Bioinformatics for Immune Response Biosignature Analyses

描述(由申请人提供)：该第二阶段提案继续开发和验证生物信息学和诊断平台，以便根据宿主基因和蛋白质组表达反应谱对生物制剂进行快速检测和症状前鉴定。我们的合作者进行的宿主-病原体初步研究表明，在完全疾病发生之前，暴露于病毒的个体对病原体表现出特殊的外周血单核细胞(PBMC)mKNA表达和血清蛋白时间模式。需要新的工具来从寄主-病原体研究产生的复杂、可变和噪声的基因组/蛋白质组数据中确定模式。我们的计算工具是基于动态贝叶斯网络(DBN)的概率能力，DBN被用来学习、建模和识别寄主病原体天然免疫反应的mRNA和蛋白质(生物签名)的动态变化模式。我们的方法的一个独特之处是将“时间”与先前的定量和定性知识相结合，从而提高了不同病原体之间的识别准确性。第一阶段S/w原型展示了概念证明，我们的计算方法从时间进程数据中生成了正确的DBN模型，模拟了对九种不同病原体类型的先天性免疫反应。该原型在将时间进程生物签名表示为DBN模型以及将未知生物签名正确地识别到正确的感染性病原体方面都表现得非常好，准确率超过98%。第二阶段的主要目标是统计显示，在动物和非人类灵长类动物模型中，Seralogix的计算框架可以使用病原体/毒素炭疽杆菌、牛痘和葡萄球菌肠毒素B的实时过程PBMC mRNA和蛋白质数据集，在宿主异质性面前提取和建模独特的宿主-病原体生物特征模式。现有的时间表数据将由我们在沃尔特里德陆军研究所和生物签名联盟(由新墨西哥大学、德克萨斯大学西南医学学院组成)的合作者提供。和劳伦斯·利弗莫尔实验室)。Seralogix相信，我们基于DBN的计算工具将在以下方面发挥重要作用：1)破译病原体/毒素的毒性和毒性的细胞信号通路和机制；2)创建新的诊断方法，用于实时、无症状的病原体识别；以及3)了解疾病的进展阶段，以帮助创建新的干预药物和治疗策略。

DESCRIPTION (provided by applicant): This Phase II proposal continues the development and validation of a bioinformatics and diagnostic platform for rapid detection and pre-symptomatic identification of biological agents on the basis of host gene and proteomic expression response profiles. Our collaborators preliminary host-pathogen studies indicate that exposed individuals display idiosyncratic peripheral blood mononuclear cell (PBMC) mKNA expression and serum protein temporal patterns to pathogenic agents prior to the onset of full illness. New tools are needed to determine the patterns from the volumes of complex, variable, and noisy genomic/proteomic data generated from host-pathogen studies. Our computational tools are based on the probabilistic power of dynamic Bayesian networks (DBNs) which are utilized to learn, model and recognize the dynamic pattern-of-change of mRNA and proteins ("biosignature") of the hostpathogen innate immune response. A unique feature of our approach is the inclusion of "time" combined with prior quantitative and qualitative knowledge that improves the recognition accuracy between different pathogenic agents. Phase I s/w prototype demonstrated proof-of-concept that our computational approach produced correct DBN models from time-course data mimicking the innate immune response to nine different pathogen types. The prototype performed remarkably well in both the representation of the time-course biosignatures as DBN models and for correctly identifying an unknown biosignature to the correct infectious agent with better than 98% accuracy. Phase II main objective is to statistically show that Seralogix's computational framework can extract and model unique host-pathogen biosignature patterns in the face of host heterogeneities using real time-course PBMC mRNA and protein datasets for the pathogens/toxin B. anthracis, cowpow, and staphylococcal enterotoxin B. in animal and non-human primate models. Existing time course data will be provided by our collaborators at the Walter Reed Army Institute of Research and the Biosignature Consortium (comprised of the University of New Mexico, the University of Texas Southwestern Med. Ctr., and Lawrence Livermore Labs). Seralogix believes that our DBN based computational tools will be important for: 1) deciphering the cellular signaling pathways and mechanisms of virulence and toxicity of pathogens/toxins; 2) creating new diagnostics for real-time, pre-symptomatic pathogenic identification, and 3) understanding the progressive stages of a disease to aid in creating new intervening drugs and therapeutic strategies.