Abstract

Viruses are fast evolving pathogens that continuously adapt to the highly variable environments they live and reproduce in. Strategies devoted to inhibit virus replication and to control their spread among hosts need to cope with these extremely heterogeneous populations and with their potential to avoid medical interventions. Computational techniques such as phylogenetic methods have broadened our picture of viral evolution both in time and space, and mathematical modeling has contributed substantially to our progress in unraveling the dynamics of virus replication, fitness, and virulence. Integration of multiple computational and mathematical approaches with experimental data can help to predict the behavior of viral pathogens and to anticipate their escape dynamics. This piece of information plays a critical role in some aspects of vaccine development, such as viral strain selection for vaccinations or rational attenuation of viruses. Here we review several aspects of viral evolution that can be addressed quantitatively, and we discuss computational methods that have the potential to improve vaccine design.

Highlights

  • Viruses are fast evolving pathogens that continuously adapt to the highly variable environments they live and reproduce in

  • We summarize some computational and mathematical techniques that play a critical role in understanding viral evolution and vaccine design

  • The results showed a slight trend of protection in the vaccinated group when compared to the control group. These numbers are still debated [100] and vaccination did not affect the degree of viremia or CD4+ T cell count in human immunodeficiency virus (HIV) infected subjects, the results offer insight for future research

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Summary

Conclusions

Viral evolution and the genetic diversity it produces are fundamental factors for the success of vaccine candidates, because immune responses need to be stimulated against a potentially very broad spectrum of existing viruses and new viral immune escape variants are likely to be generated. Mathematical modeling of viral evolutionary dynamics will play an increasingly important role in vaccine design. It can identify genomic regions that are under selective pressure, support the selection or construction of vaccine strains, predict evolutionary escape from immune pressure, guide vaccination campaigns, estimate the effect of therapeutic vaccines, and support the design of new attenuation strategies. The distinct evolutionary dynamics of influenza A and HIV-1, two of the most widely studied RNA viruses, have highlighted the need for careful analysis of viral infection dynamics within and among individuals. List of abbreviations used HIV: human immunodeficiency virus; HCV: hepatitis C virus; HVB: hepatitis B virus; RSV: respiratory syncytial virus; SIV: simian immunodeficiency virus; FMDV: foot-and-mouth disease virus; Ad5: adenovirus serotype 5 vector; LAV: live attenuated vaccine; IV: inactivated vaccine; NA: neuraminidase; HA: hemagglutinin; MHC: major histocompatibility complex; UPGMA: Unweighted Pair Group Method with Arithmetic mean; MRCA: most recent common ancestor; CTL: cytotoxic T lymphocyte; HLA: human leukocyte antigen; LD: linkage disequilibrium; PDNs: phylogenetic dependency networks; CBNs: conjunctive Bayesian networks; ODE: ordinary differential equations; HI: herd immunity

33. Moscona A
36. Sabin AB
44. Couch RB
47. Rowlands DJ
53. Mascola JR
70. Kingman JFC
77. Felsenstein J

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