Systems in Integrative Biology

摘要 这项提议的目的是通过更新系统和综合生物学来继续 培训助学金(SIBTG)，独一无二的年度博士前培训，六名学生在数学和 计算生物学。SIBTG对数学和生物课程的严格要求有 鉴于它在培养高知识和多才多艺的生物医学研究人员方面的记录，包括 许多受训人员后来在学术界和工业界担任高调职位。在现代， 数据驱动的生物医学研究，SIBTG的使命更加紧迫。例如，Expanded 在空间和时间上获取和解析数据可能会导致更高的假阳性率 检测肿瘤、大脑和心脏信号、基因测试等，尽管似乎是无限的 可用于进一步研究这些数据所隐含的生物机制的资源 仍然是有限的。因此，认真的定量分析，精炼的物理理解，和启迪 在生物医学研究中，生物洞察力将永远是至关重要的，因为人们往往受到概念的限制，而不是 数据有限。SIBTG通过以下方式提供与数量生物学典型资助垂直的培训 强调获得理论/计算数学方面的专门知识及其司法应用 为了生物学。 在这项提案中，对SIBTG提出了一些调整和改进，以 作为对医学中数据使用增加的补充，加州大学洛杉矶分校最近成立了 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，以及 校园内其他培训补助金的演变。具体地说，(1)新的董事，周教授，已经 (2)行政结构精简和多样化，(3) (4)神经生物学方面的指导能力得到了显著的更新 增加，(5)已从加州大学洛杉矶分校获得机构支持，以支持额外选定的学员 在相同的核心标准下，(6)核心研讨会的要求将扩展为完整的4个单元的课程 这包括科学写作和演示方面的培训，以及(7)协作外联和 将与加州州立大学北岭分校开展招聘活动，这是一家为拉美裔提供服务的机构 正式的。领导层和成员教职员工的选择将确保独立性和名义上的 与加州大学洛杉矶分校的其他培训补助金重叠。这些变化和这笔赠款的资金将使一个新的 一代科学家将接受严格的定量方法培训，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.