Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the ‘post antibiotic era.’ Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in in vivo versus in vitro environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of in vitro studies for the in vivo environment, and to streamline lab work. Here, we introduce and review the current status of mathematical modeling and highlight that data on genetic heterogeneity and mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models in the future. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modelers, synergising skills and smoothing the road to regulatory approval and widespread use of phage therapy.

噬菌体疗法，即病毒治疗细菌感染的治疗用途，在“后抗生素时代”具有许多理论优势。尽管如此，目前还没有获得批准的主流噬菌体疗法。原因之一是对噬菌体、细菌和真核宿主之间复杂的相互作用缺乏了解。这些三组分相互作用是复杂的，具有非线性或协同关系、解剖障碍和遗传或表型异质性，所有这些都导致了性能和功效之间的差异。体内 相对 体外环境。这些复杂环境的真实计算机或数学模型是提高预测能力的潜在途径。体外 研究为 体内环境，并简化实验室工作。在这里，我们介绍并回顾了数学建模的现状，并强调遗传异质性和突变随机性、时间延迟和种群密度的数据可能对未来现实噬菌体治疗模型的开发至关重要。考虑到这一点，我们旨在告知和鼓励微生物学家和理论建模者之间知识和专业知识的协作和共享，协同技能并为监管批准和噬菌体疗法的广泛使用铺平道路。