Spatio-temporal dynamics of Host-Virus competition: A model study of influenza A

John Whitman, Aleya Dhanji, Fernand Hayot, Stuart C. Sealfon, Ciriyam Jayaprakash

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

We present results of a study of the early-time response of the innate immune system to influenza virus infection in an agent-based model (ABM) of epithelial cell layers. We find that the competition between the anti-viral immune response and viral antagonism can lead to viral titers non-monotonic in the initial infection fraction as found in experiments. Our model includes a coarse-grained version of intra-cellular processes and inter-cellular communication via cytokine and virion diffusion. We use ABM to follow the propagation of viral infection in the layer and the increase of the viral load as a function of time for different values of the multiplicity of infection (MOI), the initial number of viruses added per cell. We find that for moderately strong host immune response, the number of infected cells and viral load for a smaller MOI exceeds that for larger MOI, as seen in experiments. We elucidate the mechanism underlying this result as the synergistic action of cytokines secreted by infected cells in controlling viral amplification for larger MOI. We investigate the length and time scales that determine this non-monotonic behavior within the ABM. We study the diffusive spread of virions and cytokines from a single infected cell in an absorbing medium analytically and numerically and deduce the length scale that yields a good estimate of the MOI at which we find non-monotonicity. Detailed computations of the temporal behavior of averaged quantities and spatial measures provide further insights into host-viral interactions and connections to experimental observations.

Original languageEnglish
Article number110026
JournalJournal of Theoretical Biology
Volume484
DOIs
StatePublished - 7 Jan 2020

Keywords

  • Agent-based modeling
  • Epithelial cells
  • Innate immune system
  • Modeling flu
  • Multiplicity of infection
  • Stochastic modeling

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