BWA Host-Pathogen Innate Immune S/W Analysis Tools

BWA 宿主病原体先天免疫软件分析工具

• 批准号: 6736146
• 负责人: Kenneth L Drake
• 金额: $ 50万
• 依托单位: SERALOGIX
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6736146
• 关键词: bactericidal immunity; bioterrorism /chemical warfare; computational biology; computer program /software; computer system design /evaluation; functional /structural genomics; host organism interaction; immunity; inflammation; model design /development; proteomics; statistics /biometry

DESCRIPTION (provided by applicant): Seralogix, inc. is proposing to develop and validate new computational tools and methods in conjunction with an intelligent framework for the identification, analysis, and modeling of the mechanisms and pathways associated with the host-pathogen innate immune and inflammatory responses to biowarfare infectious agents. Our core computational tool is based on the statistical power of dynamic Bayesian networks (DBNs), which is utilized to model the complex dynamic pattern-of-change of DNA, mRNA, proteins and metabolites, which we refer to as the "temporal biosignature" of the host-pathogen response. DBNs are based on sound statistical methods that allow us to combine prior knowledge with time-course empirical data for deciphering host-pathogen biosignatures with a biological system perspective. Because of the complex multi-dimensional data resulting from genomic and proteomic investigations, new computational tools, with built in intelligence, are required to serve the investigative needs of the 21st century. Our approach utilizes the power of "intelligent software agents" to create an integrated framework that automates: 1) steps in our DBN methodology, 2) importing and managing complex genomic and proteomic data, 3) the continuous collection and updating of prior knowledge from other sources, 4) the intelligent and error correcting construction and modification of DBN models, 5) the guided selection and application of appropriate computational techniques and analytical algorithms, 6) the visualization of results, and 7) the implementation of a self-learning process. A unique feature of our approach is the added dimensionality of "time" combined with prior knowledge that we believe enables new host-pathogen time-course investigations that hold the promise of deciphering the causal relationships across the intracellular and intercellular signaling domains. We believe that our methods and tools will aid in the identification of new targets/pathways of intervention and statistically confirm biological impact of intervening drugs and therapeutic treatments. We hypothesize that our DBN methodology should substantially improve the statistical significance for inferring innate and inflammatory pathways from less experimental data while also confronting noisy, hidden, and/or missing data points. The Phase I goal is to develop a "beta" version of our computational tools and framework to demonstrate feasibility and merit. The Team includes Seralogix Jnc, the University of Texas Medical Branch Department of Microbiology and Immunology and the Center for Biodefense, Galveston; the University of Texas Center for Biomedical inventions (UTSWCBI), Dallas; and the Walter Reed Army institute of Research.

描述（由申请人提供）：Seralogix，Inc。提议开发和验证新的计算工具和方法，并结合与宿主 - 病原体先天免疫和对BiowArafare感染剂的宿主 - 培养基先天免疫和炎症反应相关的机制和途径的识别，分析和建模的智能框架。我们的核心计算工具基于动力贝叶斯网络（DBN）的统计能力，该网络（DBN）用于建模DNA，mRNA，蛋白质和代谢物的复杂动态变化模式，我们将其称为宿主 - 病原体响应的“时间生物签名”。 DBN基于合理的统计方法，使我们能够将先验知识与时间表经验数据结合起来，以使用生物系统观点来解密宿主 - 病原体生物签名。 由于基因组和蛋白质组学研究产生的复杂的多维数据，具有内置智能的新计算工具需要满足21世纪的调查需求。我们的方法利用“智能软件代理”的力量创建一个自动化的集成框架：1）我们的DBN方法中的步骤，2）进口和管理复杂的基因组和蛋白质组学数据，3）其他来源的先验知识的持续收集和更新，4）4）4）智能和错误的构建和适用于DBN模型，5）DBN的构建和修改，5）5）指导的技术，5）结果的可视化和7）实施自我学习过程。 我们方法的一个独特特征是“时间”的附加维度以及我们认为可以实现新的宿主 - 疾病时间课程调查的知识，该研究有望破译跨细胞内和细胞间信号传导域的因果关系。我们认为，我们的方法和工具将有助于鉴定干预措施的新靶/途径，并在统计上证实介入药物和治疗治疗的生物学影响。我们假设我们的DBN方法应该从较少的实验数据中推断先天和炎症途径的统计学意义，同时还要面对嘈杂，隐藏和/或缺失的数据点。第一阶段的目标是开发我们的计算工具和框架的“ beta”版本，以证明可行性和优点。该小组包括德克萨斯大学微生物学和免疫学医学分公司的Seralogix JNC以及Galveston的Biodefense中心；得克萨斯大学生物医学发明中心（UTSWCBI），达拉斯；和沃尔特·里德陆军研究所。