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A Petri Net Model of Granulomatous Inflammation: Implications for IL-10 Mediated Control of Leishmania donovani Infection

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
A Petri Net Model of Granulomatous Inflammation: Implications for IL-10 Mediated Control of Leishmania donovani Infection
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003334
Pubmed ID
Authors

Luca Albergante, Jon Timmis, Lynette Beattie, Paul M. Kaye

Abstract

Experimental visceral leishmaniasis, caused by infection of mice with the protozoan parasite Leishmania donovani, is characterized by focal accumulation of inflammatory cells in the liver, forming discrete "granulomas" within which the parasite is eventually eliminated. To shed new light on fundamental aspects of granuloma formation and function, we have developed an in silico Petri net model that simulates hepatic granuloma development throughout the course of infection. The model was extensively validated by comparison with data derived from experimental studies in mice, and the model robustness was assessed by a sensitivity analysis. The model recapitulated the progression of disease as seen during experimental infection and also faithfully predicted many of the changes in cellular composition seen within granulomas over time. By conducting in silico experiments, we have identified a previously unappreciated level of inter-granuloma diversity in terms of the development of anti-leishmanial activity. Furthermore, by simulating the impact of IL-10 gene deficiency in a variety of lymphocyte and myeloid cell populations, our data suggest a dominant local regulatory role for IL-10 produced by infected Kupffer cells at the core of the granuloma.

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Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Brazil 1 2%
Unknown 47 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 33%
Student > Ph. D. Student 11 22%
Student > Master 7 14%
Professor 4 8%
Professor > Associate Professor 2 4%
Other 5 10%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 33%
Biochemistry, Genetics and Molecular Biology 4 8%
Immunology and Microbiology 4 8%
Medicine and Dentistry 3 6%
Mathematics 3 6%
Other 12 24%
Unknown 7 14%