Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients - basic parameters required for implementing the MIIA - are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite.

中文翻译：

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从有限的人口数据预测邻居相关的微生物相互作用。

由于邻居物种的存在而对种间相互作用的调节是控制微生物群落动态和功能的关键生态因素，然而，为理解与环境有关的相互作用而明确建立的理论框架仍处于起步阶段。在最近的研究中，我们提出了一种新的基于规则的推理方法，称为最小种间相互作用调整（MIIA），该方法预测响应于新物种的加入而相互作用网络的重组，从而使由其他成员引起的相互作用系数的调节为最小的。尽管MIIA的理论基础是通过先前的工作而建立的，但前提是假设了在轴生，二元和复杂社区中物种丰度数据的全部可用性，如果物种没有进行无菌培养（例如，由于在没有特定伙伴关系的情况下无法生长），则其对实际微生物生态学的扩展可能会受到高度限制，因为二进制相互作用系数（实施MIIA所需的基本参数）是不可估量的没有焦虑症和二元人口数据。因此，在这里，我们基于以下两个主要思想提出了一种替代的表述。首先，在仅缺少轴心菌培养物的数据的情况下，我们通过适当的缩放从控制方程中删除轴心菌种群。这使我们可以相对地预测依赖于邻居的交互（即，有邻居与无邻居之间交互的分数变化）。其次，如果既缺乏焦虑症人群又缺乏二进制人群，我们通过敏感性分析参数化二元相互作用系数以确定其值。通过对两个具有不同特征和复杂性的微生物群落（即三个成员的所有成员都可以独立生长的群落，以及四个成员的群落中成员的生长依赖于其他物种的群落）进行案例研究，我们证明了尽管存在数据限制，但提出的新配方仍能够成功预测与实验得出的结果一致的种间相互作用。因此，这项技术进步增强了我们在广泛的微生物系统中预测环境相关物种间相互作用的能力，而不必以特定的生长条件为前提。