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

免疫系统生物网络：案例研究改进的数据集成

• 批准号: 7927981
• 负责人: LINDSAY G. COWELL
• 金额: $ 7.41万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-22 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7927981
• 关键词: Acute; Area; Bacteremia; Biological; Biology; Biomedical Research; Blood Circulation; Candidate Disease Gene; Case Study; Cells; Clinical; Clinical Data; Collaborations; Communities; Data; Data Analyses; Development; Disease; Face; Foundations; Genes; Genotype; Goals; Immune response; Immune system; Infection; Infectious Diseases Research; Information Resources; Knowledge; Link; Methods; Molecular; Ontology; Organism; Outcome; Pathogenesis; Pathway interactions; Patients; Process; Research; Resources; Retrieval; Sample Size; Source; Staphylococcus aureus; Structure; System; Taxonomy; Testing; Therapeutic; Translational Research; Translations; base; clinical practice; complex biological systems; data integration; design; improved; information organization; new therapeutic target; novel diagnostics; pathogen; success; syntax; translational medicine

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.

描述（由申请人提供）：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），生物体类型和研究领域的数据整合。在传染病研究中，对数据整合的需求尤其急切，在这些研究中，生物体在所有尺度上相互作用，这些相互作用导致这些相互作用特有的过程和结构的出现。真实的数据集成，共同解释和分析异质类型数据的能力取决于将数据链接到有关数据所指的生物实体的信息的能力。面对数据和信息的快速增长，必须计算从数据到信息的链接。数据和信息之间链接的自动处理要求它们使用通用的，正式的系统以进行知识表示。生物学中知识代表的努力集中在本体发展或途径代表上。尽管两者的价值都是毫无疑问的，但两者都不完全支持传染病研究的数据和信息整合需求。我们提出了一种基于本体的途径表示方法，该方法将本体分类法和途径表示以外的本体扩展到各个层次的粒度，从而允许复杂生物系统的代表。我们的方法基于现有的本体和途径表示形式，但基于形式的本体论和逻辑原则。我们的总体目标是从经验上测试基于本体的表示可以改善转化医学的数据解释和分析的程度。我们将利用杜克葡萄球菌金黄色葡萄球菌菌群的宝贵数据资源来为案例研究金黄色葡萄球菌感染。我们将通过以下三个特定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，重点是金黄色葡萄球菌菌血症。 2。通过经验测试AIM 1中基于本体的表示的能力，通过使用代表来预测与金黄色葡萄球菌菌血症相关的疾病基因来改善数据分析和解释。 3。在凭经验上测试目标1中基于本体的表征对理解金黄色葡萄球菌发病机理的理解，对新的治疗靶标的识别以及通过实验测试AIM中的疾病基因预测来改善患者管理的鉴定以及对患者管理的改进，以实现目标2。证明他们对临床和转化研究的实用性；对细菌病原体的免疫反应有了改进的理解。以及可用于开发新颖的诊断和治疗学的与金黄色葡萄球菌相关的基因的鉴定。该提案下开发的资源将直接改善数据整合，检索和分析，支持感染性疾病内的跨学科合作，并将为生物培养的其他领域提供类似的研究基础，从而为生物培养的构成影响，从而为生物介绍而构成了相似的研究。