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.

这项调查涉及数学算法的发展，以提供快速的诊断工具，以早日检测埃博拉病毒。与当前的方法不同，所提出的方法不需要对病毒检测的受试者有症状。提出的方法可以利用这样的观察结果，即免疫系统的行为像煤矿中的金丝雀一样，提供了预警系统，如果已定量理解，可以使用该系统来识别感染，加速治疗并改善结果。检测技术包括为例如基因表达数据构建一系列数学模型，这些模型表征了健康免疫系统的标称状态。然后将这些模型应用于感染受试者中免疫系统的新颖或异常行为。最初的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方法的可行性。转录分析已被广泛应用于识别疾病分类，诊断和预后的标记。从静态的角度来看，已经开发了许多方法来确定对变化生物态（即健康和疾病状态）之间变化的信号传导途径。但是，生物状态之间的过渡是一个复杂的动态过程，是信息丰富的。在有关流感的初步工作中，使用从实验感染流感病毒的健康个体的数据中为400多个途径建立了一种非线性基因表达模型。在这些途径中，胞质DNA/RNA感应途径（用于检测病原体相关的核酸的系统）是大多数受试者中最初表现出基因表达变化的人，这些受试者成为症状，反映了免疫系统对入侵病原体的初始反应。此外，随着免疫系统反应的进行，存在一系列异常途径信号传导，反映出基因表达的变化反映出，在疾病的外部观察症状出现之前，可能会提供预警签名以检测病原体。在该项目中，将研究哺乳动物细胞对埃博拉病毒的级联反应的途径，目的是表征其疾病特异性进化的特征，该途径将用于鉴定可观察症状之前的分子签名。还将探索建模过程的灵敏度和鲁棒性。