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，生物体），生物体类型和研究领域的数据整合。在传染病研究中，生物体在所有尺度上相互作用，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，对数据集成的需求尤其迫切。真正的数据集成，即联合解释和分析异构类型数据的能力，取决于将数据与数据所指生物实体的信息联系起来的能力。面对快速增长的数据和信息量，从数据到信息的这种联系必须是可计算的。数据和信息之间的联系的自动化处理需要使用一个通用的、形式化的知识表示系统来表示。生物学知识表示的努力集中在本体论的发展或途径表示。虽然两者的价值是毋庸置疑的，但两者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途径表示方法，将本体扩展到单个分类和途径表示之外的所有级别的粒度，从而允许表示复杂的生物系统。我们的方法建立在现有的本体和途径表示，但在正式的本体和逻辑原则的基础上。我们的总体目标是测试经验的程度，基于本体的表示可以提高数据的解释和分析转化医学。我们将利用杜克金黄色葡萄球菌菌血症小组的宝贵数据资源，以金黄色葡萄球菌感染为例进行研究。我们将通过以下三个具体目标来实现我们的目标：1。创建宿主-病原体相互作用的基于本体的表示，重点关注金黄色葡萄球菌菌血症。2.通过使用表示来预测与金黄色葡萄球菌菌血症相关的疾病基因，实证测试目标1中创建的基于本体的表示改善数据分析和解释的能力。3.通过实验测试目标2下的疾病基因预测，实证测试目标1中创建的基于本体的表示对理解金黄色葡萄球菌发病机制、识别新治疗靶点以及改善患者管理的影响。预期成果是：一个基于本体论的方法，用于表示复杂的生物系统和宿主-病原体相互作用的本体论，两者都经过了旨在证明其在临床和转化研究中的实用性的测试;以及鉴定与金黄色葡萄球菌菌血症相关的基因，可用于开发新的诊断和治疗方法。该提案将直接改善数据集成、检索和分析，支持传染病研究领域的跨学科合作，并为生物医学其他领域开发类似资源提供基础，从而对生物医学研究和转化医学产生重大影响。