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Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming

Overview of attention for article published in Algorithms for Molecular Biology, July 2017
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About this Attention Score

  • Among the highest-scoring outputs from this source (#47 of 182)
  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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5 tweeters

Citations

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3 Dimensions

Readers on

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9 Mendeley
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Title
Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming
Published in
Algorithms for Molecular Biology, July 2017
DOI 10.1186/s13015-017-0110-3
Pubmed ID
Authors

Louis Fippo Fitime, Olivier Roux, Carito Guziolowski, Loïc Paulevé

Abstract

Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour. In the context of non-deterministic automata networks, we propose a static identification of so-called bifurcations, i.e., transitions after which a given goal is no longer reachable. Such transitions are naturally good candidates for controlling the occurrence of the goal, notably by modulating their propensity. Our method combines Answer-Set Programming with static analysis of reachability properties to provide an under-approximation of all the existing bifurcations. We illustrate our discrete bifurcation analysis on several models of biological systems, for which we identify transitions which impact the reachability of given long-term behaviour. In particular, we apply our implementation on a regulatory network among hundreds of biological species, supporting the scalability of our approach. Our method allows a formal and scalable identification of transitions which are responsible for the lost of capability to reach a given state. It can be applied to any asynchronous automata networks, which encompass Boolean and multi-valued models. An implementation is provided as part of the Pint software, available at http://loicpauleve.name/pint.

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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Researcher 3 33%
Unspecified 1 11%
Professor 1 11%
Student > Postgraduate 1 11%
Other 0 0%
Readers by discipline Count As %
Computer Science 3 33%
Biochemistry, Genetics and Molecular Biology 2 22%
Mathematics 1 11%
Business, Management and Accounting 1 11%
Unspecified 1 11%
Other 1 11%

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 25 July 2017.
All research outputs
#3,438,824
of 12,444,621 outputs
Outputs from Algorithms for Molecular Biology
#47
of 182 outputs
Outputs of similar age
#88,069
of 262,802 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 7 outputs
Altmetric has tracked 12,444,621 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 182 research outputs from this source. They receive a mean Attention Score of 2.9. This one has gotten more attention than average, scoring higher than 74% 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 262,802 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 66% 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.