With increasing resolution of microbial diversity at the genomic level, experimental and modeling frameworks that translate such diversity into phenotypes are highly needed. This is particularly important when comparing drug-resistant with drug-sensitive pathogen strains, when anticipating epidemiological implications of microbial diversity, and when designing control measures. Classical approaches quantify differences between microbial strains using the exponential growth model, and typically report a selection coefficient for the relative fitness differential between two strains. The apparent simplicity of such approaches comes with the costs of limiting the range of biological scenarios that can be captured, and biases strain fitness estimates to polarized extremes of competitive exclusion. Here, we propose a mathematical and statistical framework based on the Lotka-Volterra model, that can capture frequency-dependent competition between microbial strains within-host and upon transmission. As a proof-of-concept, the model is applied to a previously-published dataset from in-vivo competitive mixture experiments with influenza strains in ferrets (McCaw et al., 2011). We show that for the same data, our model predicts a scenario of coexistence between strains, and supports a higher bottleneck size in the range of 35–145 virions transmitted from donor to recipient host. Thanks to its simplicity and generality, such framework could be applied to other ecological scenarios of microbial competition, enabling a more complex and nuanced view of possible outcomes between two strains, beyond competitive exclusion.

随着基因组水平上微生物多样性的分辨率的提高，迫切需要将这种多样性转化为表型的实验和建模框架。当将耐药性与药物敏感的病原体菌株进行比较，预期微生物多样性的流行病学意义以及设计控制措施时，这一点尤其重要。经典方法使用指数增长模型量化微生物菌株之间的差异，并且通常报告两种菌株之间相对适应性差异的选择系数。这种方法的明显简单性是伴随着限制可捕获的生物场景范围的代价，并且使应变适应性估计值偏向竞争性排斥的极端。这里，我们提出了一种基于Lotka-Volterra模型的数学和统计框架，该框架可以捕获宿主内部微生物和传播时微生物菌株之间的频率依赖性竞争。作为一个概念证明，该模型将应用于之前发布的数据集 体内 在雪貂中进行流感毒株的竞争性混合实验（McCaw等， 2011年）。我们表明，对于相同的数据，我们的模型预测了菌株之间共存的情况，并支持了从供体传递到受体宿主的35–145病毒体范围内更大的瓶颈。由于其简单性和通用性，该框架可以应用于微生物竞争的其他生态场景，从而使人们能够更复杂，更细致地了解两种菌株之间可能产生的结果，而不仅仅是竞争排斥。