RAPID: Early Warning Algorithms for Predicting Ebola Infection Outcomes

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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.