Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-26T04:10:58.294Z Has data issue: false hasContentIssue false

A Branching Process for Virus Survival

Published online by Cambridge University Press:  04 February 2016

J. Theodore Cox*
Affiliation:
Syracuse University
Rinaldo B. Schinazi*
Affiliation:
University of Colorado
*
Postal address: Department of Mathematics, Syracuse University, 215 Carnegie Hall, Syracuse, NY 13244-1150, USA.
∗∗ Postal address: Department of Mathematics, University of Colorado, Colorado Springs, CO 80933-7150, USA. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Quasispecies theory predicts that there is a critical mutation probability above which a viral population will go extinct. Above this threshold the virus loses the ability to replicate the best-adapted genotype, leading to a population composed of low replicating mutants that is eventually doomed. We propose a new branching model that shows that this is not necessarily so. That is, a population composed of ever changing mutants may survive.

Type
Research Article
Copyright
© Applied Probability Trust 

Footnotes

Supported in part by NSF grant 0803517.

References

Eigen, M. (1971). Selforganization of matter and the evolution of biological macromolecules. Naturwissenschaften 58, 465523.CrossRefGoogle ScholarPubMed
Eigen, M. (2002). Error catastrophe and antiviral strategy. Proc. Nat. Acad. Sci. USA 99, 1337413376.CrossRefGoogle ScholarPubMed
Eigen, M. and Schuster, P. (1977). The hypercycle. A principle of self-organization. Part A: emergence of the hypercycle. Naturwissenschaften 64, 541565.CrossRefGoogle Scholar
Elena, S. F. and Moya, A. (1999). Rate of deleterious mutation and the distribution of its effects on fitness in vesicular stomatitis virus. J. Evol. Biol. 12, 10781088.CrossRefGoogle Scholar
Harris, T. E. (1989). The Theory of Branching Processes. Dover Publications, New York.Google Scholar
Karlin, S. and Taylor, H. M. (1975). A First Course in Stochastic Processes, 2nd edn. Academic Press, New York.Google Scholar
Manrubia, S. C., Domingo, E. and Lázaro, E. (2010). Pathways to extinction: beyond the error threshold. Phil. Trans. R. Soc. London B 365, 19431943.CrossRefGoogle ScholarPubMed
Nowak, M. A. and May, R. M. (2000). Virus Dynamics. Oxford University Press.CrossRefGoogle Scholar
Sanjuan, R., Moya, A. and Elena, S. F. (2004). The distribution of fitness effects caused by single-nucleotide substitutions in an RNA virus. Proc. Nat. Acad. Sci. USA 101, 83968401.CrossRefGoogle Scholar
Schinazi, R. B. and Schweinsberg, J. (2008). Spatial and non spatial stochastic models for immune response. Markov Process. Relat. Fields 14, 255276.Google Scholar
Smith, W. L. and Wilkinson, W. E. (1969). On branching processes in random environments. Ann. Math. Statist. 40, 814827.CrossRefGoogle Scholar
Vignuzzi, M. et al. (2006). Quasispecies diversity determines pathogenesis through cooperative interactions in a viral population. Nature 439, 344348 CrossRefGoogle Scholar