Statistical physics and network-based approaches for elucidating molecular biomarkers of COPD

项目总结 慢性阻塞性肺疾病(COPD)是一种慢性炎症性肺部疾病，导致阻塞性 从肺部流出的气流。COPD作为一种常见的复杂疾病，在全球范围内具有较高的发病率和死亡率。的确， 死于呼吸系统疾病的人数接近400万，其中大部分是由慢性阻塞性肺病造成的。那里 明确要求提高我们对COPD发病机制的认识，并制定预防措施 治疗慢性阻塞性肺疾病。然而，复杂的疾病表型通常由不同的病理生物学过程决定。 它们在网络中相互作用，而不是由单个效应器基因产物的异常引起。广泛性 有证据表明，疾病相关蛋白在人类蛋白-蛋白质中有明显的相互作用。 交互(PPI)网络(也称为人类交互作用组)，以及复合体的病理生物学过程 疾病与相互作用体的特定疾病邻域内的扰动有关，通常被称为 作为疾病模块。全面认识慢性阻塞性肺疾病的发病机制和预测疾病 为治疗提供信息的基因需要先进的工具来识别其疾病模块。虽然很多人 已有文献报道了疾病模块检测方法，它们都有根本的局限性。 更重要的是，现有方法没有充分利用多组学数据的优势。在此应用程序中， 将开发一个基于统计物理和网络的框架来检测复杂的疾病模块 使用多组学数据研究人类疾病。这一框架将用合成数据进行系统验证。 然后将其应用于丰富的多组学数据(SNP基因分型、DNA甲基化、mRNA和miRNA 表达)在两个大型COPD队列中。王博士在统计物理、网络科学和深度技术方面的培训 学习为他提出的研究做好了充分的准备。然而，理解和解释 复杂疾病的分子基础和多组学数据的统计分析仍然是艰巨的任务 将需要在特定领域进行进一步培训。王博士将利用中国优秀的智力环境 哈佛医学院及其教学医院，并将获得广泛的计算资源 通过钱宁网络医学部和哈佛医学院。通过一个 具有互补专业知识的指导和咨询团队，以及正式课程和讲习班， 王博士将全身心投入到一个专注于统计遗传学、表观遗传学、多组学的培训项目中 整合，以及肺部疾病的生物学。王博士还将参加与他的 导师和咨询委员会成员，使他能够分享他的进展并收到及时的反馈。 总而言之，王博士的培训和研究计划将使他能够扩展自己目前的技能，包括 有能力应对分析大型流行病学的复杂基因组和表观基因组数据的挑战 队列，确定COPD系统生物学中的未决问题，并最终有助于精确度 肺病医学。

PROJECT SUMMARY Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. As a common complex disease, COPD has high global morbidity and mortality. Indeed, deaths due to respiratory disease numbered nearly four million, which was mostly contributed by COPD. There is a clear demand to improve our understanding of COPD pathogenesis and develop interventions to prevent and treat COPD. Yet, a complex disease phenotype is usually determined by various pathobiological processes that interact in a network, rather than induced by the abnormality in a single effector gene product. Extensive evidence implies that disease-associated proteins have distinct interactions within the human protein-protein interaction (PPI) network (a.k.a. the human interactome), and the pathobiological processes of a complex disease are associated with perturbation within specific disease neighborhoods of the interactome, often referred to as the disease module. Comprehensive understanding of the COPD pathogenesis and predicting disease genes to inform therapeutic treatment require advanced tools to identify its disease module. Although many disease module detection methods have been reported in the literature, they all have fundamental limitations. More importantly, existing methods do not fully leverage the advantage of multi-omics data. In this application, a statistical physics and network-based framework will be developed to detect disease modules for complex human diseases using multi-omics data. This framework will be systematically validated with synthetic data. Then it will be applied to the rich multi-omics data (SNP genotyping, DNA methylation, mRNA and miRNA expression) in two large COPD cohorts. Dr. Wang’s training in statistical physics, network science and deep learning have prepared him well for his proposed research. However, understanding and interpreting the molecular basis of complex diseases and the statistical analysis of multi-omics data are still arduous tasks that will require further training in specific areas. Dr. Wang will leverage the excellent intellectual environment of Harvard Medical School and its teaching hospitals and will have access to extensive computational resources through the Channing Division of Network Medicine and Harvard Medical School. Through the guidance of a mentoring and advisory team with complementary expertise, together with formal coursework and workshops, Dr. Wang will immerse himself in a training program focusing on statistical genetics, epigenetics, multi-omics integration, and the biology of pulmonary diseases. Dr. Wang will also participate in regular meetings with his mentors and advisory committee members, allowing him to share his progress and receive timely feedback. Altogether, Dr. Wang’s training and research plan will enable him to expand his current skillset to include the ability to address the challenges of analyzing the complex genomic and epigenomic data of large epidemiological cohorts, identify open questions in the systems biology of COPD, and ultimately contribute to the precision medicine of lung diseases.