Automation and shared knowledge pave the way for the future

Automation is a vital component to fuel the labs of tomorrow, and to ensure that drug development continues at the rapid pace seen in response to the COVID-19 pandemic.

By no means is automation a novel concept for most research labs, but its swift advancement and expansion into new fields — such as synthetic biology — have shown us that we are only witnessing the start of what is possible. Together with open access data, which allows scientists around the globe to benefit from each other’s findings, it paints a bright picture of a future that is full of exciting new possibilities.

Laboratories all over the world have been shaken by the COVID-19 pandemic. This global event forced them to step up to the challenge, finding ways to handle unprecedented sample volumes quickly and efficiently for both research and diagnostics. This put automation in the spotlight, not only as a convenient tool, but a necessity, as obtaining accurate results with such speed would not have been possible if every sample was handled manually.

Alongside the need for rapid diagnostic testing, it was crucial to develop a vaccine as fast as possible, to curb rampaging infection rates and help the world recover both medically and economically. Laboratories came together, sharing their discoveries through open access (OA) data portals to ensure that breakthroughs would not only benefit one organisation or country, but the entire world. This shines light on another important point — how much more we can accomplish by sharing our knowledge, instead of guarding it.

Synthetic biology has also played a major role in winning ground against the pandemic, allowing the creation of a candidate vaccine a mere 66 days after the viral genome was released.¹ This vaccine was created using synthetic genes — an approach that is not only useful for developing vaccines, but might also be helpful in combating cancer, making it a powerful tool for drug discovery.

Synthetic is the new natural

Synthetic biology is based on metabolic engineering, but takes this concept a step further to encompass non-metabolic applications, with the aim of creating new biological building blocks and systems, or improving on those found in nature. In contrast to metabolic engineering, this discipline uses a systematic approach based on generalisable methods, making synthesis and sequencing of DNA more accessible and less costly.

One of the principles of synthetic biology is the ‘design-build-test-learn’ (DBTL) cycle, which helps to achieve a design that fulfils certain requirements through multiple iterations, learning by doing.² The first step is designing a biological system that is expected to be able to perform the task. This is followed by building that design using DNA parts, and integrating them into a microbial chassis. Once this is done, the system can be tested — using a variety of assays — to see if it is indeed suitable for the desired application. During this phase, a lot of data is collected through production- and omics-profiling. This is then used during the learn phase to influence the next design, as it is unlikely that the optimal system, demonstrating the right properties, is obtained the first time. Multiple iterations are usually required, and so the learning phase relies on the ability to predict the biological systems behaviour in response to a design change. Machine learning can be of great help here, statistically linking an input to an output, to predict the result for completely new scenarios.

Making the complex easy

Synthetic biology opens up many new possibilities, and its structured nature makes it easier to move forward towards new discoveries. However, although the principles are straightforward, the synthetic biology workflows are generally complex, and rely heavily on automation to achieve rapid and reproducible results. Without it, this new and exciting discipline would not be able to progress at a sufficient rate.

Higher and higher levels of automation can be seen in many labs all over the world, from handheld electronic pipettes that can aspirate and dispense several channels simultaneously, to fully automatic liquid handling workstations powered by intelligent software that can follow the most complex protocols. Many laboratories that perform high throughput screening or clinical and analytical testing — as well as large-scale biorepositories — simply would not exist without this technology.

In addition, automation all but removes the human variability factor, increasing reproducibility and ensuring productivity through staff absences, labour issues and a variety of other challenges.

The 3D puzzle

3D cellular models are becoming increasingly popular in drug discovery, providing more physiologically relevant results than 2D cell cultures or animal models. These microenvironments can more accurately mimic the complex immune response of human tissues, which is of great importance, helping to avoid costly late-stage failures of drugs in clinical trials. Grown using a variety of approaches, 3D cell culture workflows are another example of research benefiting from automation.³ Automated solutions are required both for consistent growth of 3D cell cultures, and to support cell imaging and real-time cytometry assays for drug discovery, since manually examining cells under a microscope is both labour-intensive and time-consuming. Automated culture maintenance and imaging improves reproducibility and throughput, as well as removing the risk of missing a key cellular event when leaving the lab — an important consideration for any cell-based study.

Collective knowledge

Many biological studies produce a tremendous amount of data, with thousands of genetic sequences produced daily. If not reused, this data will go to waste, together with all the possible insights that it could have provided.⁴ Considering that the entire human genomic sequence only requires 1 GB of storage space, this is truly a shame. Fortunately, it is becoming more and more common for researchers to upload their data, providing open access to anyone who is interested. If shared in an effective and comprehensive way, this data can greatly increase the impact of the original experiments, making the most of something that took significant funding and research time to produce. By sharing sequencing data globally, initiatives such as the ‘Darwin Tree of Life’ and the ‘100,000 Genomes’ projects are made possible.⁵ The former is a tribute to biodiversity, aiming to sequence the genomes of 70,000 species of eukaryotic organisms found in the UK, while the latter project uses data from patients affected by a rare disease or cancer, with the goal of advancing diagnosis and personalised treatment. Furthermore, giving open access to data also provides other benefits, such as increased credibility; if the research data is made available and possible to reproduce, it becomes more believable.

However, for others to make use of data, it needs to be organised and documented properly. This type of careful cataloguing of results is equally beneficial to groups that do not plan to upload their data, since it promotes traceability and repeatability. There are many software platforms that work well with automated workflows to offer scientists a convenient way to plan experiments and manage results, as well as receive feedback on the outcomes. For example, the Synthace Life Sciences R&D Cloud allows scientists to automate experimentation and share insights. Berlin-based Labforward is another company offering increased lab connectivity, enabling scientists to effectively connect their devices to make research data more manageable and easily accessible. On the same note, a company in San Francisco called Benchling had developed a platform that helps standardise and centralise R&D data, accelerating and improving research while working seamlessly with third-party hardware. Software company Titian offers similar services, driving digitalisation of research and advancing management and traceability in every step of the sample life cycle. Many of these advances are being made possible through the work of the SiLA Consortium, a non-profit industry body working to develop free and open system communication and data standards, providing researchers with an opportunity to connect, interface with their instruments and merge data across the laboratories. These are only a few examples and, as more and more scientists grasp the benefits of laboratory digitalisation, an even greater choice of solutions will become available.

Summary

Automation is a great way to catapult laboratories into the future, speeding up sample preparation and establishing high-throughput versions of complex workflows while minimising the risk of cross-contamination, eliminating human errors, and saving time and resources. Automated solutions are particularly important to fields such as synthetic biology, allowing the development of a more structured approach. This has enabled synthetic biology to become a powerful tool in drug discovery, replacing the hit-and-miss strategies commonly employed in many laboratories with the design-build-test-learn principle. This relatively new field is empowered by powerful machine learning software, which can make predictions based on large datasets that are beyond the capabilities of the human mind to quickly and easily comprehend. Driving science forward in such a structured manner helps to speed up new discoveries and reduce the number of failed experiments.

Learning from our own mistakes can be of great help, but learning from the mistakes of others performing similar research in parallel is a far more powerful tool, as many laboratories around the world are currently discovering. There are several software platforms that have been developed especially for this purpose, helping scientists to document, store and share their data with others, as well as streamlining workflows through connectivity between programs and hardware. With so many tools available, digitalising and immortalising your research has never been easier, bringing about the laboratory of the future, which is not only fully digitalised, but connected to research centres around the globe, letting everyone reap the benefits of hard-won knowledge.

Tecan is a global provider of laboratory instruments and solutions for pharma, biopharma, academia and clinical diagnostics. Visit the website to find out more.

References

1. Synthetic biology speeds vaccine development, 28 September 2020 https://www.nature.com/articles/d42859-020-00025-4
2. A machine learning Automated Recommendation Tool for synthetic biology, Nature Communications, 25 September 2020 https://www.nature.com/articles/s41467-020-18008-4
3. Don’t miss a beat with live cell imaging, Tecan Journal, 2021 https://www.tecan.com/tecan-journal/dont-miss-a-beat-with-live-cell-imaging
4. Sharing biological data: why, when, and how, FEBS Letters, 11 April 2021 https://febs.onlinelibrary.wiley.com/doi/10.1002/1873-3468.14067
5. Open access data benefits millions of scientists around the world and is essential for a rapid response to the COVID-19 pandemic, EMBL Communication, 20 October 2020 https://www.embl.org/news/science/open-data-sharing-accelerates-covid-19-research/

Image credit: ©stock.adobe.com/au/eplisterra

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