Immune System Biological Networks:Case Study Improved Data Integration & Analysis

描述(申请人提供)：生物医学研究的进展及其转化为临床实践需要跨多个尺度(分子、细胞、生物体)、生物体类型和研究领域的数据集成。在传染病研究中，对数据整合的需求尤其迫切，在传染病研究中，生物体在所有范围内相互作用，这些相互作用导致出现特定于这些相互作用的过程和结构。真正的数据集成，即联合解释和分析不同类型的数据的能力，取决于将数据链接到数据所涉及的生物实体的信息的能力。面对迅速增长的数据量和信息量，从数据到信息的这种联系必须是可计算的。要自动处理数据和信息之间的链接，需要使用通用的正式知识表示系统来表示它们。生物学中的知识表示的努力要么集中在本体开发上，要么集中在路径表示上。虽然两者的价值都是毋庸置疑的，但它们都不能完全支持传染病研究的数据和信息集成需求。我们提出了一种基于本体的路径表示方法，它将本体从单一的分类和路径表示扩展到所有级别的粒度，从而允许对复杂生物系统的表示。我们的方法建立在现有的本体论和路径表示的基础上，但基于形式的本体论和逻辑原则。我们的总体目标是经验性地测试基于本体的表示可以在多大程度上改进对转化医学的数据解释和分析。我们将以金黄色葡萄球菌感染为例，利用杜克大学金黄色葡萄球菌菌血症小组的宝贵数据资源。我们将通过以下三个具体目标来实现我们的目标：1.创建基于本体的宿主-病原体相互作用的表示，重点关注金黄色葡萄球菌菌血症。2.经验性地测试在目标1中创建的基于本体的表示通过使用该表示来预测与金黄色葡萄球菌菌血症相关的疾病基因来改进数据分析和解释的能力。3.经验性地测试在目标1中创建的基于本体的表示对于理解金黄色葡萄球菌的致病机理、识别新的治疗靶点以及通过实验测试目标2中所做出的疾病基因预测对改善患者管理的影响。预期的结果是：用于表示复杂生物系统的基于本体的方法和宿主-病原体相互作用的本体，两者都经受了旨在证明其对临床和翻译研究的有效性的测试；对细菌病原体的免疫反应的更好的理解；根据这一提议开发的资源将直接改善数据整合、检索和分析，将支持传染病研究中的跨学科合作，并将为生物医学其他领域开发类似资源提供基础，从而显著影响生物医学研究和转化医学。

DESCRIPTION (provided by applicant): Progress in biomedical research and its translation into clinical practice require the integration of data across multiple scales (molecules, cells, organisms), organism types, and fields of research. The need for data integration is especially acute in infectious disease research where organisms interact on all scales, and these interactions result in the emergence of processes and structures specific to these interactions. True data integration, the ability to jointly interpret and analyze data of heterogeneous types, depends on the ability to link data to information about the biological entities to which the data refer. In the face of rapidly growing volumes of data and information, it is imperative that this link from data to information be computable. Automated processing of the links between data and information requires that they be expressed using a common, formalized system for knowledge representation. Efforts at knowledge representation in biology have focused on either ontology development or pathway representation. While the value of both is unquestionable, neither fully supports the data and information integration needs of infectious disease research. We propose an ontology-based approach to pathway representation that extends ontologies beyond single taxonomies and pathway representations to all levels of granularity, thereby allowing the representation of complex biological systems. Our approach builds upon existing ontologies and pathway representations but is grounded in formal ontological and logical principles. Our overall goal is to test empirically the degree to which the ontology-based representation can improve data interpretation and analysis for translational medicine. We will take as our case study Staphylococcus aureus infection, utilizing the invaluable data resources of the Duke Staphylococcus aureus Bacteremia Group. We will achieve our goal through the following three specific aims: 1. Create an ontology-based representation of host-pathogen interactions, focusing on Staphylococcus aureus bacteremia. 2. Empirically test the ability of the ontology-based representation created in Aim 1 to improve data analysis and interpretation by using the representation to predict disease genes associated with Staphylococcus aureus bacteremia. 3. Empirically test the impact of the ontology-based representation created in Aim 1 on understanding of Staphylococcus aureus pathogenesis, on identification of novel therapeutic targets, and on improvement to patient management by testing experimentally the disease gene predictions made under Aim 2. The anticipated outcomes are: an ontology-based method for the representation of complex biological systems and an ontology of host-pathogen interactions, both subjected to tests designed to demonstrate their utility to clinical and translational research; an improved understanding of the immune response to bacterial pathogens; and the identification of genes associated with Staphylococcus aureus bacteremia that can be used to develop novel diagnostics and therapeutics.The resources developed under this proposal will directly improve data integration, retrieval and analysis, will support cross-disciplinary collaborations within infectious disease research, and will provide a foundation from which to develop similar resources for other areas in biomedicine, thus significantly impacting biomedical research and translational medicine.