CAREER: Representing Biological Structure Information for Multiple Uses

支持解释科学数据的技术的发展是生物信息学面临的重大挑战。分子生物学中的信息爆炸突显了维持科学模型的困难--无论是在内部还是与大量实验和理论数据都是如此。即使是最有经验的生物学家也在努力将自己的观察结果与已发表的文献相结合，以建立连贯的模型。对于任何复杂的生物现象，大量相关的实验系统和技术以指数级的比例创造了一个生物学知识网络。因此，至关重要的是要有计算技术来表示和解释多种不同的数据源，并对这些数据源进行科学的协作解释。还有必要对下一代生物信息学家进行这些技术的创造和推广方面的培训。这里提出的研究和教育活动解决了这两个问题。首先，我们详细阐述了构建下一代结构化信息资源原型的研究计划。这项工作的假设是，科学假设和作为其基础的原始数据的计算机表示可以用于支持多种活动，包括假设形成、假设探索和假设检验。其次，我们制定了一项计划，在斯坦福医学信息学培训计划中创建一个专门针对培训学生的生物信息学方法的轨道。Russ Altman博士是斯坦福大学医学系医学信息学(SMI)部门的助理教授。他还在计算机科学系担任礼仪职务。Altman博士受聘在SMI中建立强大的生物信息学存在，并将SMI的传统力量集中在基础分子生物学的重要问题上。因此，阿尔特曼博士一直专注于信息技术在基础分子生物学问题上的应用。阿尔特曼博士的这一职业发展奖有两个目标：1.第一个研究目标是创建一个试验台应用程序，用来试验生物学家与结构信息交互的方式。这个名为RiboWeb的试验床将是一个关于原核糖体16S亚单位的综合知识库。知识库将包含已公布的结构数据和已公布的结构模型的原则性表述，并将允许浏览、评估和使用这些数据和模型进行计算。这些资源将通过互联网提供，并将立即为研究这一重要结构的调查人员提供有用的功能，并允许学生试验新的信息组织、存储和检索技术，并通过经验证明其有效性。2.第二个教育目标是在斯坦福大学开发一条生物信息学的途径，提供对生物信息学领域的一般介绍，并为学生提供参与研究组成部分中描述的试验台应用等项目所需的技能。这条途径将利用医学信息科学(MIS)博士/硕士课程中已经提供的计算机科学、概率与统计、生物学和社会/伦理问题方面的正式课程。此外，这一途径将在斯坦福大学的管理信息系统培训计划中提供一门新课程，学生在该课程中学习基础生物信息学。与医学信息学的独特联系将造就一批生物信息学家，他们知道信息学其他分支中的相关技术。

The development of technologies to support interpretation of scientific data is a major challenge for bioinformatics. The information explosion within molecular biology has highlighted the difficulties in maintaining scientific models that are consistent--both internally and with the mass of experimental and theoretical data. Even the most experienced biologists struggle with the task of integrating their own observations with the published literature in order to build coherent models. For any complex biological phenomenon, the large number of relevant experimental systems and techniques creates a web of biological knowledge with exponential proportions. It is therefore critical that computational technologies exist for the representation and interpretation of multiple, diverse data sources, and for collaborative scientific interpretation of these data sources. It is further necessary that the next generation of bioinformaticians be trained in the creation and extension of these technologies. The research and education activities proposed here address both these problems. First, we elaborate a research plan to build a prototype for the next generation of structural information resources. The hypothesis of this work is that computer representations of both scientific hypotheses and the primary data upon which they are based, can be used to support multiple activities, including hypothesis formation, hypothesis exploration and hypothesis testing. Second, we elaborate a plan for creating a track within the Stanford medical informatics training program that is specifically geared towards training students in the methods of bioinformatics. Dr. Russ Altman is an Assistant Professor in the Section on Medical Informatics (SMI), within the Department of Medicine at Stanford. He also holds a courtesy appointment in the Department of Computer Science. Dr. Altman was recruited to establish a strong presence of bioinformatics within the SMI, and to focus the traditional stre ngths of the SMI on important problems in basic molecular biology. As such, Dr. Altman has focused on the application of information technology on problems in basic molecular biology. Dr. Altman's goals for this career development award are twofold: 1. The first, research goal, is to create a testbed application for experimenting with the ways in which biologists interact with structural information. This testbed, called RiboWeb, will be an integrated knowledge base about the 16S subunit of the procaryotic ribosome. The knowledge base will contain principled representations of the published structural data and published structural models, and will allow the data and the models to be browsed, evaluated and used for computation. The resource will be made available over the internet, and will provide immediately useful functionality for investigators working on this important structure, as well as allowing students to experiment with new information organization, storage and retrieval technologies, and demonstrate their efficacy empirically. 2. The second, educational goal, is to develop a pathway in bioinformatics at Stanford that provides a general introduction to the field of bioinformatics, and gives students the skills necessary to participate in research in projects such as the test bed application described in the research component. This pathway will leverage the formal coursework in computer science, probability and statistics, biology, and social /ethical issues already provided within the Medical Information Sciences (MIS) PhD/MS program. In addition, this pathway will provide a new course within the MIS training program at Stanford, in which students learn the fundamentals bioinformatics. The unique affiliation with medical informatics will create a population of bioinformaticians who are aware of relevant technologies within other branches of informatics.