BWA Host-Pathogen Innate Immune S/W Analysis Tools

描述（由申请人提供）：Seralogix，Inc.提出开发和验证新的计算工具和方法，结合智能框架，用于识别，分析和建模与生物战感染因子的宿主-病原体先天免疫和炎症反应相关的机制和途径。我们的核心计算工具是基于动态贝叶斯网络（DBN）的统计能力，它被用来模拟复杂的动态模式的变化的DNA，mRNA，蛋白质和代谢物，我们称之为“时间生物签名”的主机病原体的反应。DBN是基于健全的统计方法，使我们能够将联合收割机先验知识与时间过程的经验数据结合起来，以生物系统的角度破译宿主-病原体生物特征。 由于基因组学和蛋白质组学研究产生的复杂多维数据，需要具有内置智能的新的计算工具来服务于21世纪的研究需求。我们的方法利用“智能软件代理”的力量来创建一个集成框架，自动化：1）DBN方法中的步骤，2）导入和管理复杂的基因组和蛋白质组数据，3）从其他来源持续收集和更新先验知识，4）DBN模型的智能和纠错构建和修改，5）适当的计算技术和分析算法的指导选择和应用，6）结果的可视化，以及7）自学过程的实施。 我们的方法的一个独特之处是增加了“时间”的维度，结合我们认为能够实现新的宿主-病原体时程研究的先验知识，这些研究有望破译细胞内和细胞间信号传导域之间的因果关系。我们相信，我们的方法和工具将有助于识别新的靶点/干预途径，并在统计学上证实干预药物和治疗方法的生物学影响。我们假设，我们的DBN方法应该大大提高从较少的实验数据推断先天性和炎症途径的统计学意义，同时也面临噪音，隐藏和/或丢失的数据点。第一阶段的目标是开发我们的计算工具和框架的“测试”版本，以证明可行性和优点。该小组包括Seralogix Jnc，德克萨斯大学医学分支微生物学和免疫学系以及加尔维斯顿的生物防御中心;德克萨斯大学生物医学发明中心（UTSWCBI），达拉斯;以及沃尔特里德陆军研究所。

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.