Signature Science is awarded IARPA contract to create a laboratory model to predict the evolution of pandemic viruses

Published October 5th, 2020 by Renee

SARS-CoV-2 genes will be used to support the proof-of-concept for RAVEN (Rapid Attenuated Virus EvolutioN) model development.

AUSTIN, TEXAS – October 06, 2020 – Signature Science, LLC has been awarded a one-year contract by IARPA, under its Broad Agency Announcement for early stage research to provide rapid capability against the current COVID-19 pandemic and enhanced warning and response capacity for future similar events. Under the contract, Signature Science will establish a laboratory model of viral evolution for pandemic viruses by employing state-of-the-art synthetic biology techniques.

Signature Science will use its laboratory model, RAVEN (Rapid Attenuated Virus EvolutioN), to model the potential evolutionary pathways of individual SARS-CoV-2 genes by identifying sequence variants that lead to increased infectivity or allow the virus to evade immune system response. By identifying mutations in SARS-CoV-2 that meaningfully alter function, Signature Science will produce a catalog of viral variants of concern to compare to emerging epidemiological data.

“While the current effort will focus on SARS-CoV-2 as a proof-of-concept, RAVEN is extensible to essentially any virus, including future zoonotic threats,” said Curt Hewitt, the project’s Principal Investigator. “Our aim is to deliver to IARPA the foundation for an empirical tool to enable rapid and widespread risk estimation of potential pandemic threats.” Work under the contract will be performed at Signature Science’s Center for Advanced Genomics facilities in Austin, TX.

About Signature Science, LLC:

A subsidiary of the Southwest Research Institute, Signature Science, LLC is a scientific and technical consulting firm providing multi-disciplinary applied research, technology design and development, and scientific, technical and operational services to government and industry.

This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2020-20082700001. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

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