Modelling Viral Evolution and Adaptation

Abstract

Viral populations are extremely plastic [5]. They maintain and steadily generate high levels of genotypic and phenotypic diversity that result in the coexistence of several different viral types in quasi-species, and eventually constitute a powerful tool to deploy different adaptive strategies. The interest in understanding and formally describing viral populations has steadily increased. At present, there are major unknown factors that difficult the construction of realistic models of viral evolution, as the way in which mutations affect fitness [19] or, in a broader scenario, which is the statistical nature of viral fitness landscapes. Our understanding of viral complexity is however improving thanks to new techniques as deep sequencing [17] or massive computation, and to systematic laboratory assays that reveal that, as other complex biological systems (e.g., cancer or ecosystems) the term virus embraces a dissimilar collection of populations with a remarkable ensemble of evolutionary strategies. New empirical data and improved models of viral dynamics are clearing up the role played by neutral networks of genotypes [21], by defective and cooperative interactions among viral mutants [13], by co-evolution with immune systems [22], or by changes in host populations [10], to cite but a few examples. Models of viral evolution are steadily improving their accuracy and becoming more competent from a conceptual and a predictive viewpoint [11, 12]. Here, we review some examples were well-motivated models of viral evolution succeed at capturing experimentally described features of those populations. Such are the relationship between intra-species competition and the geometry of the propagating substrate of a viral infection [3], the origin of bipartite viral genomes [8], and the adaptation to multi-drug therapies [9, 16].