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ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks

Overview of attention for article published in Algorithms for Molecular Biology, August 2017
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Title
ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks
Published in
Algorithms for Molecular Biology, August 2017
DOI 10.1186/s13015-017-0111-2
Pubmed ID
Authors

Emna Ben Abdallah, Maxime Folschette, Olivier Roux, Morgan Magnin, Emna Ben Abdallah, Maxime Folschette, Olivier Roux, Morgan Magnin

Abstract

This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.

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

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 30%
Student > Ph. D. Student 2 20%
Unspecified 1 10%
Professor 1 10%
Student > Master 1 10%
Other 2 20%
Readers by discipline Count As %
Computer Science 3 30%
Biochemistry, Genetics and Molecular Biology 2 20%
Agricultural and Biological Sciences 2 20%
Psychology 1 10%
Medicine and Dentistry 1 10%
Other 1 10%

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 15 September 2017.
All research outputs
#7,043,047
of 11,771,127 outputs
Outputs from Algorithms for Molecular Biology
#89
of 180 outputs
Outputs of similar age
#139,918
of 266,208 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 8 outputs
Altmetric has tracked 11,771,127 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 180 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 48th percentile – i.e., 48% 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 266,208 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 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.