In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

结核分枝杆菌（Mycobacterium tuberculosis, Mtb）感染体外模型是探究宿主-病原体相互作用与药物筛选的重要工具。随着愈发复杂的体外模型的发展，亟需能够辅助分析与整合此类模型产生数据的工具。为此，我们提出了一种基于智能体的模型（agent-based model, ABM），用于表征体外环境中免疫细胞与细菌之间的相互作用。该计算模拟模型可通过调整细胞的运动规则与初始空间排布，适配对应的体外模型，从而模拟传统细胞培养模型与球体细胞培养模型。我们采用配对校准方式，在两组模拟中使用完全相同的参数，将传统培养与球体培养的模拟结果与已发表的实验数据进行拟合校准。在校准完成的模拟中，细菌载量与T细胞向球体巨噬细胞核心的浸润情况均呈现出异质性输出结果。该模拟还作出了多项预测：激活巨噬细胞的数量相等时，未必能带来相近的细菌清除效果；宿主免疫应答可通过依赖与不依赖球体结构的两种方式调控细菌生长；信号转导与转录激活因子1（signal transducer and activator of transcription 1, STAT1）的激活是球体模型中巨噬细胞激活的限速步骤；药物筛选与巨噬细胞激活研究的实验结果，会因所采用的体外培养体系不同而存在差异。未来的模型迭代将基于当前模型的局限性进行优化，尤其是针对当前模拟输出空间中较难覆盖的区域。由于该基于智能体的模型结构灵活，可用于表征更多类型的结核分枝杆菌感染体外模型，从而加速体外实验相关研究的进展。