NIH VideoCast - Covid-19 Lecture: Harnessing Synthetic Biology and Deep Learning to Address the COVID-19 Pandemic

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Covid-19 Lecture: Harnessing Synthetic Biology and Deep Learning to Address the COVID-19 PandemicHHS Only

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Air date:	Thursday, December 17, 2020, 12:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 595 (403 Live, 192 On-demand)
Category:	HHS Only
Runtime:	01:00:46
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	Covid-19 Lecture Series Dr. Collins is developing innovative ways to reprogram organisms, particularly bacteria, to perform desired tasks, such as detecting and treating infections. These programmed bacteria could lead to cheaper drugs, rapid diagnostic tests, and more effective treatments for antibiotic-resistant infections and a range of complex diseases. This work is part of the new field of synthetic biology, which Jim founded by combining science and engineering to construct biological circuits that can program organisms, much like we program computers now.

NLM Title:	Harnessing synthetic biology and deep learning to address the COVID-19 pandemic / Jim Collins, Ph.D., MIT.
Author:	Collins, James. National Institutes of Health (U.S.). COVID-19 Scientific Interest Group,
Publisher:
Abstract:	(CIT): Dr. Collins is developing innovative ways to reprogram organisms, particularly bacteria, to perform desired tasks, such as detecting and treating infections. These programmed bacteria could lead to cheaper drugs, rapid diagnostic tests, and more effective treatments for antibiotic-resistant infections and a range of complex diseases. This work is part of the new field of synthetic biology, which Jim founded by combining science and engineering to construct biological circuits that can program organisms, much like we program computers now.
Subjects:	COVID-19 Deep Learning Models, Biological Pandemics Synthetic Biology
Publication Types:	Lecture Webcast
NLM Classification:	WC 506
NLM ID:	101775917
CIT Live ID:	38876
Permanent link:	https://videocast.nih.gov/watch=38876

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