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

Abstract

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.

Keywords

• Laboratory planning
• Machine learning