BACKGROUND The dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to directly learn the ecological models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is severely limited by the lack of accurate absolute cell density measurements (biomass). METHODS We present a new computational approach that resolves this key limitation in the inference of generalized Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference with an expectation-maximization algorithm (BEEM). RESULTS BEEM outperforms the state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM's application to previously inaccessible public datasets (due to the lack of biomass data) allowed us to construct ecological models of microbial communities in the human gut on a per-individual basis, revealing personalized dynamics and keystone species. CONCLUSIONS BEEM addresses a key bottleneck in "systems analysis" of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements.

中文翻译：

---

期望最大化算法可以使用纵向微生物组测序数据进行精确的生态建模。

背景技术微生物群落的动力学是由共生关系到捕食者－猎物关系的一系列相互作用所驱动的，而大多数相互作用却鲜为人知。随着高通量微生物分类学概况分析数据可用性的提高，现在可以想到直接学习明确定义微生物相互作用并解释群落动态的生态模型。由于缺乏准确的绝对细胞密度测量值（生物量），这些方法的应用受到了严重限制。方法我们提出了一种新的计算方法，该方法通过将生物量估计和模型推论与期望最大化算法（BEEM）结合，解决了广义Lotka-Volterra模型（gLVM）推论中的这一关键限制。结果BEEM优于最新的推断gLVM的方法，同时消除了对其他实验生物量数据作为输入的需求。BEEM对以前无法访问的公共数据集的应用（由于缺乏生物量数据）使我们能够在每个人的基础上构建人类肠道微生物群落的生态模型，从而揭示个性化的动态和关键物种。结论BEEM通过启用高通量测序数据的准确生态模型推断而无需进行实验性生物量测量，从而解决了微生物群落“系统分析”中的关键瓶颈。s应用于以前无法访问的公共数据集（由于缺乏生物量数据），使我们能够在每个人的基础上构建人类肠道微生物群落的生态模型，从而揭示个性化的动力学和关键物种。结论BEEM通过启用高通量测序数据的准确生态模型推断而无需进行实验性生物量测量，从而解决了微生物群落“系统分析”中的关键瓶颈。s应用于以前无法访问的公共数据集（由于缺乏生物量数据），使我们能够在每个人的基础上构建人类肠道微生物群落的生态模型，从而揭示个性化的动力学和关键物种。结论BEEM通过启用高通量测序数据的准确生态模型推断而无需进行实验性生物量测量，从而解决了微生物群落“系统分析”中的关键瓶颈。