RAPID: Early Warning Algorithms for Predicting Ebola Infection Outcomes

这项研究涉及的数学算法的发展，提供快速诊断工具，早期发现感染埃博拉病毒。与目前的方法不同，所提出的方法不需要受试者有检测病毒的症状。所提出的方法利用了免疫系统的行为就像煤矿中的金丝雀的观察结果，提供了一个早期预警系统，如果定量了解，可以用于识别感染，加速治疗和改善结果。检测技术包括建立一系列数学模型，例如，基因表达数据，其表征健康免疫系统的标称状态。然后，这些模型被应用于检测感染受试者中免疫系统的新的或异常的行为。最初的模型构建阶段将使用非人类灵长类动物和小鼠的数据来建立该方法的可行性。转录分析已广泛应用于识别疾病分类、诊断和预后的标志物。已经开发了许多方法来鉴定响应于不同生物状态之间的变化的信号传导途径，即，健康和疾病状态，从静态的角度来看。然而，生物状态之间的转换是一个复杂的动态过程，是信息丰富的。在流感的初步工作中，使用实验感染流感病毒的健康个体的数据，为400多个途径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（用于检测病原体相关核酸的系统）是第一个在大多数出现症状的受试者中表现出基因表达变化的途径，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展，存在异常通路信号传导的级联反应，这反映在基因表达的变化上，这可以在外部可观察到的疾病症状出现之前为病原体的检测提供早期预警信号。在该项目中，将研究哺乳动物细胞对埃博拉病毒反应的级联途径，目的是表征其疾病特异性演变的特征，这将用于在可观察到的症状之前确定诊断的分子特征。还将探讨建模过程的灵敏度和鲁棒性。

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.