Microbial Interaction Networks (MINs) provide important information for understanding bacterial communities. MINs can be inferred by examining microbial abundance profiles. Abundance profiles are often interpreted with the Lotka Volterra model in research. However existing research fails to consider a biologically meaningful underlying mathematical model for MINs or to address the possibility of multiple solutions. In this paper we present IMPARO, a method for inferring microbial interactions through parameter optimisation. We use biologically meaningful models for both the abundance profile, as well as the MIN. We show how multiple MINs could be inferred with similar reconstructed abundance profile accuracy, and argue that a unique solution is not always satisfactory. Using our method, we successfully inferred clear interactions in the gut microbiome which have been previously observed in in-vitro experiments. IMPARO was used to successfully infer microbial interactions in human microbiome samples as well as in a varied set of simulated data. The work also highlights the importance of considering multiple solutions for MINs.

中文翻译：

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IMPARO：通过参数优化推断微生物相互作用。

微生物相互作用网络 (MIN) 为了解细菌群落提供了重要信息。MIN 可以通过检查微生物丰度分布来推断。研究中经常使用 Lotka Volterra 模型来解释丰度分布。然而，现有研究未能考虑 MIN 的具有生物学意义的基础数学模型或解决多种解决方案的可能性。在本文中，我们提出了 IMPARO，一种通过参数优化推断微生物相互作用的方法。我们对丰度曲线和最小值使用具有生物学意义的模型。我们展示了如何以相似的重建丰度剖面精度推断多个 MIN，并认为唯一的解决方案并不总是令人满意。使用我们的方法，我们成功推断出肠道微生物组中明确的相互作用，这些相互作用先前已在体外实验中观察到。IMPARO 用于成功推断人类微生物组样本以及各种模拟数据中的微生物相互作用。这项工作还强调了考虑 MIN 多种解决方案的重要性。