Integrated, Interdisciplinary, Inter-university PHD Program Computational Biology

描述(由申请人提供)：生物医学研究的实践在最近几年经历了巨大的变化，主要是由高通量数据生成的新生物技术推动的。这些技术包括用于成像、基因测序、蛋白质组学、结构确定的高通量方法，以及许多其他任务，这些任务现在使得从分子到生物水平的生命系统的许多方面的精细表征成为可能。生物技术的这些进步及其产生的海量数据使生物医学研究发生了革命性的变化。然而，它们也创造了对能够在一个日益依赖数据驱动和依赖先进计算方法的领域工作的科学家的迫切需求。特别是，现代生物医学研究依赖于新一代在计算和数学上成熟的研究人员，他们能够理解新的生物技术，开发出理解其数据所需的创新数学模型和计算机算法，并将这些知识应用于推动生物和医学的进步。要做到这一点，这些研究人员需要对计算科学、他们所从事的生物医学应用以及为他们提供信息的生物和物理科学有很强的了解。卡内基梅隆大学/匹兹堡大学计算生物学博士项目(CPCB)就是为了满足这种对计算生物学专家的培训需求而设立的。该项目旨在培养未来的计算生物学领导者：对计算理论、生物和物理科学有深厚知识的研究科学家，以及在这些领域的交叉点上不断增长的专业跨学科知识。为了实现这一目标，该计划利用了两个主办机构的共同优势，这两个机构共同在计算机科学、工程和医学研究方面处于世界领先地位，在计算生物学研究和教育方面拥有长期的创新记录。培训计划包括一个涵盖宽泛定义的计算生物学基础的创新课程，以及大量的高级选修课程，涵盖计算生物学研究的四个广泛领域：生物图像信息学、细胞和系统建模、计算基因组学和计算结构生物学。该项目的学生在计算生物学研究前沿的众多实验室中的任何一个进行论文研究。课程和论文研究的这些主要组成部分还有许多机制作为补充，以促进学生的成功，促进专业发展，鼓励负责任的研究行为，并帮助招募和留住代表性不足的群体。拟议的计划旨在为更广泛的CPCB研究生计划中选定的部分学生提供培训支持。它将为最有前途的学生提供为期两年的研究支持，为他们提供额外的资源和灵活性，以追求最具创新性的研究方向，并帮助他们发展成为未来计算生物学和生物医学研究的整体领导者。

DESCRIPTION (provided by applicant): The practice of biomedical research has undergone dramatic changes in recent years, largely driven by new biotechnology for high-throughput data generation. These technologies include high-throughput methods for imaging, genetic sequencing, proteomics, structure determination, and numerous other tasks that now make it possible to finely characterize numerous aspects of living systems from the molecular to the organismal levels. These advances in biotechnology and the vast amounts of data they are producing have revolutionized biomedical research. They have also, however, created a pressing need for scientists capable of working in a field that is increasingly data-driven and dependent on advanced computational methods. In particular, modern biomedical research depends on a new breed of computationally and mathematically sophisticated researchers who can understand new biotechnologies, develop innovative mathematical models and computer algorithms needed to make sense of their data, and apply this knowledge to drive biological and medical advances. To do so, these researchers require a strong command of computational science, the biomedical applications on which they work, and the biological and physical sciences that inform them. The Carnegie Mellon University/University of Pittsburgh Ph.D. Program in Computational Biology (CPCB) was created to meet this need for training experts in computational biology. The program aims to prepare the future leaders of computational biology: research scientists with deep knowledge of computational theory, biological and physical sciences, and a growing body of specialized interdisciplinary knowledge at the intersection of these areas. To accomplish this, the program leverages the shared strengths of its two hosts institutions, collectively world leaders in computer science, engineering, and medical research with long track records of innovation in computational biology research and educational. The training program includes an innovative curriculum covering fundamentals of computational biology, broadly defined, and a large body of advanced elective coursework spanning four broad domains of computational biology research: bioimage informatics, cellular and systems modeling, computational genomics, and computational structural biology. Program students perform thesis research in any of numerous laboratories at the cutting edge of computational biology research. These primary components of coursework and thesis research are supplemented by numerous mechanisms to facilitate student success, promote professional development, encourage responsible conduct of research, and aid in recruiting and retaining underrepresented groups. The proposed program seeks to renew training support for a select subset of students in the broader CPCB graduate program. It will provide the most promising students with two years of research support, providing them added resources and flexibility to pursue the most innovative research directions and to aid in their development into future leaders of computational biology and biomedical research as a whole.