Integrative and Quantitative Biosciences Accelerated Training Environment

综合和定量生物科学加速培训环境

• 批准号: 10620308
• 负责人: James C. Gumbart
• 金额: $ 30.14万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10620308
• 关键词: Acceleration; Biological Sciences; Environment; Training

Abstract The InQuBATE T32 at the Georgia Institute of Technology will support the integrated training of interdisciplinary Ph.D. students advancing the integrative study of living systems enabled by high- dimensional data analytics and computational models. Student-driven discoveries will broaden the knowledge-base of living systems to address foundational challenges of biomedical relevance spanning molecules, cells, to populations (whether in biofilms amongst population of microbes or between eukaryotic cellular assemblages from tumor microenvironments to cardiac tissue). T32 trainees and affiliates will learn to reason quantitatively about living systems given uncertainty, implement rigorous data analytic principles and algorithms as computational software, extract information and insights from high-dimensional bioscience data sets, and communicate the relevance of their findings to advancing basic research and industrial applications. Outreach will focus on junior- and senior-level undergraduates at local and national institutions as a means to enhance the pipeline of diverse students entering interdisciplinary bioscience Ph.D.-s. The translation of core discoveries into industrial and translation applications will be facilitated by industry engagement events where trainees will acquire practical management and leadership skills, applied data analytics training, and will expand their professional networks via direct interactions with industry leaders via multiple industry partners. Overall, the InQuBATE T32 will transform the core set of competencies of Ph.D. students across the spectrum of bioscience, bioengineering, and data science disciplines. In doing so, the T32 program will integrate best practices from bioscience, computing, and engineering disciplines, train the next generation of biomedical scientists to harness the data revolution, and enable trainees to initiate dynamic careers aimed at understanding the structure and dynamics of living systems and their impacts on human health and well-being.

抽象的 佐治亚理工学院的 InQuBATE T32 将支持以下人员的综合培训： 跨学科博士学生推进高水平生命系统的综合研究 维度数据分析和计算模型。学生驱动的发现将拓宽 生命系统的知识库，以应对生物医学相关的基本挑战 跨越分子、细胞、群体（无论是微生物群体中的生物膜还是 从肿瘤微环境到心脏组织的真核细胞组合之间）。 T32 受训者和附属机构将学习在不确定的情况下对生命系统进行定量推理， 将严格的数据分析原理和算法作为计算软件实施，提取 来自高维生物科学数据集的信息和见解，并传达 他们的发现与推进基础研究和工业应用的相关性。外展会 重点关注地方和国家机构的低年级和高年级本科生，以此作为 加强多元化学生进入跨学科生物科学博士学位的渠道。这 将促进核心发现转化为工业和翻译应用 行业参与活动，学员将获得实践管理和领导力 技能、应用数据分析培训，并将通过直接扩展他们的专业网络 通过多个行业合作伙伴与行业领导者互动。总体而言，InQuBATE T32 将 转变博士的核心能力。生物科学领域的学生， 生物工程和数据科学学科。这样做，T32 程序将最好地集成 生物科学、计算和工程学科的实践，培养下一代 生物医学科学家利用数据革命，使学员能够启动动态 旨在了解生命系统的结构和动态及其影响的职业 关于人类健康和福祉。

项目成果

Spatial subcellular organelle networks in single cells.

• DOI: 10.1038/s41598-023-32474-y
• 发表时间: 2023-04-01
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Venkatesan, Mythreye;Zhang, Nicholas;Marteau, Benoit;Yajima, Yukina;Garcia, Nerea Ortiz De Zarate;Fang, Zhou;Hu, Thomas;Cai, Shuangyi;Ford, Adam;Olszewski, Harrison;Borst, Andrew;Coskun, Ahmet F.
• 通讯作者: Coskun, Ahmet F.

Evolutionary consequences of nascent multicellular life cycles.

• DOI: 10.7554/elife.84336
• 发表时间: 2023-10-27
• 期刊: eLife
• 影响因子: 7.7
• 作者: Pentz JT;MacGillivray K;DuBose JG;Conlin PL;Reinhardt E;Libby E;Ratcliff WC
• 通讯作者: Ratcliff WC