↓ Skip to main content

Molecular network control through boolean canalization

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, November 2015
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
14 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
Molecular network control through boolean canalization
Published in
EURASIP Journal on Bioinformatics & Systems Biology, November 2015
DOI 10.1186/s13637-015-0029-2
Pubmed ID
Authors

David Murrugarra, Elena S. Dimitrova

Abstract

Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studied in the context of network modeling where each layer of canalization adds a degree of stability in the dynamics of the network. Recently, dynamic network control approaches have been used for the design of new therapeutic interventions and for other applications such as stem cell reprogramming. This work studies the role of canalization in the control of Boolean molecular networks. It provides a method for identifying the potential edges to control in the wiring diagram of a network for avoiding undesirable state transitions. The method is based on identifying appropriate input-output combinations on undesirable transitions that can be modified using the edges in the wiring diagram of the network. Moreover, a method for estimating the number of changed transitions in the state space of the system as a result of an edge deletion in the wiring diagram is presented. The control methods of this paper were applied to a mutated cell-cycle model and to a p53-mdm2 model to identify potential control targets.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 7%
Mexico 1 7%
United States 1 7%
Unknown 11 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Student > Ph. D. Student 3 21%
Professor 2 14%
Student > Doctoral Student 2 14%
Other 1 7%
Other 2 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 21%
Mathematics 3 21%
Engineering 2 14%
Unspecified 2 14%
Computer Science 2 14%
Other 2 14%

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 07 January 2016.
All research outputs
#7,191,492
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#15
of 51 outputs
Outputs of similar age
#151,859
of 346,954 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#2
of 7 outputs
Altmetric has tracked 12,457,990 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 51 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 66% 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 346,954 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 53% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.