Core E - Data Analysis and Modeling Core

摘要 从系统层面了解登革热病毒(DENV)宿主关系，特别是网络结构和 动力学，可以通过跨数据集和建模的计算分析从实验数据中得出。这个 宿主对感染的反应是一个复杂的过程，涉及整个RNA转录、蛋白质信号和 相辅相成地影响细胞、组织和整个生物体行为的代谢。这种复杂性要求 一种了解免疫反应的系统生物学方法，因为对单一途径的研究不太可能 解释整个网络正在发生的变化。数据分析和建模核心(核心E)不仅将 对每个数据集执行标准多变量分析，以找到可靠的生物标志物来区分结果 感染，但还将进一步将它们与全面的公共网络和路径数据相结合，以构建 具有生物学意义的DENV-宿主相互作用的多尺度、整体网络模型。因为这个模型是 量化和数学定义，它非常适合于训练高级分类器，可以预测两者 个体化的临床结果比单独的生物标记物和新的关键驱动因素生物分子更准确 可通过体外siRNA筛选进行验证(项目3)。这些数据应该进一步说明以下因素之间的协同作用 多条相互关联的分子通路和网络支撑着两种病毒的表型差异 个人。我们提出的DENV模型的规模是史无前例的，跨越基因组、转录和 蛋白质组、细胞间信号和免疫细胞亚群水平-只有在这种规模的建模下才能 解决每个项目中关键生物学问题的卓越的、无偏见的、数据驱动的模型涌现出来， 解释宿主对DENV感染和疫苗接种影响临床结果的各种微妙之处。

SUMMARY A system-level understanding of the dengue virus (DENV) host relationship, in particular, the network structure and dynamics, can be derived from experimental data with computational analysis across data sets and modeling. The host response to infection is a complex process involving entire networks of RNA transcription, protein signaling, and metabolism that complementarily influence cellular, tissue, and whole organism behaviors. This complexity demands a systems biology approach for understanding immune response, since investigation of single pathways is unlikely to explain changes taking place across the entire network. The Data Analysis and Modeling Core (Core E) will not only perform standard multivariate analyses on each dataset to find reliable biomarkers for differentiating outcomes of infection, but will furthermore integrate them with the full range of public network and pathway data to construct a multiscale, holistic network model of biologically meaningful DENV-host interactions. Because this model is quantitative and mathematically defined, it is well suited for training advanced classifiers that can predict both individualized clinical outcomes with more accuracy than biomarkers alone and novel “key driver” biomolecules that can be validated with ex vivo siRNA screens (Project 3). These data should further inform on the synergy among multiple interrelating molecular pathways and networks that underpin the differences in phenotype between individuals. The scale of our proposed model for DENV is unprecedented, spanning the genomic, transcriptomic, proteomic, intercellular signaling, and immune cell subpopulation levels—and only with this scale of modeling will superior unbiased, data driven models that address the key biological questions in each of the Projects emerge, explaining the diverse subtleties of host response to DENV infection and vaccination that affect clinical outcomes.