Accounting for space - quantification of cell-to-cell transmission kinetics using virus dynamics models
Mathematical models based on ordinary differential equations (ODE) that describe the population dynamics of viruses and infected cells have been an essential tool to characterize and quantify viral infection dynamics. Although an important aspect of viral infection is the dynamics of viral spread, w...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2018
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| In: |
Viruses
Year: 2018, Volume: 10, Issue: 4 |
| ISSN: | 1999-4915 |
| DOI: | 10.3390/v10040200 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.3390/v10040200 Verlag, kostenfrei, Volltext: http://www.mdpi.com/1999-4915/10/4/200 
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| Author Notes: | Peter Kumberger, Karina Durso-Cain, Susan L. Uprichard, Harel Dahari and Frederik Graw |
| Summary: | Mathematical models based on ordinary differential equations (ODE) that describe the population dynamics of viruses and infected cells have been an essential tool to characterize and quantify viral infection dynamics. Although an important aspect of viral infection is the dynamics of viral spread, which includes transmission by cell-free virions and direct cell-to-cell transmission, models used so far ignored cell-to-cell transmission completely, or accounted for this process by simple mass-action kinetics between infected and uninfected cells. In this study, we show that the simple mass-action approach falls short when describing viral spread in a spatially-defined environment. Using simulated data, we present a model extension that allows correct quantification of cell-to-cell transmission dynamics within a monolayer of cells. By considering the decreasing proportion of cells that can contribute to cell-to-cell spread with progressing infection, our extension accounts for the transmission dynamics on a single cell level while still remaining applicable to standard population-based experimental measurements. While the ability to infer the proportion of cells infected by either of the transmission modes depends on the viral diffusion rate, the improved estimates obtained using our novel approach emphasize the need to correctly account for spatial aspects when analyzing viral spread. |
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| Item Description: | Published: 17 April 2018 Gesehen am 15.06.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1999-4915 |
| DOI: | 10.3390/v10040200 |