Progress in shortening the duration of tuberculosis (TB) treatment is hampered by the lack of a predictive model that accurately reflects the diverse environment within the lung. This is important as TB has been shown to produce distinct localisations to different areas of the lung during different disease stages, with the environmental heterogeneity within the lung of factors such as air ventilation, blood perfusion and oxygen tension believed to contribute to the apical localisation witnessed during the post-primary form of the disease.

Building upon our previous model of environmental lung heterogeneity, we present a networked metapopulation model that simulates TB across the whole lung, incorporating these notions of environmental heterogeneity across the whole TB life-cycle to show how different stages of the disease are influenced by different environmental and immunological factors. The alveolar tissue in the lung is divided into distinct patches, with each patch representing a portion of the total tissue and containing environmental attributes that reflect the internal conditions at that location. We include populations of bacteria and immune cells in various states, and events are included which determine how the members of the model interact with each other and the environment. By allowing some of these events to be dependent on environmental attributes, we create a set of heterogeneous dynamics, whereby the location of the tissue within the lung determines the disease pathological events that occur there.

Our results show that the environmental heterogeneity within the lung is a plausible driving force behind the apical localisation during post-primary disease. After initial infection, bacterial levels will grow in the initial infection location at the base of the lung until an adaptive immune response is initiated. During this period, bacteria are able to disseminate and create new lesions throughout the lung. During the latent stage, the lesions that are situated towards the apex are the largest in size, and once a post-primary immune-suppressing event occurs, it is the uppermost lesions that reach the highest levels of bacterial proliferation. Our sensitivity analysis also shows that it is the differential in blood perfusion, causing reduced immune activity towards the apex, which has the biggest influence of disease outputs.

缺乏准确反映肺内不同环境的预测模型阻碍了缩短结核病 (TB) 治疗持续时间的进展。这一点很重要，因为结核病已被证明在不同疾病阶段对肺部不同区域产生不同的定位，而肺内环境的异质性，如空气通气、血液灌注和氧张力等因素被认为有助于心尖定位在疾病的初级形式期间。

在我们之前的环境肺异质性模型的基础上，我们提出了一个模拟整个肺的 TB 的网络化元种群模型，将这些环境异质性概念纳入整个 TB 生命周期，以显示疾病的不同阶段如何受到不同环境的影响和免疫因素。肺中的肺泡组织被分成不同的斑块，每个斑块代表总组织的一部分，并包含反映该位置内部条件的环境属性。我们包括处于各种状态的细菌和免疫细胞群，并且包括确定模型成员如何与彼此和环境相互作用的事件。通过允许其中一些事件依赖于环境属性，

我们的研究结果表明，肺内的环境异质性是原发后疾病心尖定位背后的一个可能的驱动力。初始感染后，细菌水平将在肺底部的初始感染位置增加，直到启动适应性免疫反应。在此期间，细菌能够在整个肺部传播并产生新的病变。在潜伏期，位于顶端的病灶最大，一旦发生原发性免疫抑制事件，细菌增殖水平最高的病灶是最上面的病灶。我们的敏感性分析还表明，这是血液灌注的差异，导致对顶点的免疫活性降低，这对疾病输出的影响最大。