New computational methods for analyzing the response to vaccination and Infection

用于分析疫苗接种和感染反应的新计算方法

• 批准号: 8306398
• 负责人: Daphne Koller
• 金额: $ 22.74万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-18 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8306398
• 关键词: Affect; Age; Bioinformatics; Biological Assay; Cells; Computer software; Computing Methodologies; Cytokine Signaling; Daphne plant; Data; Data Analyses; Data Set; Development; Diagnostic; Disease; Gender; Gene Expression; Genes; Genetic; Genetic Polymorphism; Genotype; Grant; Health; Human; Immune; Immune response; Immune system; Infection; Instruction; Knowledge; Learning; Literature; Machine Learning; Measurement; Measures; Mediating; Methods; Metric; Modeling; Pathway interactions; Patients; Phenotype; Phosphoproteins; Play; Population; Regulator Genes; Role; Sampling; Series; Serum; Signal Transduction; Staging; System; Techniques; Testing; Vaccination; Vaccines; Whole Blood; Work; cell type; computerized tools; cytokine; genome-wide; insight; network models; novel; peripheral blood; research study; response; therapeutic target; tool

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.

在Daphne Koller博士、Robert Tibshirani博士和AtuI Butte博士的带领下， 项目将集中于开发用于分析蜂窝网络的计算方法 免疫系统的基础，并了解这个网络是如何被对 疫苗该项目将建立在我们成功开发新的反卷积软件（csSAM）的基础上， 将其扩展到除了全血基因表达之外的其他数据集的应用程序开发 如血清细胞因子和磷酸流。在现有的成功工具的基础上， 然后，开发的细胞特异性去卷积数据将用于构建每个细胞的调控网络。 外周血中存在的不同细胞类型以及它们之间的细胞间网络。整合 从所有其他项目中收集的数据，这个项目将开发计算工具 用于预测疫苗反应，并用于识别常见的疫苗机制和遗传相关性 有回应。学习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;以及不同免疫细胞之间的相互作用。 免疫细胞。所有这些都将用于人类免疫指标的规范。 项目7的具体目标是： 目标1：混合样本数据的分析方法; 目标2：使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;以及： 目的4：使用系统水平分析，以确定影响免疫反应的因果多态性。