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

免疫分析的一个核心挑战，特别是用于监测疫苗效力或疾病进展， 是确定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， 健康免疫系统及其对抗原扰动的反应是多种多样的，而不是单一的。我们 假设对来自对疫苗或病毒感染有反应的个体的数据的综合分析将 允许推断与临床定义的结果相关的免疫过程的因果链 （反应性，疾病严重程度）。研究项目3将集中在询问的时间行为， 使用新的单细胞技术和数学方法在不同的队列中进行免疫应答。的 应用时间序列基因表达数据以确定时间基因表达模式（SA 1）， 单细胞分析，以确定细胞间影响网络（SA2），和多变量统计 方法（SA3）将生成描述免疫系统的动态功能响应的模型， 系统，并确定临床结果的可测量预测因子集。该项目涉及一个合作 四个实验室：Xavier实验室（MGH/Broad）和Kleinstein实验室（耶鲁大学），具有生物信息学专业知识 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 Lauffenburger实验室（MIT/Broad），在微尺度单细胞测定和数学算法方面具有专业知识 来推断蜂窝网络我们将联合收割机的努力1）整合不同免疫反应的数据 本研究计划中的研究（健康或老年人接种疫苗，病毒自然感染）， 2)基于全面的单细胞分析生成新的网络模型。该项目将利用 来自工程和计算机科学的新兴方法，以改善生物学的详细建模 现象，并提供反馈，以完善该计划的其他领域的实验假设。的 这项健康研究的成果将是一套全面的、综合的全身免疫模型， 这些反应将为疫苗设计提供信息，并改善临床监测生物标志物的选择。

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.