Systems Immunology in Aging and Chronic Diseases of Aging

项目摘要/摘要 在本R13申请中所要求的资金用于部分支持《老龄化和慢性疾病的系统免疫学》 《老年病》年度会议将于2020年至2022年每年9月在杰克逊酒店举行 康涅狄格州法明顿的基因组医学实验室(JAX-GM)。这次会议是建立在它的 2019年成功初审，将汇集多达150名跨学科科学家，其中包括分子 生物学家、免疫学家、计算生物学家和老年病学家，他们对 在系统层面上理解衰老和衰老相关疾病。许多与衰老相关的疾病，如 如癌症和心血管疾病，会受到免疫系统功能障碍的影响。最新进展 在基因组图谱技术中(例如，单细胞转录组)提供了发现与衰老相关的机会 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。的财富和复杂性 使用这些技术产生的数据正在不断增加，开发高级计算的需求也在不断增加 方法对这些数据进行挖掘和整合。尽管有这种需要，但目前还没有正式的场馆 科学家，特别是老龄化领域的科学家，可以接受数据挖掘的基础和应用方面的培训 技术(即机器学习算法)。此外，目前关于老龄化问题的会议的目标不是 特别是将计算生物学家、免疫学家以及基础和临床衰老研究人员聚集在一起。 因此，本次会议的目标是：(1)认识并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交流 学科：老年学、免疫学和计算生物学；(2)提供实习生(学生和博士后 研究员)与更资深的调查人员密切互动并获得反馈的机会 他们的项目并建立联系，以帮助他们建立职业生涯；以及(3)为 老龄化领域的研究人员学习机器学习技术的基础，他们将能够 回国后立即申请进行自己的研究。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 研讨会的重点是老龄化和与老龄化相关的疾病。会议将包括为期两天的研讨会。 以顶尖科学家的演讲为特色，随后是为期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 张贴会。在AIM 3中，我们将招募不同的参与者。我们建议的演讲者名单上有几位女性 科学家，我们将利用我们的合作网络专门从全国范围内招募与会者 代表不足的种族和民族群体。这次会议的最终目的是推进老龄化研究 通过应用以下技术加速合作和了解与衰老相关的基因组数据 高级数据挖掘方法。

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.