Literature DB >> 26920246

A spatial model of the efficiency of T cell search in the influenza-infected lung.

Drew Levin1, Stephanie Forrest2, Soumya Banerjee2, Candice Clay3, Judy Cannon4, Melanie Moses2, Frederick Koster5.   

Abstract

Emerging strains of influenza, such as avian H5N1 and 2009 pandemic H1N1, are more virulent than seasonal H1N1 influenza, yet the underlying mechanisms for these differences are not well understood. Subtle differences in how a given strain interacts with the immune system are likely a key factor in determining virulence. One aspect of the interaction is the ability of T cells to locate the foci of the infection in time to prevent uncontrolled expansion. Here, we develop an agent based spatial model to focus on T cell migration from lymph nodes through the vascular system to sites of infection. We use our model to investigate whether different strains of influenza modulate this process. We calibrate the model using viral and chemokine secretion rates we measure in vitro together with values taken from literature. The spatial nature of the model reveals unique challenges for T cell recruitment that are not apparent in standard differential equation models. In this model comparing three influenza viruses, plaque expansion is governed primarily by the replication rate of the virus strain, and the efficiency of the T cell search-and-kill is limited by the density of infected epithelial cells in each plaque. Thus for each virus there is a different threshold of T cell search time above which recruited T cells are unable to control further expansion. Future models could use this relationship to more accurately predict control of the infection.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Agent-based model; Computational biology; Immunology; Systems biology; Virology

Mesh:

Substances:

Year:  2016        PMID: 26920246      PMCID: PMC4862360          DOI: 10.1016/j.jtbi.2016.02.022

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


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