Bioinformatics for Immune Response Biosignature Analyses

描述（由申请人提供）：本II期提案继续开发和验证生物信息学和诊断平台，用于基于宿主基因和蛋白质组表达反应谱快速检测和症状前鉴定生物制剂。我们的合作者的初步宿主-病原体研究表明，暴露个体在完全疾病发作之前对病原体显示特异质外周血单核细胞（PBMC）mKNA表达和血清蛋白时间模式。需要新的工具来确定从宿主-病原体研究产生的复杂的、可变的和嘈杂的基因组/蛋白质组数据的量的模式。我们的计算工具是基于动态贝叶斯网络（DBN）的概率功率，其用于学习，建模和识别宿主病原体先天免疫应答的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包括“时间”与先前的定量和定性知识相结合，提高了不同病原体之间的识别准确性。I期s/w原型证明了我们的计算方法从模拟对九种不同病原体类型的先天免疫反应的时程数据产生正确的DBN模型的概念验证。该原型在将时间过程生物特征表示为DBN模型以及正确识别未知生物特征以正确的传染性病原体方面表现得非常好，准确率超过98%。第二阶段的主要目标是统计学上表明，Seralogix的计算框架可以使用病原体/毒素B的真实的时程PBMC mRNA和蛋白质数据集，在面对宿主异质性时提取和建模独特的宿主-病原体生物特征模式。炭疽菌、牛瘟和葡萄球菌肠毒素B。在动物和非人类灵长类动物模型中。现有的时间过程数据将由我们在沃尔特里德陆军研究所和生物签名联盟（由新墨西哥州大学，得克萨斯大学西南医学中心，和Lawrence Livermore Labs）。Seralogix认为，我们基于DBN的计算工具将对以下方面非常重要：1）破译病原体/毒素的毒力和毒性的细胞信号传导途径和机制; 2）创建用于实时、症状前病原体识别的新诊断方法; 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.