New computational methods for analyzing the response to vaccination and Infection

由Daphne Koller博士、Robert Tibishani博士和ATUI Butte博士领导的这一系列生物信息学 项目将侧重于开发用于分析蜂窝网络的计算方法 在免疫系统的底层，并理解这个网络是如何被 疫苗。该项目将建立在我们成功开发的新反卷积软件(CsSAM)的基础上，以 将其扩展到开发除全血基因表达之外的其他数据集的应用程序 如血清细胞因子和磷脂。建立在现有成功工具的基础上，该团队以前 然后，将使用开发的、特定于细胞的去卷积数据来构建每个 不同的细胞类型存在于外周血中，以及它们之间的细胞间网络。集成 从该提案中的所有其他项目收集的数据，该项目将开发计算工具 用于预测疫苗反应，并用于确定常见的疫苗机制和遗传相关性 并作出回应。学习到的模型将提供关于免疫系统的新的科学见解， 包括：基因型、基因表达、磷蛋白水平和表型之间的关系； 免疫细胞内基因之间的相互作用；以及不同细胞因子之间的相互作用 免疫细胞。所有这些都将用于人体免疫指标的规范。 项目7的具体目标是开发： 目的1：混合样本数据的分析方法； 目的2：利用不同数据构建免疫系统细胞网络模型的方法； 目标3：了解影响免疫反应的因素的方法；并： 目的4：使用系统水平的分析来确定影响免疫反应的原因多态。

Led by Dr. Daphne Koller, together with Drs. Robert Tibshirani and AtuI Butte, this series of bioinformatics projects will focus on the development of computational methods for analyzing the cellular network underlying the immune system and for understanding how this network is perturbed by the response to vaccine. This project will build on our successful development of new deconvolution software (csSAM) to extend it to the development of applications to other datasets in addition to whole blood gene expression such as serum cytokines and phosphoflow. Building on existing successful tools this team has previously developed, cell-specific deconvolved data will then be used to construct the regulatory network of each of the different cell-types present in peripheral blood as well as the inter-cellular network between them. Integrating the data collected from all ofthe other projects in this proposal, this project will develop computational tools for predicting vaccine responses, and for identifying common vaccine mechanisms and genetic correlates with response. The learned models will provide novel scientific insights regarding the immune system, including: the relationships between genotype, gene expression, phosphoprotein levels, and phenotype; the interactions between genes within an immune cell; and the cytokine-mediated interaction between different immune cells. All these will be used for the specification of human immune metrics. The specific aims for Project 7 are to develop: Aim 1: Methods for analysis of data derived from mixed samples; Aim 2: Methods for using diverse data to construct network models for immune system cells; Aim 3: Methods for understanding the factors affecting immune response; and to: Aim 4: Use systems-level analysis to identify causal polymorphisms that affect immune response.