Viral gene drive simulation
This is a R program that perform simple simulations of a viral gene drive. See papers:
- Walter and Verdin, 2020: https://www.nature.com/articles/s41467-020-18678-0).
- Walter et al., 2021: https://journals.asm.org/doi/10.1128/JVI.00802-21.
This is really a work in progress, insuffiently commented i'm aware. So please ask questions and i'll do my best to help.
In brief, the simulation works as follow. 4 type of viruses exist:
- WT: wildtype virus that can be converted to new gene drive virus
- GD: Gene drive virus. If coinfecting a cell with a WT virus, it convert the WT virus into GDo or Res virus
- GDo: Same as GD essentially, except that it is marked as originating from a WT virus. It can convert new WT virus
- R: Virus resistant to the drive, doesn't care about GD and GDo
GD and GDo replicate with a fitness cost f, and convert WT viruses with an efficiency 1-alpha
At each viral generation, NxMOI virus randomly infect N new cells
- If a cell is infected by WT and/or R virus, it produce in average 100 (SD 30, gaussian distribution) viruses in the same proportions (ie. if the cell is infected by 2 WT and 1 R virus, it will produce in average 100 viruses, with a proportion of 2/3 WT - 1/3 R)
- If a cell is infected by GD and/or GDo virus, it produce in average fx100 virus in the same proportion.
- If a cell is infected by WT AND GD or GDo, in average 100 virus are created (no fitness cost), but WT viruses are converted into GDo (proportion 1-alpha) and R virus (proportion alpha)
After doing that for every cells at a given generation, we calculate the new global proportion of the different viruses, and use it to iter to the next generation.
The simulation in Resistant drive is the same principle, except that resistant viruses R grow with the same replicative fitness cost f as GD viruses.