Computational Modeling Core

项目总结(见说明)： 计算建模核心(CMC)的目标是通过迭代计算和实验方法开发病毒致病机理的预测模型。我们的系统病毒学计划将在人类和动物模型系统中的感染过程中为多个宿主组织系统生成转录(mRNA和miRNA)、蛋白质组、脂组和代谢组数据。CMC将通过为实验设计、数据处理和预测建模的所有方面提供计算专业知识，为该计划做出贡献。我们拥有一支在生物信息学、统计学和机械建模方面具有专业知识的多学科团队，以提供支持核心目标的综合能力工具箱。 我们的计划利用研究项目、技术核心和建模人员之间持续的、已建立的合作，以及与流感病毒感染有关的大量现有数据。我们将利用这些数据和我们的专业知识，扩大我们对埃博拉和西尼罗河病毒感染发病机制的研究。具体地说，中央军委的目标是： 通过对差异表达的统计评估，识别和量化病毒感染起始和进展过程中宿主反应通路的变化。 比较和对比不同病毒病原体感染宿主组织中激活的途径，并特异性地识别那些参与致病的途径。 发展定量预测寄主-病原体动态相互作用的数学模型。 利用这些模型来确定可以通过治疗干预来操纵的宿主-病毒相互作用的新的紧急性质。 我们正在进行的计划的更新将使用新的数据类型(miRNA、磷蛋白质组学、脂质组学和代谢组学)来增强现有数据，并提供宿主反应路径的机械建模和虚拟组织建模。

PROJECT SUMMARY (See instructions): The objective of the Computational Modeling Core (CMC) is to develop predictive models of viral pathogenesis through iterative computational and experimental approaches. Our Systems Virology program will generate transcriptomic (mRNA & miRNA), proteomic, lipidomic and metabolomic data for multiple host tissue systems over at time course of infection in both human and animal model systems. The CMC will contribute to the program by providing computational expertise for all aspects of experimental design, data processing and predictive modeling. We have a multi-disciplinary team with expertise in bioinformatics, statistics and mechanistic modeling to provide an integrated toolbox of capabilities to support the Core goals. Our program leverages ongoing, established collaborations between the research projects, technology cores, and modelers, as well as substantial existing data with Influenza virus infections. We will utilize these data and our expertise to extend our investigations into the pathogenesis of Ebola and West Nile virus infections. Specifically, the goals for the CMC are: To identify and quantify changes in host response pathways during initiation and progression of viral infection through statistical evaluation of differential expression. ¿ To compare and contrast pathways activated in host tissues infected by different viral pathogens and specifically identify those involved in pathogenicity. ¿ To develop mathematical models which quantitatively predict dynamical host-pathogen interactions. To utilize the models to identify novel emergent properties of host-virus interactions that can be manipulated through therapeutic intervention. The renewal of our ongoing program will augment existing data with new data types (miRNA, phosphoproteomics, lipidomics and metabolomics), as well as provide mechanistic modeling of host response pathways and virtual tissue modeling.