BWA Host-Pathogen Innate Immune S/W Analysis Tools

描述(申请人提供)：这项第二阶段SBIR研究是生物防务资助的第一阶段的继续，目的是开发新的计算工具来发现宿主-病原体遗传相互作用机制，并创建数学模型来分析和预测先天免疫和获得性免疫。有了新的计算方法来识别受基因调控的宿主免疫反应，将大大有助于促进我们对分子靶标和免疫机制的理解，这些分子靶点和免疫机制对于强大的防御病原微生物至关重要。这对于生物制剂(BWA)和其他公共卫生高度关注的常见疾病，特别是对于安全和有效地开发新疫苗和免疫治疗药物来说，是极其重要的知识。此外，帮助将大量原始基因组/蛋白质组和生理学数据转化为可操作知识的新计算工具将有助于指导和加快研究人员对新疫苗、佐剂和免疫治疗药物的研究。这不仅与生物防御有关，而且与许多其他病原体和疾病有关--特别是在研究中，最终根据动物模型预测人类反应是很重要的。第一阶段产生了很好的结果，证明我们的基于动态贝叶斯网络(DBN)的计算工具可以用于发现机制过程，并对BWAS和其他传染病的宿主免疫反应进行建模。与第一阶段一样，我们的第二阶段计算工具将基于DBN的统计/概率能力，我们计划扩大DBN的统计/概率能力，以支持和验证导致机制发现和预测免疫反应模型创建的多条件比较研究。这些模型是从基因、蛋白质和生理因素的时间进程模式中衍生出来的，我们称之为宿主的“生物特征”。我们计算方法的一个创新是能够包括时间进程数据(基因、蛋白质和生理数据)与先前的生物学知识(即调节途径、基因-基因/蛋白质-蛋白质关系、基因同源性、功能本体论等)相融合。我们认为，这极大地促进了新免疫学成分的发现和基于机制的免疫反应模型的建立。第二阶段的目标是：1)实施一种创新的多条件比较计算方法，旨在能够发现潜在的宿主-病原体遗传机制，以及2)在一组精选的病原体(布氏杆菌病、炭疽杆菌、球孢子虫病和流感)和疫苗(基因工程和减毒)上验证我们的机械发现和建模方法。对新疫苗和药物的研究正在产生大量的免疫学基因组、蛋白质组和生理反应数据。这些“原始”数据必须转化为可操作的知识，以指导和加速研究人员对新疫苗、佐剂和免疫治疗药物的研究。Seralogix用于识别受基因调控的宿主免疫反应的新计算方法将大大有助于促进我们对分子靶标和免疫机制的理解，这些分子靶标和免疫机制对于强大的防御病原微生物至关重要。这不仅与生物防御有关，而且与许多其他病原体和疾病有关--特别是在研究中，最终根据动物模型预测人类反应是很重要的。Seralogix认为，这种工具将能够发现新的免疫机制，因此具有巨大的商业潜力。

DESCRIPTION (provided by applicant): This Phase II SBIR research is the continuation of a Biodefense funded Phase I with the objective to develop new computational tools to discover host-pathogen genetic interactive mechanisms and create mathematical models for analyzing and predicting innate and adaptive immunity. Having new computational methods for identifying genetically regulated host immune response will significantly aid in advancing our understanding of the molecular targets and immunologic mechanisms critical to robust defense against pathogenic microbes. This is extremely important knowledge for biowarfare agents (BWA) and other common diseases of high public health concern especially for the safe and effective development of new vaccines and immunotherapeutic drugs. Further, new computational tools to help transform volumes of raw genomic/proteomic and physiologic data to actionable knowledge will help guide and accelerate researchers' investigations for new vaccines, adjuvants, and immunotherapeutic drugs. This is relevant, not only to biodefense, but for many other pathogens and diseases -- especially in studies where it is important to eventually predict human response from animal models. Phase I produced excellent results in demonstrating that our computational tools based on dynamic Bayesian networks (DBNs) can be used to discover mechanistic processes and model the host immune response to BWAs and others infectious diseases. As in Phase I, our Phase II computational tools will be based on the statistical/probabilistic power of DBNs which we plan to expand to enable and validate for multi-conditional comparative studies leading to mechanistic discovery and creation of predictive immune response models. These models are derived from the time-course patterns of genes, proteins and physiologic factors which we call the host's "biosignature". An innovation of our computational approach is the ability to include time-course data (gene, protein, and physiologic data) fused with prior biological knowledge (i.e. regulatory pathways, gene-gene/protein-protein relations, gene homologies, functional ontologies, etc.) which we believe significantly advances the discovery of novel immunologic components and the creation of mechanistic-based immune response models. The Phase II goals are to: 1) implement an innovative multi-conditional comparative computational methodology designed to enable the discovery of underlying host-pathogen genetic mechanisms, and 2) the validation of our mechanistic discovery and modeling methodology on a select set of pathogens (B. melitensis (Brucellosis), B. anthracis (anthrax), Coccidioides immitis (Valley fever), and Influenza) and vaccines (genetically engineered and attenuated). The investigation of new vaccines and drugs are producing huge volumes of "raw" immunologic genomic, proteomic and physiological response data. This "raw" data must be transformed into actionable knowledge to guide and accelerate researchers' investigations for new vaccines, adjuvants, and immunotherapeutic drugs. Seralogix's new computational methods for identifying genetically regulated host immune response will significantly aid in advancing our understanding of the molecular targets and immunologic mechanisms critical to robust defense against pathogenic microbes. This is relevant, not only to biodefense, but for many other pathogens and diseases -- especially in studies where it is important to eventually predict human response from animal models. Seralogix believes that such tools will be capable of discovering novel immunologic mechanism and thus have significant commercial potential.