1. In recent times, interest has grown in understanding how microbiomes – the collection of microorganisms in a specific environment – influence the survivability or fitness of their plant and animal hosts. The profound diversity of bacterial and fungal species found in certain environments, such as soil, provides a large pool of potential microbial partners that can interact in ways that reveal patterns of associations linking host–microbiome traits developed over time. However, most microbiome sequence data are reported as a community fingerprint, without analysis of interaction networks across microbial taxa through time.
2. To address this knowledge gap, more robust tools are needed to account for microbiome dynamics that could signal a beneficial change to a plant or animal host. In this paper, we discuss applying mathematical tools, such as dynamic network modelling, which involves the use of longitudinal data to study system dynamics and microbiomes that identify potential alterations in microbial communities over time in response to an environmental change. In addition, we discuss the potential challenges and pitfalls of these methodologies, such as handling large amounts of sequencing data and accounting for random processes that influence community dynamics, as well as potential ways to address them.
3. Ultimately, we argue that components of microbial community interactions can be characterized through mathematical models to reveal insights into complex dynamics associated with a plant or animal host trait. The inclusion of interaction networks in microbiome studies could provide insights into the behaviour of complex communities in tandem with host trait modification and evolution.

中文翻译：

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动态网络建模可用于识别适应性微生物组吗？

1. 近年来，人们越来越了解微生物组（特定环境中的微生物收集）如何影响其动植物的生存能力或适应性。在某些环境（例如土壤）中发现的细菌和真菌种类的丰富多样性，提供了大量潜在的微生物伴侣，它们可以通过相互作用的方式揭示连接宿主-微生物组特征的关联模式。但是，大多数微生物组序列数据被报告为群落指纹，而没有分析整个微生物群之间的相互作用网络。
2. 为了解决这一知识鸿沟，需要更强大的工具来解决微生物组动态问题，这可能会向植物或动物宿主发出有益的信号。在本文中，我们讨论了数学工具的应用，例如动态网络建模，其中涉及使用纵向数据来研究系统动力学和微生物群落，以识别随着时间的变化，微生物群落随时间变化的潜在变化。此外，我们还讨论了这些方法的潜在挑战和陷阱，例如处理大量测序数据和说明影响社区动态的随机过程，以及解决这些问题的潜在方法。
3. 最终，我们认为微生物群落相互作用的组成可以通过数学模型来表征，以揭示对与植物或动物宿主性状相关的复杂动力学的见解。在微生物组研究中包括相互作用网络可以提供洞察复杂特征与宿主性状的修饰和进化相结合的行为。