Collaborative Research: MIM: Using multilayer interaction networks to predict microbiome assembly and function

微生物在地球上的大多数生态系统中发挥着重要作用，并为植物，动物和人类的福祉做出贡献。了解微生物群落结构和功能对于我们有能力（1）管理人类和动物的健康和植物生产力以及（2）预测生态系统对环境变化的反应是必要的。因此，迫切需要了解微生物如何相互作用以及与环境（包括其植物，动物或人类宿主）的相互作用。在这个项目中，研究人员将联合收割机数学模型和数据分析相结合，以揭示控制微生物群落和物种相互作用的结构和动态的机制。他们的方法的新奇在于，它将不同复杂性的模型与不同宿主物种的微生物动力学数据相结合，以指导这些模型的构建、探索和测试。研究人员将开发简单的模型来明确描述微生物小群体之间不同的积极和消极相互作用，例如资源竞争，化学物质的互利交换或毒素抑制。他们还将开发多物种、多层网络模型，以反映微生物群落的复杂性，并探索它们对不同干扰（特别是病原体入侵、抗生素治疗或资源水平改变）的反应。该项目的结果将提高对微生物群落如何发挥作用以及如何对其进行修改以改善动物，植物和人类健康的理解。博士后研究人员和学生将接受微生物生态学，数学建模和网络科学的跨学科培训。研究人员将通过出版物、教学和演示向其他科学家和公众提供该项目的方法和结果。该项目结合了生态学、数学和物理学的理论框架--包括基于资源的生态学、多层网络分析和元共生理论--以获得对微生物相互作用如何决定群落动态和功能的机理性理解。研究人员将开发基于资源的微生物相互作用模型，并使用多层网络理论将相互作用模块集成到异构网络结构中，包括多个关系，多个子系统，多个尺度和时间依赖性。他们将使用张量形式主义来跟踪层内和层间的相互作用。他们的模型将探索不同的微生物相互作用和环境条件如何影响群落结构、稳定性、恢复力和功能，并利用宿主相关微生物组的现有数据集测试他们的模型。该项目将促进我们对微生物群落生态学的理解，并帮助解决管理不同微生物组以获得宿主和/或生态系统效益的问题。动态多物种模型和多层网络的分析将适用于其他自然和社会系统。研究人员将通过将模型和代码存放在公共存储库中来为开放科学做出贡献。他们将把项目结果纳入本科和研究生课程，包括MSU-UCLA分布式研讨会。他们还将开发基于调查的微生物群落教育模块，以提高K-12学生的定量推理技能和微生物生态学知识。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Microbes play important roles in most ecosystems on Earth and contribute to plant, animal, and human well-being. Understanding microbial community structure and function is necessary for our ability to (1) manage human and animal health and plant productivity and (2) predict ecosystem responses to environmental changes. Consequently, there is an urgent need to understand how microbes interact with each other and with the environment (including their plant, animal, or human hosts). In this project, the researchers will combine mathematical models and data analyses to uncover the mechanisms that govern the structure and dynamics of microbial communities and species interactions. The novelty of their approach is that it integrates models of different complexity with data on microbial dynamics in different host species to guide the construction, exploration, and testing of those models. The researchers will develop simple models to explicitly describe different positive and negative interactions among small groups of microbes, such as competition for resources, mutually beneficial exchange of chemicals, or inhibition by toxins. They will also develop multispecies, multilayer network models that reflect the complexity of microbial communities and explore their responses to different disturbances (specifically, invasions by pathogens, antibiotic treatments, or resource-level alterations). The results of this project will improve understanding of how microbial communities function and how they can be modified to improve animal, plant, and human health. Postdoctoral researchers and students will receive interdisciplinary training in microbial ecology, mathematical modeling, and network science. The researchers will make the approaches and the results of the project available through publications, teaching, and presentations to other scientists and the public.The project combines theoretical frameworks from ecology, mathematics, and physics — including resource-based ecology, multilayer network analysis, and metacommunity theory — to gain a mechanistic understanding of how microbial interactions determine community dynamics and function. The researchers will develop resource-based models of microbial interactions, and use multilayer network theory to integrate the interaction modules in heterogeneous network structures that include multiple relations, multiple subsystems, multiple scales, and time-dependence. They will use a tensorial formalism to keep track of both intralayer and interlayer interactions. Their models will explore how different microbial interactions and environmental conditions influence community structure, stability, resilience, and function, and test their models with existing data sets on host-associated microbiomes. The project will advance our understanding of microbial community ecology and help address the problems of managing diverse microbiomes for host and/or ecosystem benefits. Analyses of dynamic multispecies models and multilayer networks will be applicable to other natural and social systems. The researchers will contribute to open-science efforts through depositing the models and the code in public repositories. They will incorporate the project results into both undergraduate and graduate courses, including the MSU–UCLA distributed seminar. They will also develop inquiry-based educational modules on microbial communities to increase quantitative reasoning skills and microbial-ecology knowledge in K–12 students. Partial funding was provided by the Math Biology program in the MPS Directorate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.