Systems Immunology in Aging and Chronic Diseases of Aging

项目概要/摘要 本R13申请中要求的资金用于部分支持"衰老和慢性疾病中的系统免疫学" 从2020年到2022年，每年9月在杰克逊酒店举行"老龄化疾病"年会 位于康涅狄格州法明顿的基因组医学实验室（JAX-GM）。本次会议建立在其非常 2019年首次成功，将汇集多达150名跨学科科学家，包括分子生物学 生物学家，免疫学家，计算生物学家和老年病学家，他们有着共同的兴趣， 在系统层面理解衰老和衰老相关疾病。许多与衰老有关的疾病，如 如癌症和心血管疾病，都受到免疫系统功能障碍的影响。最新进展 在基因组分析技术中（例如，单细胞转录组学）提供了一个发现衰老相关 人类细胞/组织的变化，并将这些变化与健康和寿命联系起来。财富和复杂性 使用这些技术产生的数据正在不断增加，因为需要开发先进的计算技术， 挖掘和整合这些数据的方法。尽管有这种需要，但目前没有正式的场所， 科学家，特别是老龄领域的科学家，可以接受数据挖掘基础知识和应用方面的培训 技术（即，机器学习算法）。此外，目前关于老龄问题的会议并不旨在 特别是汇集了计算生物学家，免疫学家和基础和临床衰老研究人员。 因此，本次会议的目标是：（1）认识和强调高度跨学科的性质 并促进和加速三个领域的合作和思想的交叉授粉 学科：老龄化，免疫学和计算生物学;（2）提供培训人员（学生和博士后 有机会与更资深的调查员密切互动，并从他们那里获得反馈， 他们的项目和建立联系，以帮助建立他们的职业生涯;（3）提供一个机会， 衰老领域的研究人员学习机器学习技术的基础知识，他们将能够 返回原院校后立即申请从事研究工作。我们将实现这些目标 通过实现以下目标。在目标1中，我们将组织一次跨学科会议和实践 讲习班的重点是老龄化和与老龄化有关的疾病。会议将包括为期两天的研讨会 由领先的科学家进行演讲，然后是为期1天的机器学习基础实践研讨会。 在目标2中，我们将促进互动，以促进合作研究和职业发展，包括通过 海报展示会在目标3中，我们将招募不同的与会者。我们提议的发言人名单上有几位女性 科学家，我们将利用我们的伙伴关系网络，专门招募来自全国各地的与会者， 代表性不足的种族和族裔群体。会议的最终目的是推进老龄化研究 通过加速合作和理解与衰老相关的基因组数据， 先进的数据挖掘方法。

PROJECT SUMMARY / ABSTRACT The funds requested in this R13 application are for partial support of “Systems Immunology in Aging and Chronic Diseases of Aging” annual meetings to be offered each September from 2020 through 2022 at The Jackson Laboratory for Genomic Medicine (JAX-GM) in Farmington, Connecticut. This meeting builds on its very successful first instance in 2019 and will bring together up to 150 interdisciplinary scientists including molecular biologists, immunologists, computational biologists, and geriatricians, who share a common interest in understanding aging and aging-associated disease at the systems level. Many aging-associated diseases, such as cancer and cardiovascular disease, are influenced by dysfunctions in the immune system. Recent advances in genomic profiling techniques (e.g., single cell transcriptomics) provide an opportunity to uncover aging-related changes in human cells/tissues and to link these changes to health and lifespan. The wealth and complexity of data produced using these technologies is ever increasing, as is the need to develop advanced computational methods to mine and integrate these data. Despite this need, there are currently no formal venues at which scientists, specifically those in the aging field, can be trained in the basics and application of data mining techniques (i.e., machine learning algorithms). Furthermore, current conferences on aging are not aimed at specifically bringing together computational biologists, immunologists and basic and clinical aging researchers. Therefore, the objectives of this meeting are: (1) to recognize and emphasize the highly interdisciplinary nature of the aging field and to promote and accelerate collaborations and cross-pollination of ideas across the three disciplines: aging, immunology, and computational biology; (2) to provide trainees (students and postdoctoral fellows) an opportunity to closely interact with, and gain feedback from, more senior investigators to advance their projects and establish connections to help build their careers; and (3) to provide an opportunity for researchers in the field of aging to learn the basics of machine learning techniques, which they will be able to immediately apply to their own research upon return to their home institutions. We will reach these objectives through carrying out the following Aims. In Aim 1, we will organize an interdisciplinary meeting and hands-on workshop focused on aging and aging-related diseases. The meeting will include a 2-day seminar session featuring talks by leading scientists, followed by a 1-day hands-on workshop on the basics of machine learning. In Aim 2, we will promote interactions to foster collaborative research and career advancement, including through a poster session. In Aim 3, we will recruit diverse attendees. Our proposed speaker list features several female scientists, and we will use our partnership networks to specifically recruit attendees from nationally underrepresented racial and ethnic groups. The ultimate goal of the meeting is to advance the aging research field through expediting collaborations and the understanding of aging-related genomic data via application of advanced data mining approaches.