RAPID: Early Warning Algorithms for Predicting Ebola Infection Outcomes

RAPID：预测埃博拉感染结果的早期预警算法

• 批准号: 1513633
• 负责人: Michael Kirby
• 金额: $ 13.71万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513633&HistoricalAwards=false
• 关键词: RAPID; Early; Warning; Algorithms; Predicting

This investigation concerns the development of mathematical algorithms to provide rapid diagnostic tools for the early detection of infection by the Ebola virus. Unlike current methods, the proposed approach will not require that the subject be symptomatic for detection of the virus. The proposed methodology exploits the observation that the immune system behaves like a canary in a coal mine, providing an early warning system that, if quantitatively understood, could be used to identify infection, accelerate treatment and improve outcomes. The detection technique consists of building an array of mathematical models for, e.g., gene expression data, that characterize the nominal state of the healthy immune system. These models are then applied to detect novel, or anomalous behavior of the immune system in infected subjects. The initial model building phase will employ non-human primate and mouse data to establish viability of the approach.Transcriptional analysis has been widely applied to identify markers for disease classification, diagnosis, and prognosis. Many methods have been developed to identify the signaling pathways that respond to the changes between varying biological states, i.e., healthy and disease states, from a static viewpoint. However, the transition between biological states is a complex dynamic process that is information rich. In preliminary work on influenza, a nonlinear model of gene expression was built for over 400 pathways using data from healthy individuals that were experimentally infected with influenza virus. Of these pathways, the cytosolic DNA/RNA sensing pathway (a system for detecting pathogen-associated nucleic acids) was the first to exhibit changes in gene expression in the majority of subjects who became symptomatic, reflecting the immune system's initial response to an invading pathogen. Moreover, as the immune system response progressed, there was a cascade of anomalous pathway signaling, reflected by changes in gene expression, which could provide an early warning signature for detection of a pathogen well before externally observable symptoms of the disease appear. In this project, the pathway cascade of the mammalian cell response to Ebola virus will be investigated with the goal of characterizing the features of its disease-specific evolution, which will be used to identify molecular signatures for diagnosis prior to observable symptoms. The sensitivity and robustness of the modeling procedure will also be explored.

这项研究涉及数学算法的开发，为埃博拉病毒感染的早期检测提供快速诊断工具。与目前的方法不同，所提出的方法不需要受试者出现症状即可检测病毒。所提出的方法利用了免疫系统的行为就像煤矿里的金丝雀的观察结果，提供了一个早期预警系统，如果定量地理解该系统，可用于识别感染、加速治疗和改善结果。该检测技术包括为基因表达数据等建立一系列数学模型，以表征健康免疫系统的正常状态。然后将这些模型应用于检测受感染受试者免疫系统的新颖或异常行为。初始模型构建阶段将采用非人类灵长类动物和小鼠数据来确定该方法的可行性。转录分析已广泛应用于识别疾病分类、诊断和预后的标记。已经开发了许多方法来从静态的角度识别响应不同生物状态（即健康状态和疾病状态）之间的变化的信号通路。然而，生物状态之间的转变是一个信息丰富的复杂动态过程。在流感的初步研究中，利用实验感染流感病毒的健康个体的数据，为 400 多种通路建立了基因表达的非线性模型。在这些途径中，胞质 DNA/RNA 传感途径（一种检测病原体相关核酸的系统）最先在大多数出现症状的受试者中表现出基因表达的变化，反映了免疫系统对入侵病原体的最初反应。此外，随着免疫系统反应的进展，出现了一系列异常通路信号传导，通过基因表达的变化反映出来，这可以为在疾病的外部可观察症状出现之前检测病原体提供早期预警信号。在该项目中，将研究哺乳动物细胞对埃博拉病毒反应的途径级联，目的是表征其疾病特异性进化的特征，这将用于在可观察到的症状之前识别用于诊断的分子特征。还将探讨建模过程的敏感性和稳健性。