 X users

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
91 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
RACIPE: a computational tool for modeling gene regulatory circuits using randomization
Published in
BMC Systems Biology, June 2018
DOI 10.1186/s12918-018-0594-6
Pubmed ID
Authors

Bin Huang, Dongya Jia, Jingchen Feng, Herbert Levine, José N. Onuchic, Mingyang Lu

Abstract

One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks. We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. Taking the network topology as the only input, RACIPE generates an ensemble of circuit models with distinct randomized parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss the implementation of the software and the statistical analysis methods of RACIPE-generated data to identify robust gene expression patterns and the functions of genes and regulatory links. Finally, we apply the tool on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis. We expect our new computational tool to contribute to a more comprehensive and unbiased understanding of mechanisms underlying gene regulatory networks. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub ( https://github.com/simonhb1990/RACIPE-1.0 ).

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 91 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 19%
Student > Bachelor 12 13%
Researcher 9 10%
Professor > Associate Professor 7 8%
Student > Doctoral Student 6 7%
Other 15 16%
Unknown 25 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 24%
Agricultural and Biological Sciences 11 12%
Computer Science 5 5%
Physics and Astronomy 5 5%
Mathematics 5 5%
Other 17 19%
Unknown 26 29%
Attention Score in Context

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 23 January 2023.
All research outputs
#13,908,131
of 23,578,918 outputs
Outputs from BMC Systems Biology
#475
of 1,135 outputs
Outputs of similar age
#170,504
of 328,802 outputs
Outputs of similar age from BMC Systems Biology
#10
of 23 outputs
Altmetric has tracked 23,578,918 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,135 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 55% 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 328,802 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 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 56% of its contemporaries.