The life science industry is at a pivotal moment where scientists are making great strides with technological advances driven by artificial intelligence (AI). Adopting AI into the life science space creates the opportunity to drastically improve decision-making in the laboratory, from early discovery to clinical research. This transition has been hampered by disparate data systems and incomplete record-keeping, but one way to tackle this challenge is to perform research through a cloud lab.
Very broadly speaking, a cloud lab uses the internet to enable researchers to conduct experiments using very distinct operational components that we will look at more closely later in this article. For the time being, it’s important to acknowledge that this approach marks a real change in the way laboratory research is being done.
Why the Change?
Traditional approaches to managing and scaling laboratory research cannot meet the increasing demand to make life-changing discoveries in research and medicine. As life-threatening rare diseases and cancers continue to impact everyone’s daily lives, the amount of time it takes to set up and carry out experiments is creating a bottleneck effect that slows down discovery.
Scientists are spending too much time at the lab bench managing the logistics of science. In fact, this amounts to roughly 80% of their time dealing with things such as ordering materials, setting up instruments, waiting for instruments to run, etc. Not enough time is spent on the science itself – forming hypotheses, designing new experiments, and analyzing results.
Creating the Architecture for Success
As science and researchers continue to develop new hypotheses and set up new experiments, they need to be able to focus more of their time on data analysis and experimental design rather than the actual experiment itself. Scientists need an accessible data platform that allows them to integrate AI and machine learning algorithms with experimental instrumentation to increase efficiency within the lab. With the tools and capabilities of a cloud lab, scientists are able to create complex experimental platforms that leverage AI to interpret data and make low-level decisions which are then programmatically carried out in the cloud lab.
With AI working behind the scenes to streamline operations, a cloud lab runs semi-autonomously to enable experiments to run remotely but otherwise exactly how they would carry them out in a normal lab. Scientists can ship samples to the lab, design and implement complex protocols through one single digital interface and execute experimental workflows without having to ever set foot in the facility. Cloud labs, coupled with AI, improve the productivity and reproducibility of experiments.
AI helps bring value to cloud lab users. For a cloud lab to function, all of the scientific instruments in the lab must communicate using a common language. Furthermore, users’ protocols must be exhaustive and standardized. AI helps streamline these workflows by helping scientists compile their protocols in a way the software interface (and therefore the relevant scientific instruments) can understand. By default, this standardization makes experiments reproducible.
Cloud labs run 24 hours a day, seven days a week, which means experiments are running constantly, improving researchers’ productivity, and reducing downtime. Once scientists set up their experiments, with one click of a button, they are able to run their experiments in parallel. Users can run from five to eight more experiments every day, compared to traditional methods.
Criteria of a Cloud Lab?
The term “cloud lab” is a relatively new term. Cloud labs can be defined by the following five criteria;
1.Remote, on-demand experimentation: Users must be able to design and execute their experiments on-demand from anywhere, at any time, from a single computer interface.
2.On-demand control of every experiment: Users must be able to replicate all aspects of their scientific experiments as if they were sitting at the bench, without requiring extra lead time, software, or expertise.
3.Comprehensive Instrumentation: Users must have access to all of the scientific instruments they need to perform their experiments on-demand.
4.Comprehensive Sample Preparation: Users must be able to store and handle all of their samples remotely, regardless of sample or container type.
5.Single software interface for the entire lab: Users must be able to plan and execute multiple experiments, as well as process, analyze, visualize, and interpret their data, using a single computer interface.
Conclusion
As the demand for greater productivity in the laboratory increases, AI can be harnessed in the early stages of research and development to make laboratories more efficient, helping scientists answer new questions and make discoveries faster than they ever could before.