Robust Biosecurity Measures Should Be Standardized at Scientific Cloud Labs

In the last decade, various labs that allow users to remotely conduct physical and life science research have been established around the world. Known as cloud labs, these facilities often rely on standardized procedures, highly automated workflows, and sophisticated data management to support research in areas such as drug discovery and materials chemistry in a fee-for-service model. Users do not need direct access to a laboratory for their own research.

At the same time, though, these remotely operated cloud labs pose new risks of accidents and misuse. By adopting comprehensive security measures, including routine evaluations and monitoring via artificial intelligence (AI), the scientific community, ideally through a consortium, can harness the full potential of cloud labs while ensuring safe and secure research environments.

Cloud labs are changing scientific research by offering fully remote, on-demand, and highly automated laboratory environments that confer a high degree of autonomy to scientific research as an on-demand service. Users can plan and customize experimental workflows without ever having to step foot inside a physical laboratory. Cloud labs, and their user interfaces, are the link between the digital and physical domains in science.

With their increased accessibility, scalability, and operational flexibility, cloud labs enable researchers to collaborate across geographical boundaries and reduce the costs of acquiring expensive instrumentation needed for research.

Emerald Cloud Lab (ECL) is one example of this innovative approach. Established in 2010 with a $100 million investment, ECL operates two main facilities: a 105,000-square-foot laboratory in Austin, Texas, and a 20,000-square-foot site in Pittsburgh, Pennsylvania, developed in partnership with Carnegie Mellon University.

Although on-site human operators are needed, researchers can design and manage experiments, drastically reducing costs and time to achieve key data. The advantages of cloud labs, as demonstrated by ECL, include improved efficiency, flexibility, and productivity.

By ensuring reproducibility at the push of a button and capturing data automatically, cloud labs overcome typical challenges faced by traditional laboratories, such as limited access to instruments and high operational costs. This cloud lab model not only maximizes the use of laboratory resources but also facilitates collaboration among distant researchers, streamlining data management and potentially reducing costs in areas like drug discovery, ultimately advancing scientific progress and societal benefits.

Automation in science is not new, nor are scientific cloud labs. What is new is the possibility for artificial intelligence to transform how users interact with cloud labs to direct a range of experiments, from peptide synthesis, to high-throughput compound screens, to cell culture.

Integration of AI systems, as demonstrated by the Coscientist agent, can also provide semi-autonomous experimental design and execution. AI has allowed for natural language inputs to be translated into carefully planned steps that happen inside a laboratory. While this could drive research, it also has implications for biosecurity. A lower technical skill barrier to designing experiments, combined with the kind of one-stop-shop research that cloud labs may provide, could expand the pool of bad actors.

Currently, there is a lack of data regarding cloud lab operations, their workflows, or their number and types of customers. There are no public documents that detail the locations or capabilities of cloud labs around the world, akin to Global Biolabs’ tracking mechanisms that document high-containment laboratories conducting pathogen research.