Title: Microbiome abundance patterns as attractors and the implications for the inference of microbial interaction networks Show Chinese title

Title: 微生物组丰度模式作为吸引子及其对微生物相互作用网络推断的影响

Abstract: Inferring microbial interaction networks from abundance patterns is an important approach to advance our understanding of microbial communities in general and the human microbiome in particular. Here we suggest discriminating two levels of information contained in microbial abundance data: (1) the quantitative abundance values and (2) the pattern of presences and absences of microbial organisms. The latter allows for a binary view on microbiome data and a novel interpretation of microbial data as attractors, or more precisely as fixed points, of a Boolean network. Starting from these attractors, our aim is to infer an interaction network between the species present in the microbiome samples. To accomplish this task, we introduce a novel inference method that combines the previously published ESABO (Entropy Shifts of Abundance vectors under Boolean Operations) method with an evolutionary algorithm. The key idea of our approach is that the inferred network should reproduce the original set of (observed) binary abundance patterns as attractors. We study the accuracy and runtime properties of this evolutionary method, as well as its behavior under incomplete knowledge of the attractor sets. Based on this theoretical understanding of the method we then show an application to empirical data. Show Chinese abstract

Abstract: 从丰度模式推断微生物相互作用网络是推进我们对微生物群落一般理解和人类微生物组特别理解的重要方法。 在这里，我们建议区分微生物丰度数据中包含的两个层次的信息：(1) 定量的丰度值和(2)微生物生物的存在和缺失模式。 后者允许对微生物组数据进行二进制视图，并将微生物数据新颖地解释为布尔网络的吸引子，或更准确地说，作为固定点。 从这些吸引子出发，我们的目标是推断微生物组样本中存在物种之间的相互作用网络。 为了完成这个任务，我们引入了一种新的推断方法，该方法结合了之前发表的ESABO（布尔运算下丰度向量的熵变化）方法与进化算法。 我们方法的关键思想是，推断的网络应能再现原始的（观察到的）二进制丰度模式作为吸引子。 我们研究了这种进化方法的准确性和运行时间特性，以及在对吸引子集知识不完整时的行为。 基于对该方法的理论理解，我们随后展示了其在实证数据上的应用。