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AI-driven laboratory workflows enable operation in the age of social distancing

The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels—from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protoco...

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Autores principales: Marescotti, Diego, Narayanamoorthy, Chandrasekaran, Bonjour, Filipe, Kuwae, Ken, Graber, Luc, Calvino-Martin, Florian, Ghosh, Samik, Hoeng, Julia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Inc. on behalf of Society for Laboratory Automation and Screening. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679500/
https://www.ncbi.nlm.nih.gov/pubmed/35058197
http://dx.doi.org/10.1016/j.slast.2021.12.001
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author Marescotti, Diego
Narayanamoorthy, Chandrasekaran
Bonjour, Filipe
Kuwae, Ken
Graber, Luc
Calvino-Martin, Florian
Ghosh, Samik
Hoeng, Julia
author_facet Marescotti, Diego
Narayanamoorthy, Chandrasekaran
Bonjour, Filipe
Kuwae, Ken
Graber, Luc
Calvino-Martin, Florian
Ghosh, Samik
Hoeng, Julia
author_sort Marescotti, Diego
collection PubMed
description The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels—from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protocols, suspension of operations because of social distancing and stay-at-home guidelines during the pandemic. This global crisis has opened opportunities to leverage internet of things, connectivity, and artificial intelligence (AI) to build a connected laboratory automation platform. However, laboratory operations involve complex, multicomponent systems. It is unrealistic to completely automate the entire diversity of laboratories and processes. Recently, AI technology, particularly, game simulation has made significant strides in modeling and learning complex, multicomponent systems. Here, we present a cloud-based laboratory management and automation platform which combines multilayer information on a simulation-driven inference engine to plan and optimize laboratory operations under various constraints of COVID-19 and risk scenarios. The platform was used to assess the execution of two cell-based assays with distinct parameters in a real-life high-content screening laboratory scenario. The results show that the platform can provide a systematic framework for assessing laboratory operation scenarios under different conditions, quantifying tradeoffs, and determining the performance impact of specific resources or constraints, thereby enabling decision-making in a cost-effective manner. We envisage the laboratory management and automation platform to be further expanded by connecting it with sensors, robotic equipment, and other components of scientific operations to provide an integrated, end-to-end platform for scientific laboratory automation.
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spelling pubmed-86795002021-12-17 AI-driven laboratory workflows enable operation in the age of social distancing Marescotti, Diego Narayanamoorthy, Chandrasekaran Bonjour, Filipe Kuwae, Ken Graber, Luc Calvino-Martin, Florian Ghosh, Samik Hoeng, Julia SLAS Technol Short Communication The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels—from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protocols, suspension of operations because of social distancing and stay-at-home guidelines during the pandemic. This global crisis has opened opportunities to leverage internet of things, connectivity, and artificial intelligence (AI) to build a connected laboratory automation platform. However, laboratory operations involve complex, multicomponent systems. It is unrealistic to completely automate the entire diversity of laboratories and processes. Recently, AI technology, particularly, game simulation has made significant strides in modeling and learning complex, multicomponent systems. Here, we present a cloud-based laboratory management and automation platform which combines multilayer information on a simulation-driven inference engine to plan and optimize laboratory operations under various constraints of COVID-19 and risk scenarios. The platform was used to assess the execution of two cell-based assays with distinct parameters in a real-life high-content screening laboratory scenario. The results show that the platform can provide a systematic framework for assessing laboratory operation scenarios under different conditions, quantifying tradeoffs, and determining the performance impact of specific resources or constraints, thereby enabling decision-making in a cost-effective manner. We envisage the laboratory management and automation platform to be further expanded by connecting it with sensors, robotic equipment, and other components of scientific operations to provide an integrated, end-to-end platform for scientific laboratory automation. The Author(s). Published by Elsevier Inc. on behalf of Society for Laboratory Automation and Screening. 2022-06 2021-12-17 /pmc/articles/PMC8679500/ /pubmed/35058197 http://dx.doi.org/10.1016/j.slast.2021.12.001 Text en © 2021 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Short Communication
Marescotti, Diego
Narayanamoorthy, Chandrasekaran
Bonjour, Filipe
Kuwae, Ken
Graber, Luc
Calvino-Martin, Florian
Ghosh, Samik
Hoeng, Julia
AI-driven laboratory workflows enable operation in the age of social distancing
title AI-driven laboratory workflows enable operation in the age of social distancing
title_full AI-driven laboratory workflows enable operation in the age of social distancing
title_fullStr AI-driven laboratory workflows enable operation in the age of social distancing
title_full_unstemmed AI-driven laboratory workflows enable operation in the age of social distancing
title_short AI-driven laboratory workflows enable operation in the age of social distancing
title_sort ai-driven laboratory workflows enable operation in the age of social distancing
topic Short Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679500/
https://www.ncbi.nlm.nih.gov/pubmed/35058197
http://dx.doi.org/10.1016/j.slast.2021.12.001
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