Biostatistics Training for Basic Biomedical Research

人类基因组的测序为生物学的下一个主要前沿--蛋白质组--奠定了基础， 为生物医学研究的“组学”新时代铺平了道路。爆炸性的数据瀑布 需要集成用于数据表示和操作的生物信息学工具，以及复杂的 统计和数学方法，用于验证、分析、建模，最后统一和 解释无数的“组学”数据以推断生物功能。生物统计学培训的目标是 基础生物医学研究(BTBBR)计划，由生物统计部门提出， 南卡罗来纳医科大学(MUSC)的生物信息学和流行病学，正在培养一种新的 在基础生物医学方面有丰富教学和实践经验的一代生物统计学家 科学，以便他们准备好在新一代基础生物医学研究中发挥关键作用。 BTBBR计划强调生物统计学理论和方法的整合，包括非线性 系统分析和数学建模，利用生物信息学的工具解决量化前沿问题 在现代多学科生物学研究中。该计划将利用一个成熟的和 成功的大学课程。提供共同的基础科学课程，提供结构化的， 基础生物医学入门级的广泛教学和实验室培训； 建立生物统计方案，强调生物知识和生物统计的整合 原则；以及在基础生物医学科学中承认需要生物统计学投入的氛围 MUSC具有国家认可实力的四个重点领域的研究人员：蛋白质组学、脂组学、 信号和神经生物学。

The sequencing of the human genome has set the stage for the next major frontier in biology, the proteome, and has paved the way for the new "omics" era in biomedical reseach. The exploding cascadeof data requires integration of bioinformatics tools for data representation and manipulation and sophisticated statistical and mathematical approaches for validating, analyzing, modeling and, finally, unifying and interpreting the myriad of "omic" data to infer biological function. The aim of the Biostatistics Training for Basic Biomedical Research (BTBBR) program, proposed by the Department of Biostatistics, Bioinformatics, and Epidemiology at the Medical University of South Carolina (MUSC), is to train a new generation of biostatisticians who have substantial didactic and hands-on experience in the basic biomedical sciences so that they are prepared to assume key roles in this new generation of basic biomedical research. The BTBBR program stresses the integration of biostatistical theory and methods, including nonlinear systems analysis and mathematical modeling, with tools from bioinformatics to address quantitative frontiers in modern multi-disciplinary biological research. The program will capitalize on an established and successful college-wide program.offering a common basic science curriculum that providesstructured, broad-based didactic and laboratory training in the.basic biomedical sciences at an entry level; an established biostatistics program that emphasizes integration of biological knowledge and biostatistical principles; and an atmosphere of acknowledged need for biostatistical input among basic biomedical science researchers in four focus areas in which MUSC has nationally recognized strength: proteomics, lidipomics, signaling, and neurobiology.