Proper function of a wastewater treatment plant (WWTP) relies on maintaining a delicate balance between a multitude of competing microorganisms. Gaining a detailed understanding of the complex network of interactions therein is essential to maximising not only current operational efficiencies, but also for the effective design of new treatment technologies. Metagenomics offers an insight into these dynamic systems through the analysis of the microbial DNA sequences present. Unique taxa are inferred through sequence clustering to form operational taxonomic units (OTUs), with per-taxa abundance estimates obtained from corresponding sequence counts. The data in this study comprise weekly OTU counts from an activated sludge (AS) tank of a WWTP. To model the OTU dynamics, we develop a Bayesian hierarchical vector autoregressive model, which is a linear approximation to the commonly used generalised Lotka-Volterra (gLV) model. To tackle the high dimensionality and sparsity of the data, they are first clustered into 12 "bins" using a seasonal phase-based approach. The autoregressive coefficient matrix is assumed to be sparse, so we explore different shrinkage priors by analysing simulated data sets before selecting the regularised horseshoe prior for the biological application. We find that ammonia and chemical oxygen demand have a positive relationship with several bins and pH has a positive relationship with one bin. These results are supported by findings in the biological literature. We identify several negative interactions, which suggests OTUs in different bins may be competing for resources and that these relationships are complex. We also identify two positive interactions. Although simpler than a gLV model, our vector autoregression offers valuable insight into the microbial dynamics of the WWTP.

翻译：废水处理厂(WWTP)的适当功能取决于在众多相互竞争的微生物之间保持微妙的平衡。 详细了解其中复杂的相互作用网络对于最大限度地提高当前操作效率至关重要, 并且对于有效设计新的处理技术也至关重要。 Metgenomic 可以通过分析现有的微生物DNA序列对这些动态系统进行洞察。 通过序列组集得出独特的分类法以形成操作的分类单位(OTUs), 从相应的序列计数中得出每税项丰度估计值。 本研究中的数据包括来自WWWTP的激活淤泥(AS)的复杂互动库的OTU每周计数。 为了模拟OTU的动态, 我们开发了一种Bayes级向级向量递增的矢量递增模式模型, 这是通过分析常用的Lotka-Volterra(gLV) 模型, 能够直线接近这些数据, 通过一个更简单的阶段计数, 自动递增系数矩阵的模型可以识别到12个“bindes ” 。 我们的自动递增系数矩阵的模型可以测量到两个不同的 。