Mathmatical models for immune signatures from population- and single-cell-base an

免疫图谱的一个核心挑战，特别是监测疫苗效力或疾病进展， 已经确定了可以预测免疫反应结果的可测量参数。 考虑到个体反应的异质性，我们预计定义一个 健康的免疫系统及其对抗原干扰的反应是多种多样的，而不是单一的。我们 假设对对疫苗或病毒感染有反应的个体的数据进行综合分析将 允许推断与临床定义的结果相关的免疫过程的因果链 (响应性、疾病严重性)。研究项目3将集中于询问时间行为 使用新的单细胞技术和数学方法在不同队列中的免疫反应。这个 应用时间序列基因表达数据来确定时间基因表达模式(SA1)， 确定细胞间影响网络的单细胞分析(SA2)和多变量统计 方法(SA3)将生成描述免疫的动态功能反应的模型 系统，并确定临床结果的可测量预测因素集。该项目涉及一项协作 在四个实验室中：拥有生物信息学专业知识的泽维尔实验室(MGH/布罗德)和克莱因斯坦实验室(耶鲁) 和先天免疫和适应性免疫的建模方法，以及Love实验室(麻省理工学院/布罗德研究所)和 劳芬堡实验室(麻省理工学院/布罗德)，在微型单细胞分析和数学算法方面拥有专业知识 来推断蜂窝网络。我们将共同努力1)整合关于不同免疫反应的数据 在本研究计划中进行的研究(健康或老年人接种疫苗，病毒自然感染)和 2)在综合单细胞分析的基础上生成新的网络模型。这个项目将利用 工程学和计算机科学新出现的改进生物详细建模的方法 并提供反馈以完善该计划其他领域的实验假设。这个 这项健康研究的成果将是一套全面的、集成的系统免疫模型 将为疫苗设计提供信息并改进用于临床监测的生物标志物选择的反应。

A central challenge for immune profiling, particularly for monitoring vaccine efficacy or disease progression, has been the identification of measurable parameters that can predict the outcome of an immune response. Given the heterogeneity of individual responses, we expect that the characteristic factors that define a healthy immune system, and its response to antigenic perturbations, are manifold rather than singular. We hypothesize that integrative analysis of data from individuals responding to vaccines or viral infections will allow for inference of causal chains of immunological processes associated with clinically defined outcomes (responsiveness, disease severity). Research Project 3 will focus on interrogating temporal behaviors of immune responses in diverse cohorts using novel single-cell techniques and mathematical methods. The application of time-series gene expression data to determine temporal gene expression patterns (SA1), single-cell analyses to determine intercellular influence networks(SA2),and multivariate statistical approaches (SA3) will generate models that describe the dynamic functional responses of the immune system, and identify sets of measurable predictors of clinical outcomes. This project involves a collaboration among four labs: The Xavier lab (MGH/Broad) and the Kleinstein lab (Yale) with expertise in bioinformatics and modeling approaches to innate and adaptive immunity, and the Love lab (MIT/Broad Institute) and the Lauffenburger lab (MIT/Broad) with expertise in microscale single-cell assays and mathematical algorithms to infer cellular networks. We will combine efforts 1) to integrate data on the diverse immune responses studied in this research program (vaccinations in healthy or aged persons, natural infections by viruses) and 2) to generate new network models based on comprehensive single-cell analyses. This project will leverage emerging approaches from engineering and computer science to improve detailed modeling of biological phenomena and provide feedback to refine experimental hypotheses in other areas of the program. The outcome of this research for health will be a set of comprehensive, integrated models for systemic immune responses that will inform vaccine design and improve the selection of biomarkers for clinical monitoring.