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Using Synthetic Biology to Create a Safe and Stable Ebola Surrogate for Effective Development of Detection and Therapy Platforms
Title
Using Synthetic Biology to Create a Safe and Stable Ebola Surrogate for Effective Development of Detection and Therapy Platforms.
Creator
Holmes, Douglas, Esiobu, Nwadiuto, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Biological Sciences
Abstract/Description
Ebolavirus is responsible for a deadly hemorrhagic fever that has claimed thousands of lives in Africa and could become a global health threat. Because of the danger of infection, novel Ebola research is restricted to BSL-4 laboratories; this slows progress due to both the cost and expertise required to operate these laboratories. The development of a safe surrogate would speed research and reduce risk to researchers. Two highly conserved Ebola gene segments—from the glycoprotein and...
Show moreEbolavirus is responsible for a deadly hemorrhagic fever that has claimed thousands of lives in Africa and could become a global health threat. Because of the danger of infection, novel Ebola research is restricted to BSL-4 laboratories; this slows progress due to both the cost and expertise required to operate these laboratories. The development of a safe surrogate would speed research and reduce risk to researchers. Two highly conserved Ebola gene segments—from the glycoprotein and nucleoprotein genes—were designed with modifications preventing expression while maintaining sequence integrity, spliced into high copy number plasmids, cloned into E.coli, and tested for stability, safety, and potential research applications. The surrogates were stable over 2-3 months, had a negligible mutation rate (<0.165% over the experiment), and were detectable in human blood down to 5.8E3-1.17E4 surrogates/mL. These protocols could be used to safely simulate other pathogens and promote infectious disease treatment and detection research.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013015
Subject Headings
Ebolavirus, Infectious disease research, Ebola virus disease, Synthetic biology
Format
Document (PDF)
Predictive Models for Ebola using Machine Learning Algorithms
Title
Predictive Models for Ebola using Machine Learning Algorithms.
Creator
Jain, Abhishek, Agarwal, Ankur, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Identifying and tracking individuals affected by this virus in densely populated areas is a unique and an urgent challenge in the public health sector. Currently, mapping the spread of the Ebola virus is done manually, however with the help of social contact networks we can model dynamic graphs and predictive diffusion models of Ebola virus based on the impact on either a specific person or a specific community. With the help of this model, we can make more precise forward predictions of the...
Show moreIdentifying and tracking individuals affected by this virus in densely populated areas is a unique and an urgent challenge in the public health sector. Currently, mapping the spread of the Ebola virus is done manually, however with the help of social contact networks we can model dynamic graphs and predictive diffusion models of Ebola virus based on the impact on either a specific person or a specific community. With the help of this model, we can make more precise forward predictions of the disease propagations and to identify possibly infected individuals which will help perform trace – back analysis to locate the possible source of infection for a social group. This model will visualize and identify the families and tightly connected social groups who have had contact with an Ebola patient and is a proactive approach to reduce the risk of exposure of Ebola spread within a community or geographic location.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00004919, http://purl.flvc.org/fau/fd/FA00004919
Subject Headings
Communicable diseases--Epidemiology., Public health surveillance., Ebola virus disease--Transmission., Machine learning., Computer algorithms., Virtual reality., Interactive multimedia., Computer graphics., History--Graphic methods., Historiography--Technological innovations.
Format
Document (PDF)