A Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response.

2.50
Hdl Handle:
http://hdl.handle.net/10033/577312
Title:
A Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response.
Authors:
Boianelli, Alessandro; Pettini, Elena; Prota, Gennaro; Medaglini, Donata; Vicino, Antonio
Abstract:
The study of the initial phase of the adaptive immune response after first antigen encounter provides essential information on the magnitude and quality of the immune response. This phase is characterized by proliferation and dissemination of T cells in the lymphoid organs. Modeling and identifying the key features of this phenomenon may provide a useful tool for the analysis and prediction of the effects of immunization. This knowledge can be effectively exploited in vaccinology, where it is of interest to evaluate and compare the responses to different vaccine formulations. The objective of this paper is to construct a stochastic model based on branching process theory, for the dissemination network of antigen-specific CD4+ T cells. The devised model is validated on in vivo animal experimental data. The model presented has been applied to the vaccine immunization context making references to simple proliferation laws that take into account division, death and quiescence, but it can also be applied to any context where it is of interest to study the dynamic evolution of a population.
Affiliation:
Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.
Citation:
A Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response. 2015, 10 (8):e0135787 PLoS ONE
Journal:
PloS one
Issue Date:
2015
URI:
http://hdl.handle.net/10033/577312
DOI:
10.1371/journal.pone.0135787
PubMed ID:
26301680
Type:
Article
Language:
en
ISSN:
1932-6203
Appears in Collections:
publications of the research group system immunology ([BRICS]SIMM)

Full metadata record

DC FieldValue Language
dc.contributor.authorBoianelli, Alessandroen
dc.contributor.authorPettini, Elenaen
dc.contributor.authorProta, Gennaroen
dc.contributor.authorMedaglini, Donataen
dc.contributor.authorVicino, Antonioen
dc.date.accessioned2015-09-15T08:47:04Zen
dc.date.available2015-09-15T08:47:04Zen
dc.date.issued2015en
dc.identifier.citationA Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response. 2015, 10 (8):e0135787 PLoS ONEen
dc.identifier.issn1932-6203en
dc.identifier.pmid26301680en
dc.identifier.doi10.1371/journal.pone.0135787en
dc.identifier.urihttp://hdl.handle.net/10033/577312en
dc.description.abstractThe study of the initial phase of the adaptive immune response after first antigen encounter provides essential information on the magnitude and quality of the immune response. This phase is characterized by proliferation and dissemination of T cells in the lymphoid organs. Modeling and identifying the key features of this phenomenon may provide a useful tool for the analysis and prediction of the effects of immunization. This knowledge can be effectively exploited in vaccinology, where it is of interest to evaluate and compare the responses to different vaccine formulations. The objective of this paper is to construct a stochastic model based on branching process theory, for the dissemination network of antigen-specific CD4+ T cells. The devised model is validated on in vivo animal experimental data. The model presented has been applied to the vaccine immunization context making references to simple proliferation laws that take into account division, death and quiescence, but it can also be applied to any context where it is of interest to study the dynamic evolution of a population.en
dc.language.isoenen
dc.titleA Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response.en
dc.typeArticleen
dc.contributor.departmentHelmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.en
dc.identifier.journalPloS oneen

Related articles on PubMed

This item is licensed under a Creative Commons License
Creative Commons
All Items in HZI are protected by copyright, with all rights reserved, unless otherwise indicated.