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A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES).

Overview of attention for article published in Biology Direct, January 2015
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A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES).
Published in
Biology Direct, January 2015
DOI 10.1186/s13062-015-0070-9
Pubmed ID

Campos, Marcelino, Llorens, Carlos, Sempere, José M, Futami, Ricardo, Rodriguez, Irene, Carrasco, Purificación, Capilla, Rafael, Latorre, Amparo, Coque, Teresa M, Moya, Andres, Baquero, Fernando, Campos Frances, Marcelino, Sempere Luna, José María, Rodríguez, Irene, Coque, Teresa M., Moya, Andrés


Antibiotic resistance is a major biomedical problem upon which public health systems demand solutions to construe the dynamics and epidemiological risk of resistant bacteria in anthropogenically-altered environments. The implementation of computable models with reciprocity within and between levels of biological organization (i.e. essential nesting) is central for studying antibiotic resistances. Antibiotic resistance is not just the result of antibiotic-driven selection but more properly the consequence of a complex hierarchy of processes shaping the ecology and evolution of the distinct subcellular, cellular and supra-cellular vehicles involved in the dissemination of resistance genes. Such a complex background motivated us to explore the P-system standards of membrane computing an innovative natural computing formalism that abstracts the notion of movement across membranes to simulate antibiotic resistance evolution processes across nested levels of micro- and macro-environmental organization in a given ecosystem. In this article, we introduce ARES (Antibiotic Resistance Evolution Simulator) a software device that simulates P-system model scenarios with five types of nested computing membranes oriented to emulate a hierarchy of eco-biological compartments, i.e. a) peripheral ecosystem; b) local environment; c) reservoir of supplies; d) animal host; and e) host's associated bacterial organisms (microbiome). Computational objects emulating molecular entities such as plasmids, antibiotic resistance genes, antimicrobials, and/or other substances can be introduced into this framework and may interact and evolve together with the membranes, according to a set of pre-established rules and specifications. ARES has been implemented as an online server and offers additional tools for storage and model editing and downstream analysis. The stochastic nature of the P-system model implemented in ARES explicitly links within and between host dynamics into a simulation, with feedback reciprocity among the different units of selection influenced by antibiotic exposure at various ecological levels. ARES offers the possibility of modeling predictive multilevel scenarios of antibiotic resistance evolution that can be interrogated, edited and re-simulated if necessary, with different parameters, until a correct model description of the process in the real world is convincingly approached. ARES can be accessed at http://gydb.org/ares . This article was reviewed by Eugene V. Koonin, and Eric Bapteste.

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Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 23%
Student > Ph. D. Student 10 19%
Student > Master 7 13%
Student > Bachelor 6 11%
Professor 2 4%
Other 9 17%
Unknown 7 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 25%
Engineering 5 9%
Computer Science 4 8%
Biochemistry, Genetics and Molecular Biology 4 8%
Mathematics 3 6%
Other 14 26%
Unknown 10 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 27 May 2016.
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So far Altmetric has tracked 569 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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