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# Emergence of elevated levels of multiple infection in spatial host-virus dynamics - Bradford Taylor

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### Emergence of elevated levels of multiple infection in spatial host-virus dynamics - Bradford Taylor

1. 1. Emergence of elevated levels of multiple infection in spatial host- virus dynamics Bradford Taylor1, Catherine Penington2, Joshua Weitz3,1 1School of Physics and 3School of Biology, Georgia Institute of Technology 2School of Mathematical Sciences, Queensland University of Technology http://ecotheory.biology.gatech.edu Quantitative Laws II
2. 2. Multiple infection allows intracellular interactions between viruses La Scola et al, Nature (2008) Coinfection alters ecological parameters
3. 3. Multiple infections alter evolutionary rates due to shared resources Recombination vs. Complementation
4. 4. Multiple infection rates are unknown in vivo
5. 5. How does space affect:  the distribution of multiplicity of infection (MOI)? RM Donlan, Emerg infect dis, 2002
7. 7. Well-mixed model dynamics Host Infected hosts Viruses
8. 8. Well-mixed model yields geometric MOI distribution Host Infected hosts Viruses Geometric distribution for MOI
9. 9. MOI distribution follows geometric distribution for low adsorption Viruses 0 [1 5] [6 10] [11 15] [16 20] >20 Hosts E H I1 C
10. 10. Clustered MOI distributions feature fat tails Hosts E H I1 C Viruses 0 [1 5] [6 10] [11 15] [16 20] >20
11. 11. How many viruses are colocated with a host of a specific MOI? MOI Layer (internal viruses) Viral Layer (external viruses) 11
12. 12. How many viruses are colocated with a host of a specific MOI? MOI Layer (internal viruses) Viral Layer (external viruses) 12
13. 13. How many viruses are colocated with a host of a specific MOI? MOI Layer (internal viruses) Viral Layer (external viruses) 1 13
14. 14. How many viruses are colocated with a host of a specific MOI? MOI Layer (internal viruses) Viral Layer (external viruses) 1 2 14
15. 15. Random dispersal gives Poisson distribution of viruses 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity 15 No clustering clustering
16. 16. 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 Clustered Viral distribution skew right with increasing MOI16 No clustering clustering
17. 17. 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 Clustered Viral distribution skew right with increasing MOI17 No clustering clustering
18. 18. 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 MOI =2 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 MOI =2 Clustered Viral distribution skew right with increasing MOI18 No clustering clustering
19. 19. 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 MOI =2 MOI =3 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 MOI =2 MOI =3 Clustered Viral distribution skew right with increasing MOI19 No clustering clustering
20. 20. 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 MOI =2 MOI =3 MOI =4 MOI =5 MOI =6 0 5 10 Local Viruses 0 0.2 0.4 0.6 0.8 1 ProbabilityDensity MOI =0 MOI =1 MOI =2 MOI =3 MOI =4 MOI =5 MOI =6 Clustered Viral distribution skew right with increasing MOI20 No clustering clustering
21. 21. Well-mixed model yields geometric MOI distribution Host Infected hosts Viruses Deviates from geometric distribution
22. 22. Clustered MOI distributions feature fat tails Hosts E H I1 C Viruses 0 [1 5] [6 10] [11 15] [16 20] >20
23. 23. Multiple infection dynamics driven by invasions of clusters
24. 24. Invasions of larger clusters skews viral distributions Viruses 0 5 10 Probabilitydensity 0 0.2 0.4 0.6 0.8 1 <Viral Distribution> MOI =1 R=3 R=4 R=5 R=7 R=10 24
25. 25. Invasions of larger clusters skews viral distributions Viruses 0 5 10 Probabilitydensity 0 0.2 0.4 0.6 0.8 1 <Viral Distribution> MOI =2 R=3 R=4 R=5 R=7 R=10 Viruses 0 5 10 Probabilitydensity 0 0.2 0.4 0.6 0.8 1 <Viral Distribution> MOI =3 R=3 R=4 R=5 R=7 R=10 Viruses 0 5 10 Probabilitydensity 0 0.2 0.4 0.6 0.8 1 <Viral Distribution> MOI =1 R=3 R=4 R=5 R=7 R=10 25
26. 26. Conclusions  High adsorption leads to clustering  Clustering leads to fat tails in MOI distribution  Cluster invasions drive the dynamics to skew MOI distributions
27. 27. Future work: spatial models of Virophage—viruses of viruses Taylor et al, JTB (2014) Paired Entry Mode (PEM) Independent Entry Mode (IEM) Desnues et al. PNAS (2012) Fischer and Suttle Science (2011)
28. 28. Acknowledgements  Weitz Lab (GaTech)  Funding:  Quantitative Laws II  NSF Physics of Living Systems  James S Mcdonnell Foundation  Nerem Fellowship  Burroughs Wellcome Fund
29. 29. Acknowledgements Questions? Hosts E H I1 C Viruses 0 [1 5] [6 10] [11 15] [16 20] >20 BP Taylor, CJ Penington, JS W bioRxiv: 048876  Weitz Lab (GaTech)  Funding:  Quantitative Laws II  NSF Physics of Living Systems  James S Mcdonnell Foundation  Nerem Fellowship  Burroughs Wellcome Fund
30. 30. Analogous PDE model 31
31. 31. Parametrize based on prochlorococcus single time step System size: 500 x 500 lattice pts=¼ ml 32 Backup slide List processes