Research School of Electrical, Energy and Materials Engineering, College of Engineering & Computer Science, Australian National University, Acton, 2601, Australia.
Biodiversity Research Center, Academia Sinica, Nan-Kang, Taipei, 11529, Taiwan.
BMC Mol Cell Biol. 2020 Aug 19;21(Suppl 1):34. doi: 10.1186/s12860-020-00269-y.
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.
微生物相互作用网络（MINs）为理解细菌群落提供了重要信息。MINs 可以通过检查微生物丰度谱来推断。在研究中，丰度谱通常用洛特卡-沃尔泰拉模型来解释。然而，现有研究未能考虑 MINs 的生物学意义上的基本数学模型，也未能解决多个解决方案的可能性。
在本文中，我们提出了 IMPARO，这是一种通过参数优化推断微生物相互作用的方法。我们同时使用了丰度谱和 MIN 具有生物学意义的模型。我们展示了如何用相似的重建丰度谱准确性推断出多个 MINs，并认为单一解决方案并不总是令人满意的。使用我们的方法，我们成功地推断出了肠道微生物组中以前在体外实验中观察到的明确相互作用。
IMPARO 成功地推断了人类微生物组样本以及一系列不同的模拟数据中的微生物相互作用。这项工作还强调了考虑 MINs 的多个解决方案的重要性。
