ArticleJournal of theoretical biology2016
A spatial model of the efficiency of T cell search in the influenza-infected lung.
Article in Journal of theoretical biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Spatial Immune Model of Alveolar Lung Infection (SIMALI) Identifies Structural Determinants of Lung Inflammation.Research square · 2026Article
- Spatially structured models of viral dynamics: a scoping review.Microbiology and molecular biology reviews : MMBR · 2025Article
- Quantitatively Mapping Immune Control during Influenza.Current opinion in systems biology · 2024Article
- An agent-based modeling approach for lung fibrosis in response to COVID-19.PLoS computational biology · 2023Article
- Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection.Biophysical journal · 2022Article
- Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations.Frontiers in physiology · 2022Review
- Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection.PLoS computational biology · 2021Article
- Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity.eLife · 2021Article
- Dynamic Perspectives on the Search for a Universal Influenza Vaccine.The Journal of infectious diseases · 2019Article
- Immunobiochemical Reconstruction of Influenza Lung Infection-Melanoma Skin Cancer Interactions.Frontiers in immunology · 2019Article
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.