ConProject-001

复杂的生物过程，包括器官发育、免疫反应和疾病进展， 都是动态的学习他们的监管架构需要了解如何 一个大系统的组成部分动态地相互作用，并产生紧急行为。 最近的实验进展使我们有可能以一种新的方式研究这些生物系统。 数据驱动的方式高时间分辨率，允许识别新的基因及其调控 交互.纵向组学数据集在临床实践中越来越普遍 也这些相互作用基因的信息可以被整合，以获得系统水平的 深入了解生物途径和过程的作用，包括疾病的进展。因此，委员会认为， 开发可解释的方法来学习基因、蛋白质、 从高维时间序列数据中提取代谢产物已成为一个及时的研究问题。 这些时间进程数据集的性质带来了令人兴奋的机会和有趣的挑战 从统计学的角度来看。典型的时程组学数据集具有挑战性，因为 它们的高维性和系统组件之间的非线性关系。为了应对这些挑战， 人们需要复杂的降维技术，这些技术在生物学上有意义，在计算上有意义， 有效，并允许不确定性量化。结合现有生物技术的方法 信息（例如，途径成员，蛋白质-蛋白质相互作用）纳入数据分析是好的 候选人分析这样的高维系统使用小样本。 在这里，我们将开发三种核心方法来应对上述挑战-（目标1）： 基于先验生物学知识的高维组学时程数据聚类贝叶斯框架 (Aim 2）：一个基于分位数的格兰杰因果关系框架，用于学习基因间的相互作用 或代谢物从他们的铅滞后关系;和（目标3）：决策树集成框架， 从基因的时间表达谱中寻找基因间相互作用的级联。我们的跨学科 一个由统计学家和科学家组成的小组将分析来自三项研究的时间进程数据， 项目：（i）果蝇的先天免疫反应系统，（ii）小鼠模型的发育过程， 和（ii）TB患者的纵向代谢物谱。这些见解将用于建立和 验证我们的方法，这将在一个公开的软件中实现。这项建议是 创新的是在新的降维框架中结合了先前的生物学知识 用于询问高维时程组学数据的技术。这项研究在 它将通过阐明数据驱动的、可检验的监管架构假设来影响基础科学 通过监测疾病进展和预后来了解生物过程和临床实践。

Complex biological processes, including organ development, immune response and disease progression, are inherently dynamic. Learning their regulatory architecture requires understanding how components of a large system dynamically interact with each other and give rise to emergent behavior. Recent experimental advances have made ii possible to investigate these biological systems in a data-driven fashion al high temporal resolution, allowing identification of new genes and their regulatory interactions. Longitudinal omics data sets are becoming increasingly common in clinical practice as well. Information on these collections of interacting genes can be integrated to gain systems-level insights into the roles of biological pathways and processes, including progression of diseases. Consequently, developing interpretable methods for learning functional relationships among genes, proteins or metabolites from high-dimensional time series data has become a timely research problem. The nature of these time-course data sets presents exciting opportunities and interesting challenges from a statistical perspective. Typical time-course omics data sets are challenging because of their high-dimensionality and non-linear relationships among system components. To tackle these challenges, one needs sophisticated dimension-reduction techniques that are biologically meaningful, computationally efficient and allow uncertainty quantification. Methods that incorporate prior biological information (e.g., pathway membership, protein-protein interactions) into the data analysis are good candidates for analyzing such high-dimensional systems using small samples. Here, we will develop three core methods to address the above challenges - (Aim 1): an empirical Bayes framework for clustering high-dimensional omics time-course data using prior biological knowledge; (Aim 2): a quantile-based Granger causality framework for learning interactions among genes or metabolites from their lead-lag relationships; and (Aim 3): a decision tree ensemble framework for searching cascades of interactions among genes from their temporal expression profiles. Our interdisciplinary team of statisticians and scientists will analyze time-course ornics data from three research projects: (i) innate immune response systems in Drosophila, (ii) developmental process in mouse models, and (ii) longitudinal metabolite profiling of TB patients. These insights will be used to build and validate our methodology, which will be implemented in a publicly available software. This proposal is innovative in its incorporation of prior biological knowledge in the framework of novel dimension reduction techniques for interrogating high-dimensional time-course omics data. This research is significant in that it will impact basic sciences by elucidating data-driven, testable hypotheses on the regulatory architecture of biological processes, and clinical practice by monitoring disease progression and prognosis.