Models of RNA virus evolution and their roles in vaccine design

Ojosnegros, Samuel; Beerenwinkel, Niko

HIV mutational pathways have also been modeled in the context of evolutionary escape from the selective pressure of antiretroviral therapy. Several probabilistic models have been developed for describing the accumulation of amino acid changes in response to specific drugs or drug combinations, including Markov chains [124] , Bayesian networks [125] , mutagenetic trees [126] [127] [128] , and conjunctive Bayesian networks (CBNs) [129] . CBNs are a class of probabilistic graphical models that describe the order in which mutations occur. In this model, the partial order of mutations that maximizes the likelihood of the data can be learned efficiently from observed mutational patterns. CBNs allow for several escape paths with different probabilities and the partial order restricts the viral genotype space to the subset of those mutational patterns compatible with the partial order constraints (Figure 1 ). The rationale for this approach is that many combinations of mutations are never observed, for example, because they result in inviable viruses, or because they are too far away in sequence space from current strains to be reached on the relevant time scale. CBNs can explicitly represent the timeline of mutations occurring along escape pathways [130] and they have been extended to account for noisy observations [131] .