Scientific cloud labs are highly automated, centralised laboratories that allow scientists to design and run experiments remotely from anywhere in the world, using only a computer. Not only do they provide scientists with complete control over their experimental setup, but they also enable researchers from all over the world to access top-of-the-range equipment without the high upfront purchase costs or the requirement for any lab space whatsoever 1.
There are several advantages to cloud labs that are resulting in their ever-growing popularity, however, they are not without their challenges.
In this article, we will discuss the opportunities and limitations presented by these state-of-the-art laboratories.
How do cloud labs work?
Cloud labs, in essence, allow researchers to outsource their experimental work to highly sophisticated machines that run 24 hours a day, 365 days a year. But how exactly do they work?
- Experiments are designed on an online platform. The user interface acts as a highly skilled technician, suggesting points at which experiments may run into issues
- If specific samples need to be analysed, these are usually dropped off or posted to the lab
- Experiments are conducted remotely at the cloud lab facility to the exact specifications provided by the user
- Data and protocols are automatically collected and stored on an online laboratory information management system, which can be shared with collaborators as and when required
- The platform provides software and tools for a broad range of analysis needs, which can also be automated
Examples of cloud labs
The first example of automation using a cloud-based infrastructure was the robot scientist “Adam”, which integrated all the required lab equipment for performing microbial batch experiments 2.
Then, in 2010, Emerald Therapeutics, a biotech company developing antiviral therapeutics, was developed. Following frustrations with equipment malfunctions, the founders developed centralised management software for their scientific instruments and a database to store metadata and results. This evolved into Emerald Cloud Labs (ECL), a fully automated cloud lab offering a diverse range of bioanalytical services.
Since then, several other cloud labs have been developed, including Strateos, which has cloud lab facilities focused on drug discovery and synthetic biology.
Another drug discovery-focused platform, Arctoris, has facilities in Oxfordshire, Boston, and Singapore and offers automated, AI-driven drug discovery and testing services.
Carnegie Mellon University’s (CMU) academic cloud lab represents the first-ever academic cloud lab facility 3. It provides access to a wide range of instrumentation for research and teaching purposes.
Opportunities presented by cloud labs
Digitally transforming traditional labs offers several advantages over traditional research labs, which is leading to increased interest and broader adoption in the scientific community 4. Some of the major advantages include:
- Accessibility: Cloud labs allow researchers to access equipment and conduct experiments from anywhere in the world, at any time
- Scalability: Cloud labs effectively overcome the space and equipment constraints as well as researcher availability issues that come with physical labs, rendering it easier to scale up research activities
- Cost-effectiveness: Establishing a physical lab can be expensive, considering the upfront costs of the space, equipment, and maintenance. Cloud labs can significantly reduce these costs, making research more affordable
- Collaboration: Multiple researchers can access and analyse data simultaneously, providing opportunities for global collaboration
- Reproducibility: Experiments can be standardised across different locations, leading to more reproducible results 5
The challenges and risks of cloud labs
Despite the valuable opportunities offered by cloud labs, they are not without their limitations. Some of the most significant challenges associated with cloud labs include:
- Data security: Remote data processing may present regulatory issues, particularly with the lack of universal regulations. This is particularly critical when dealing with sensitive data, such as patient data
- Reliability: Downtimes or technical issues such as poor internet connectivity could significantly disrupt the research process
- Limited possibilities: Not all experiments are currently supported with cloud labs, however, this is constantly improving
Looking to the future
Going forward, cloud lab technology is only set to evolve and become more advanced than ever.
Integration with AI and machine learning technologies will likely improve cloud labs’ abilities to automate routine tasks, analyse large datasets, and generate data insights 6.
Enhanced security measures and the development of effective regulatory frameworks will make it easier to utilise cloud labs for clinical and pharmaceutical research activities. Together, these developments are likely to foster the widespread adoption of cloud labs across more institutions around the world.
With the future potential of cloud labs in mind, Automata’s advanced LINQ Cloud automation software was developed as a future-proof tool aimed at supporting labs in harnessing the power of cloud-based systems.
When integrated with LINQ Bench, it provides flexible, adaptable automated solutions that can be integrated into existing laboratory setups.
Automata’s user-friendly cloud-based software enables researchers to build workflows and run them remotely on the LINQ Bench system, offering all the advantages of a centralised cloud lab, within your existing lab space.
References
1. Arnold C. Cloud labs: where robots do the research. Nature. 2022;606(7914):612-613. doi:10.1038/d41586-022-01618-x
2. King RD, Rowland J, Oliver SG, et al. The Automation of Science. Science. 2009;324(5923):85-89. doi:10.1126/science.1165620
3. Armer C, Letronne F, DeBenedictis E. Support academic access to automated cloud labs to improve reproducibility. PLOS Biol. 2023;21(1):e3001919. doi:10.1371/journal.pbio.3001919
4. Advantages of a Cloud Laboratory Over the Traditional Laboratory. Carnegie Mellon University Accessed June 20, 2023. https://events.mcs.cmu.edu/cloud-lab-information-sessions/wp-content/uploads/sites/24/2021/09/why_use_cloud_lab.pdf
5. Jessop-Fabre MM, Sonnenschein N. Improving Reproducibility in Synthetic Biology. Front Bioeng Biotechnol. 2019;7:18. doi:10.3389/fbioe.2019.000186. Frisby TS, Gong Z, Langmead CJ. Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories. Bioinformatics. 2021;37(Supplement_1):i451-i459. doi:10.1093/bioinformatics/btab291
