Bayesian Dynamical Modeling of Microbial Communities

项目摘要/摘要 微生物群落及其宿主在许多应用中发挥着关键作用，包括保护人类或植物 防治疾病或开发下一代生物燃料和生物修复系统所需的 可持续增长。深入了解这些系统的基本生物学是解决这些问题的关键。 挖掘他们的潜力。元基因组、元翻译等高通量多组学技术的研究进展 TOMICS、外代谢组学和蛋白质组学使我们能够捕获这些复杂生物的多个快照 一次处理。这些快照创建组学特征的大规模高维数据集(例如， 鳄鱼物种、微生物基因、蛋白质和小分子)。成本的降低也让研究人员能够 收集更多的多组学时间序列数据。这些时间分辨的多组学特征可以一起提供 生物过程及其基本活动的全面图景。 这些精心设计的多组学研究尚未得到最充分的潜力分析，主要是因为 缺乏这类分析所需的适当工具和注释数据库。例如，系统地 研究这些纵向数据的时间成分，以研究组学特征的时间动力学 在许多研究中，与疾病活动的关系是一个未得到满足的需求。因此，迫切需要 统计工具，通过集成不同的数据类型和系统地极大地改善研究基础设施 研究这些纵向数据的时间成分。 该项目的总体目标是在我们以前的基础上开发有效的、可解释的和可扩展的fi工具 发展了信号模型，称为部分观测布尔动力系统(POBDS)来描述时间 通过多组学数据组成并捕捉微生物群落的动态行为。原版 可通过以下研究目标组织投稿： (I)在POBDS背景下开发新的方法，能够模拟通过以下途径获得的多组学数据 各种分子检测技术和各种疾病/领域。 (2)开发贝叶斯优化框架，以便对网络进行有效和可伸缩的重建 微生物群落的拓扑结构(即，推断大量基因之间的相互作用的类型， 细菌和微生物)通过高维多组学数据。 (3)开发贝叶斯强化学习扰动策略，以减少所需的数据量 用于建模/学习过程(克服非IDENTIfi能力问题)，并获得最丰富的信息 微生物群落中的数据。 在这个项目中开发的所有工具将以用户友好的软件/工具的形式呈现，其他人可以自由访问 研究人员。

Project Summary/Abstract Microbial communities and their hosts play a key role in many applications, including protecting humans or plants against diseases or developing the next generation of biofuels and biological remediation systems needed for sustainable growth. Gaining a deep understanding of the fundamental biology of these systems is the key to har- nessing their potential. Advances in high-throughput multi-omics techniques like metagenomics, metatranscrip- tomics, exometabolomics, and proteomics, allow us to capture multiple snapshots of these complex biological processes at once. These snapshots create large-scale high-dimensional datasets of omics features (e.g., mi- crobial species, microbial genes, proteins, and small molecules). The reduced cost has also allowed researchers to collect more multi-omics time-series data. These temporally resolved multi-omics features can together provide a comprehensive picture of biological processes and their underlying activities. These well-designed multi-omics studies have not been analyzed to their fullest potential yet, primarily due to the lack of appropriate tools and annotation databases required for such analyses. For example, systematically investigating the time component of this longitudinal data to investigate the temporal dynamics of omics features in relationship with disease activities is an unmet need in many studies. Therefore, there is a critical need for statistical tools to greatly improve research infrastructure by integrating different data types and systematically investigating the time component of this longitudinal data. This project's overarching goal is to develop efficient, interpretable, and scalable tools based on our previously developed signal model, called partially-observed Boolean dynamical systems (POBDS), to characterize the time component and capture the dynamical behavior of microbial communities through multi-omics data. The original contributions can be organized across the following research goals: (i) Developing novel methods in the POBDS context capable of modeling multi-omics data obtained through various molecular profiling technologies and various diseases/domains. (ii) Developing Bayesian optimization frameworks for the efficient and scalable reconstruction of the network topology of microbial communities (i.e., inferring the type of interactions between a large number of genes, bacteria, and microbes) through high dimensional multi-omics data. (iii) Developing Bayesian reinforcement learning perturbation policies to decrease the number of data required for the modeling/learning process (overcoming the non-identifiability issue) and acquire the most informative data in microbial communities. All the developed tools in this project will be presented in a user-friendly software/tool freely accessible to other researchers.