Systems Immunology in Aging and Chronic Diseases of Aging

衰老和慢性衰老疾病中的系统免疫学

• 批准号: 10459457
• 负责人: Duygu Ucar
• 金额: $ 3.44万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10459457
• 关键词: Academia; Address; Affect; Age; Aging; Alzheimer' s Disease; Animal Model; Cardiovascular Diseases; Career Mobility; Cell physiology; Cells; Chronic Disease; Cities; Clinical; Collaborations; Complex; Computational Biology; Computational Technique; Computing Methodologies; Connecticut; Data; Data Analyses; Data Science; Development; Discipline; Disease; Economic Burden; Educational workshop; Elderly; Ethnic group; Etiology; Feedback; Female; Fostering; Functional disorder; Funding; Genomic medicine; Genomics; Geroscience; Goals; Health; Home; Human; Immune; Immune system; Immunologist; Immunology; Industry; Inflammation; Institution; Lead; Learning; Link; Location; Longevity; Machine Learning; Malignant Neoplasms; Metabolic; Methods; Molecular; Mus; Nature; Non-Insulin-Dependent Diabetes Mellitus; Organism; Outcome; Participant; Phenotype; Play; Population; Postdoctoral Fellow; Process; Public Health; Pythons; Race; Research; Research Personnel; Resources; Role; Scholarship; Science; Scientist; Series; Shock; Societies; Students; Support System; System; Techniques; Technology; The Jackson Laboratory; Time; Tissues; Training; Underrepresented Minority; Universities; Work; aging population; cancer type; career; clinical biomarkers; clinically significant; data complexity; data mining; deep learning algorithm; epigenomics; frailty; genomic biomarker; genomic data; graduate student; health economics; healthspan; innovation; interdisciplinary approach; interest; machine learning algorithm; machine learning model; meetings; next generation; novel strategies; posters; programs; recruit; response; senescence; single cell technology; skills; symposium; technology development; transcriptomics; translational approach

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.

项目摘要/摘要