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 佐治亚理工学院的询问T32将支持综合培训 跨学科博士学生推进高级生活系统的综合研究 维数据分析和计算模型。学生驱动的发现将扩大 生活系统的知识基础，以应对生物医学相关性的基本挑战 跨越分子，细胞，到种群（无论是微生物种群中的生物膜还是在生物膜中）还是 从肿瘤微环境到心脏组织的真核细胞组合之间。 T32 受训者和分支机构将学会对生活系统进行定量推理，鉴于不确定性， 将严格的数据分析原理和算法作为计算软件，提取 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。宣传威尔 专注于地方和国家机构的初中和高级本科生 加强进入跨学科生物科学博士学位的不同学生的管道。这 将核心发现转化为工业和翻译应用的转换将由 培训人员将获得实践管理和领导才能的行业参与活动 技能，应用数据分析培训，并将通过直接扩展其专业网络 通过多个行业合作伙伴与行业领导者的互动。总体而言，查询T32将 改变博士学位的核心能力集。遍布生物科学领域的学生， 生物工程和数据科学学科。为此，T32程序将最佳整合 生物科学，计算和工程学科的实践，训练下一代 生物医学科学家利用数据革命，并使学员能够发起动态 旨在了解生活系统及其影响的结构和动态的职业 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