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A framework to find the logic backbone of a biological network

Overview of attention for article published in BMC Systems Biology, December 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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5 tweeters

Citations

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10 Dimensions

Readers on

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26 Mendeley
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Title
A framework to find the logic backbone of a biological network
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0482-5
Pubmed ID
Authors

Parul Maheshwari, Réka Albert

Abstract

Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network. In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures. The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 35%
Researcher 6 23%
Student > Bachelor 5 19%
Student > Master 3 12%
Unspecified 2 8%
Other 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 27%
Biochemistry, Genetics and Molecular Biology 6 23%
Mathematics 3 12%
Unspecified 3 12%
Chemical Engineering 2 8%
Other 5 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 December 2017.
All research outputs
#6,494,719
of 12,269,011 outputs
Outputs from BMC Systems Biology
#368
of 1,015 outputs
Outputs of similar age
#137,694
of 344,237 outputs
Outputs of similar age from BMC Systems Biology
#16
of 53 outputs
Altmetric has tracked 12,269,011 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,015 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 62% 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 344,237 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 59% of its contemporaries.
We're also able to compare this research output to 53 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 66% of its contemporaries.