The process of evolution is a consequence of the interplay of mutation, selection, and chance on a population of organisms, leading to an observable change in its genetic makeup...
...because of their high replication rates, simple genomes, large population sizes, and high mutation rates, viruses make good models for studying and testing evolutionary theory.
HIV displays a remarkable extent of genetic variation concurrent with a high speed of evolution: in the most variable region of the genome (env), individual genomes within a population from an infected person can vary by as much as 3 to 5% (2, 43, 78); substitutions in env accumulate at a rate of approximately 1% per year (71), 50 million times faster than in the small subunit of rRNA (61). This variation has important consequences. It allows the virus to evolve to infect different cell types (9, 20, 30) and to rapidly become resistant to otherwise highly effective antiviral drugs (10, 47, 50); it may play a role in evading the immune system (4, 56, 73, 79). Furthermore, its high mutation rate (estimated to average about 3 3 1025 per nucleotide site per replication cycle [49]), large population size (variously estimated from about 107 to 108 productively infected cells), and continuous steady state, in which the large majority of virions and productively infected cells turns over every day (25, 77), create a situation which, at least in principle, is amenable to (and requires) mathematical modeling.
Deterministic approaches, including quasispecies theory (15, 26), assume that the population size is very large, such that the frequency of a given mutation at any given time is completely predictable if one knows the initial frequency, the mutation rate, and the selection coefficient (i.e., the differential growth rate conferred by the different alleles). At first glance, such approaches would seem justified by the large number of infected cells at each generation (21); however, a number of factors, such as variation in the replication potential and generation times among infected cells, may lead to an effective population size much smaller than the actual number of infected cells. Stochastic models, as applied to HIV (to this point), proceed from the opposite assumption: that the effective population size is so small (or that selective forces are so weak) that random drift dominates over selection. The hypothesis of selectively neutral mutations has a long, successful history in describing the evolution of organisms where populations are small (and not uniformly distributed) and mutation rates are very low (36). Their applicability to virus populations remains to be established. Many of the assumptions that underlie neutral theory are not appropriate for virus populations, and a number of characteristics of HIV genetic variation in vivo, such as the uneven ratio of synonymous to nonsynonymous changes in different regions of the genome (5, 44, 48), argue against simple application of neutral theory. However, inclusion of selection effects in evolutionary analysis (for example, the coalescent method) presents a mathematical challenge that has not yet been fully solved in a practical fashion, although progress toward this goal has been made recently (42, 55).