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Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

Overview of attention for article published in Frontiers in immunology, January 2013
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Title
Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers
Published in
Frontiers in immunology, January 2013
DOI 10.3389/fimmu.2013.00300
Pubmed ID
Authors

Véronique Thomas-Vaslin, Adrien Six, Jean-Gabriel Ganascia, Hugues Bersini

Abstract

Dynamic modeling of lymphocyte behavior has primarily been based on populations based differential equations or on cellular agents moving in space and interacting each other. The final steps of this modeling effort are expressed in a code written in a programing language. On account of the complete lack of standardization of the different steps to proceed, we have to deplore poor communication and sharing between experimentalists, theoreticians and programmers. The adoption of diagrammatic visual computer language should however greatly help the immunologists to better communicate, to more easily identify the models similarities and facilitate the reuse and extension of existing software models. Since immunologists often conceptualize the dynamical evolution of immune systems in terms of "state-transitions" of biological objects, we promote the use of unified modeling language (UML) state-transition diagram. To demonstrate the feasibility of this approach, we present a UML refactoring of two published models on thymocyte differentiation. Originally built with different modeling strategies, a mathematical ordinary differential equation-based model and a cellular automata model, the two models are now in the same visual formalism and can be compared.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 4%
Romania 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 29%
Student > Ph. D. Student 7 25%
Professor 5 18%
Other 3 11%
Student > Master 2 7%
Other 2 7%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 36%
Unspecified 4 14%
Computer Science 3 11%
Immunology and Microbiology 2 7%
Medicine and Dentistry 2 7%
Other 3 11%
Unknown 4 14%
Attention Score in Context

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 01 October 2013.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from Frontiers in immunology
#27,447
of 31,554 outputs
Outputs of similar age
#258,564
of 289,149 outputs
Outputs of similar age from Frontiers in immunology
#335
of 503 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 31,554 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 503 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.