Stochastic Population Genetic Models of Chronic Pathogens

了解导致慢性感染的寄生虫的进化是复杂的，因为相关的群体遗传过程（选择和人口统计随机性）发生在受感染的宿主内部和之间，并在可比的时间尺度上运作。虽然有时可以模拟急性感染，就好像它们在进化时间尺度上是瞬时的，但当模拟时间依赖性性状的进化时，这种简化将是不合适的，例如由偶然位点控制的性状，或者在多阶段寄生虫生命周期过程中表达有限的性状。在这里，我们提出了一些模型，利用测量值的随机过程，我们相信捕捉一些相关的慢性感染的生物学现象，但仍然在数学上易于处理。我们认为这两种流行病，我们建议模型使用分支马尔可夫过程，和地方性感染，可以使用各种严格的和启发式的近似一类measurevaluedgeneralizations的莫兰模型进行研究。然后，我们描述了如何使用这些模型来研究三个具体的现象：（一）在有限的人口毒力的演变;（二）主机的异质性和多样化的选择HIV-1;和（iii）假基因的积累和非洲锥虫的VSG抗原库的演变。

Understanding the evolution of parasites which cause chronic infections is complicated by the fact that the relevant population genetic processes (selection and demographic stochasticity) occur both within and among infected hosts and operate on comparable time scales. Whereas it may sometimes be possible to model acute infections as though they were instantaneous over evolutionary time scales, such simplifications will be unsuitable when modeling the evolution of time-dependent traits such as those controlled by contingency loci or which have limited expression over the course of multi-stage parasite life cycles. Here we propose a number of models making use of measure-valued stochastic processes which we believe capture some of the biological phenomena relevant to chronic infections yet remain mathematically tractable. We consider both epidemics, which we propose to model using branching Markov processes, and endemic infections, which can be studied using various rigorous and heuristic approximations to a class of measure-valuedgeneralizations of the Moran model. We then describe how these models can be used to study three concrete phenomena: (i) the evolution of virulence in finite populations; (ii) host heterogeneity and diversifying selection on HIV-1; and (iii) pseudogene accumulation and the evolution of the VSG antigen repertoire in African trypanosomes.