Systems in Integrative Biology

摘要 该提案的目的是通过系统和综合生物学的更新， 培训补助金（SIBTG），六名学生在数学和数学专业的独特的年度博士前培训， 计算生物学SIBTG严格的数学和生物课程要求 鉴于它在培养知识渊博和多才多艺的生物医学研究人员方面的良好记录， 许多受训人员后来在学术界和工业界担任要职。在现代， 数据驱动的生物医学研究，SIBTG的使命更加紧迫。例如膨胀 空间和时间上的数据采集和分辨率可能会导致更高的误报率， 肿瘤检测，大脑和心脏信号，基因检测等，尽管看似无限量 数据，可用于进一步研究这些数据所暗示的生物学机制的资源 仍然有限。因此，仔细的定量分析，精炼的物理理解， 在生物医学研究中，生物学的洞察力将是至关重要的，因为人们往往是概念有限的，而不是 数据有限SIBTG通过以下方式提供与定量生物学典型赠款正交的培训： 强调获得理论/计算数学及其司法应用方面的专业知识 到生物学 在该提案中，对SIBTG提出了一些调整和改进， 作为对医学数据使用增加的补充，最近成立了加州大学洛杉矶分校医学研究所， 定量和计算生物学，加州大学洛杉矶分校抑郁症大挑战的发起， 校园内其他培训赠款的演变。具体而言，（1）新董事周通宇教授 （2）行政结构得到精简和多样化，（3） 参与教师已大大更新，（4）在神经生物学辅导能力已 增加，（5）从加州大学洛杉矶分校获得机构支持，以支持额外的受训者选择 在相同的核心标准下，（6）核心研讨会的要求将扩展为一个完整的4单元课程 包括科学写作和演示方面的培训，以及（7）合作推广和 与加州北岭，一个西班牙裔服务机构的招聘活动将被开发， 正式化。领导和成员教员将被选定，以确保独立性和名义 与加州大学洛杉矶分校的其他培训赠款重叠。这些变化和这笔赠款的资金将使新的 一代科学家在定量方法方面受到严格的培训，使他们能够真正收获 大数据的果实。

ABSTRACT The aim of this proposal is to continue, through the renewal of the System and Integrative Biology Training Grant (SIBTG), the unique yearly pre-doctoral training of six students in mathematical and computational biology. The rigorous mathematical and biological course requirements of the SIBTG have given it a track record of producing highly knowledgeable and versatile biomedical researchers, including many trainees who have gone on to high-profile academic and industrial positions. In the modern era of data-driven biomedical research, the mission of the SIBTG is even more urgent. For example, expanded acquisition and resolution of data in space and time will likely lead to higher false positive rates for detection of tumors, brain and cardiac signals, genetic testing, etc. Despite a seemingly unlimited amount of data, the resources available for further investigating the biological mechanisms implied by these data remain limited. Therefore, careful quantitative analysis, refined physical understanding, and enlightened biological insight will be ever critical in biomedical research where one is often concept-limited rather than data-limited. The SIBTG provides training orthogonal to that of typical grants for quantitative biology by stressing the acquisition of expertise in theoretical/computational mathematics and its judicial application to biology. In this proposal, a number of adjustments and improvements to the SIBTG are being proposed to complement the increased use of data in medicine, the recent establishment of the UCLA Institute of Quantitative and Computational Biology, the initiation of the Grand Challenge in Depression at UCLA, and the evolution of other training grants on campus. Specifically, (1) a new director, Prof. Tom Chou, has been named, (2) the administrative structure has been streamlined and diversified, (3) the roster of participating faculty has been significantly updated, (4) mentoring capacity in neurobiology has been increased, (5) institutional support from UCLA has been obtained to support an additional trainee selected under the same core criteria, (6) the core seminar requirement will be expanded into a full 4-unit course that incorporates training in scientific writing and presentations, and (7) collaborative outreach and recruitment activities with Cal State-Northridge, a Hispanic-serving institution will be developed and formalized. The leadership and member faculty will be selected to ensure independence and nominal overlap with other training grants at UCLA. These changes and the funding of this grant will enable a new generation of scientists to be rigorously trained in quantitative methods, allowing them to truly harvest the fruits of big data.