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Investigating the well-mixed assumption in viral infection dynamics

January 30, 2006

  • Date: Tuesday, January 30, 2006 
  • Time: 11:00 am — 12:15 pm.
  • Place: Woodward 149

Catherine Beauchemin
Department of Computer Science, UNM

In the past, viral kinetics has been typically studied through the use of spatially well-mixed ordinary differential equations which describe the number of cells of each type as a function of time only, assuming that those cells are uniformly distributed in space. In the real system, however, spatial heterogeneities such as localized pockets of dead cells can emerge over the course of the infection which can affect the spread of infection not unlike a counter-fire which can stop a forest fire from spreading.

In order to investigate the possible role of spatial heterogeneities on the development and outcome of a viral infection, I propose a simple agent-based model (ABM). I will show that the model is complex enough to reproduce the general shape of a response to an uncomplicated viral infection, and that it gives quantitatively reasonable results when calibrated for the particular case of influenza A. I will then use the ABM to investigate the effects of relaxing the well-mixed assumption. Particularly, the effects of the initial distribution of infected cells, the regeneration rule for dead epithelial cells, and the proliferation rule for immune cells are explored and shown to have an important impact on the development and outcome of the viral infection in our model.

Finally, if time permits, I will also present results from an analysis of published T cell track data captured by two-photon microscopy experiments in vivo.

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