Interdisciplinary Training in Systems & Biomolecular Data Science

系统跨学科培训

基本信息

批准号：
10631096
负责人：
Kevin A Janes
金额：
$ 31.83万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10631096
关键词：
Data Science System Training

项目摘要

PROJECT SUMMARY/ABSTRACT Contemporary basic research uses data-intensive approaches to address fundamental questions about biological phenomena that emerge at the systems level. This application will establish the Systems & Biomolecular Data Science (SBDS) training program as a hub for cultivating quantitative, computational, and analytic predoctoral students at the University of Virginia (UVA). The SBDS training program brings together 12 degree-granting programs and 34 faculty mentors across the UVA Schools of Medicine, Engineering, Data Science, and Arts & Sciences. UVA has a rich history in SBDS, which spans decades of systems physiology in different cell types and organs, thus ensuring a competitive pool of predoctoral applicants each year. The co-PIs have complementary expertise in the SBDS field and track records in mentorship and education, career development of predoctoral students, scientific rigor and transparency, and diversity, equity, and inclusion. The overarching goal of the SBDS training program is to prepare the next generation of transdisciplinary biomedical scientists at the interface of quantitative biology, systems biology, machine learning, and informatics. The one-year SBDS curriculum will guide 10 second-year Ph.D. students plus one cost-shared student through coursework, a discussion series on collaborative foundations, a journal club, a hackathon, and an 80-hour research immersion in the laboratory of an approved co-mentor who is complementary to the primary mentor. SBDS alumni remain connected to the training program through a sustained relationship with their co-mentor and journal-club flexibility for discussions of research in progress and follow-on fellowship applications. The training program builds upon the strengths of a one-time Big Data to Knowledge training grant at UVA, which was sponsored by the Office of the Director and coordinated by the National Library of Medicine. SBDS has robust plans for assessment and dissemination to ensure that the program emulates the agility and versatility that will characterize its trainees.

项目概要/摘要当代基础研究使用数据密集型方法来解决以下基本问题：在系统层面出现的生物现象。该应用程序将建立系统和生物分子数据科学 (SBDS) 培训计划作为培养定量、计算和分析能力的中心弗吉尼亚大学 (UVA) 的分析博士前学生。 SBDS 培训计划汇集了 UVA 医学院、工程学院、数据学院设有 12 个学位授予项目和 34 名教师导师科学、艺术与科学。 UVA 在 SBDS 方面拥有丰富的历史，跨越了数十年的系统生理学不同的细胞类型和器官，从而确保每年有一个有竞争力的博士前申请者群体。这联合 PI 在 SBDS 领域拥有互补的专业知识，并在指导和教育、职业生涯方面拥有良好的记录博士前学生的发展、科学的严谨性和透明度、多样性、公平性和包容性。 SBDS 培训计划的总体目标是培养下一代跨学科人才定量生物学、系统生物学、机器学习等领域的生物医学科学家信息学。为期一年的 SBDS 课程将指导 10 名二年级博士生。学生加一名费用分摊学生通过课程作业、关于协作基础的讨论系列、期刊俱乐部、黑客马拉松以及在经批准的共同导师的实验室进行 80 小时的研究沉浸，该导师是对主要导师。 SBDS 校友通过与以下机构的持续关系，与培训计划保持联系：他们的共同导师和期刊俱乐部可以灵活地讨论正在进行的研究和后续研究金应用程序。该培训计划建立在一次性大数据知识培训的优势之上 UVA 的赠款，由馆长办公室赞助并由国家图书馆协调药品。 SBDS 制定了强有力的评估和传播计划，以确保该计划模仿学员的敏捷性和多才多艺是其特点。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantitative mechanistic model reveals key determinants of placental IgG transfer and informs prenatal immunization strategies.

定量机制模型揭示了胎盘 IgG 转移的关键决定因素，并为产前免疫策略提供了信息。

DOI：
发表时间：
2023-11
期刊：
影响因子：
4.3
作者：
Wessel, Remziye E;Dolatshahi, Sepideh
通讯作者：
Dolatshahi, Sepideh

Metabolic modeling of sex-specific liver tissue suggests mechanism of differences in toxicological responses.

性别特异性肝组织的代谢模型提示了毒理学反应差异的机制。

DOI：
发表时间：
2023-08
期刊：
影响因子：
4.3
作者：
Moore, Connor J;Holstege, Christopher P;Papin, Jason A
通讯作者：
Papin, Jason A

Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration of Xenopus mesendoderm.

模拟粘性、细胞嵌入和组织几何形状在非洲爪蟾中内胚层集体细胞迁移中的作用。

DOI：
发表时间：
2023-10-17
期刊：
影响因子：
0
作者：
Comlekoglu, Tien;Dzamba, Bette J;Pacheco, Gustavo G;Shook, David R;Sego, T J;Glazier, James A;Peirce, Shayn M;DeSimone, Douglas W
通讯作者：
DeSimone, Douglas W

Metabolic modeling of sex-specific tissue predicts mechanisms of differences in toxicological responses.

性别特异性组织的代谢模型可以预测毒理学反应差异的机制。