The fitness landscape of a virus inferred based on a mathematical model of the population dynamics

我们研究了具有拮抗作用的多效性突变（即，抗体逃避与受体结合）对宿主内病毒复制的影响可以影响宿主群体中的病毒免疫逃避。为了研究病毒基因组突变与病毒种群适应性进化之间的因果关系，我们建立了一个数学模型。利用群体遗传学理论，我们评估了突变体在宿主群体中被固定的概率。可以以高概率固定的突变可能决定疫苗保持有效的时间。我们模拟了严重急性呼吸综合征的病原体冠状病毒的适应性进化，并表明某些突变可能在接种疫苗的宿主群体中具有较高的固定概率。

We examined how pleiotropic mutations that have antagonistic effects (i.e., antibody evasion vs. receptor binding) on viral replication within hosts can impact viral immune escape in the host population. To investigate the causal relation between a mutation in a viral genome and adaptive evolution of a viral population, we developed a mathematical model. Using population genetic theory, we evaluated the probability that a mutant is fixed in a host population. The mutations that can be fixed with high probabilities may determine how long a vaccine remains effective. We simulated the adaptive evolution of coronavirus, the etiological agent of severe acute respiratory syndrome, and showed that some of mutations may have high fixation probabilities in the vaccinated host population.