Towards the Lab of the Future

In thelab of the future, researchers will be freed from manual, repetitive experimental tasks, as automated tools and artificial intelligence-powered robots carry out protocols, collect and analyze data and design subsequent experiments, freeing up time for humans to focus on interpreting what the results mean and addressing the bigger scientific questions.

未来的实验室willbring together a range of different technologies, all digitally connected and seamlessly integrated. These innovations will be involved in every step of the research cycle, from managing a lab’s supply chain of scientific products and reagents – handling samples, chemicals and equipment – to sharing data within and across organizations.

But the timescale for realizing this vision is slower in some research sectors than others. In this article, we look at the barriers preventing more widespread adoption of automation and digitization, and the opportunities they could bring.

Automating the academic lab

Anyone who has worked in a lab will be familiar with the repetitive, manual nature of many experiments, and there seems ripe opportunity for using automation to free up researchers’ time. But for academic labs, adopting automation can be daunting and cost-prohibitive, and isn’t helped by structures for funding and impact assessment.

“I think the vision of the lab of the future differs in academia and industry because we have different outputs,” saysDr.Ian Holland, an engineer who moved from the automation industry to a lab focused on tissue biofabrication at the University of Edinburgh and has written about the ‘automation gap’ in academia.¹“Academic labs tend to carry out a wider range of work and there’s considerable protocol variability, whereas industry uses standardized protocols for highly focused, repetitive applications, which are more amenable to automation. Academic labs cannot afford to invest in off-the-shelf technologies that aren’t more flexible to suit their needs. So, although there is appetite for the improved efficiency that automation brings, the route to the lab of the future for academic labs is less clear.”

There is a shared vision though, which is a world where scientists spend more time doing science and automation carries out the manual tasks. “It’s not good having highly educated people carrying out manual tasks, and I think that happens too much in academia. I’d like to see more manual tests done by machines and let scientists do more science,” says Holland.

Digital Laboratory Automation Strategy and Process Mapping

This eBook outlines how to plan and implement a digitalized regulated laboratory, focusing on where you are now and where you want to be. Download this eBook to discover how to understand and map a laboratory’s current processes, the three principles of digitalization andtips for planning a digitalization strategy

View eBook

Sponsored Content

Barriers to adopting automation

The short-term nature of academic research funding does not lend itself to investing in large-scale technologies for modernizing the lab, says Holland, and although investment in major infrastructure such as robotics will improve efficiency, it is difficult to directly relate that to an increase in the output of research papers – the main metric used to measure a lab’s success – making the investment hard to justify.

This is a problem also experienced byProf. Ross King, at Cambridge University, who has been working for several decades on ‘robot scientists’ – semi- or fully autonomous robots that automate simple forms of scientific research, from setting new hypotheses to automatically designing and running efficient experiments to discriminate between them. This futuristic type of research seems to divide funding panels, who have tended to take a conservative view, and existing university structures don’t lend themselves to the collaborative, interdisciplinary nature of the work required. “I think it’s slowly changing, and we’re getting traction in different areas, especially now these ideas are being taken up by the pharmaceutical industry,” says King.

Another challenge for academic scientists is a skills gap, because automation and robotics requires an understanding of mathematical models, machine learning and engineering – expertise not every lab has easy access to. And although automation brings efficiency, it also brings with it new challenges, such as how to manage large amounts of data.

This is where having the right expertise can help, asProf. Ola Spjuth来自瑞典乌普萨拉大学,解释道:“我们have a big focus on trying to automate our entire cell-based screening and profiling methods in the lab, and this generates a lot of images. This scale of data can scare a lot of researchers, but we have a background in managing big data and using high-performance computing clusters here, so we see large amounts of data as valuable. We’re also not the typical life scientists in that we take an engineering approach and have a multidisciplinary group with experimentalists, data scientists and engineers.”

Enhancing the Scientific Experience Using Laboratory Software

Scientists need to be able to focus on their critical work and not be burdened by challenges such as siloed data, manual processes and old technology.Watch this webinar to discover novel ways to interact with a laboratory information management system, how to leverage better insights from your data, the power of automating processes and creating more intuitive workflows.

Watch Webinar

Sponsored Content

Using automation and AI to improve efficiency and reproducibility

Spjuth took what he calls an unconventional strategy to automating the lab in that they did not go out and procure entire robotic installations from vendors, instead choosing to buy individual equipment components and build the system themselves using an open-source approach. “It’s a lot more challenging than buying something off the shelf, but we have full control of all steps in the protocol, and we wanted a research environment that we can grow with and update.”

So far, the main efficiency gains have not been the envisioned capacity increase from using robots able to work 24/7. “We are getting there,” says Spjuth, “but our system still needs a lot of human support, and steps such as cell culture are too expensive for an academic lab to fully automate right now.” The major gain, he says, is in reproducibility – every experiment is carried out in exactly the same way.

In fact, alongside efficiency, reproducibility appears to be one of the main drivers for automating research processes. One of the goals of King’s work on robot scientists is to improve the scientific method. “Machines in some ways already do better quality science than humans because what they do is recorded, explicit and clear,” says Ross. “Human beings are often unintentionally sloppy about what they do in experiments, and there’s a huge problem with scientific reproducibility because experiments are so susceptible to human error. Just like games on computers have improved over the years, we think that in science, the machines will keep progressing. Ultimately, they’ll be as good as humans at science, and maybe even better.”

King has already developed two prototype robot scientists,Adam and Eve。Adam was designed to carry out functional genomics in yeast, assigning functions to the genome that was sequenced back in 1996. Eve specializes in early-stage drug design, using artificial intelligence to find compounds to treat specific diseases.

“The way compound screening used to be done in industry was you would make an automated assay to tell you if a compound was likely to be good or not, and then you’d screen a large compound library – maybe one million compounds – and find a small number of hits to take forwards. Then you’d start again with another assay and library,” explains King. “But actually, that’s a missed opportunity, because you’ve learned something during the screen and you could use that insight to decide what to do next.” By using quantitative structure-activity relationship (QSAR) models and accumulating biological knowledge, Eve was trained to find hits using only a small fraction of the compounds in a library – speeding up the process and make it more cost effective.

Now, King is working on the next iteration of the robot scientist – called Genesis – as part of theNobel–Turing AI scientist grand challenge。The challenge is to develop AI systems capable of making Nobel-quality scientific discoveries autonomously at a level comparable, and possibly superior, to the best human scientists by 2050.

Genesis is a scaled-up robot scientist with thousands of micro-chemostats – tiny bioreactors where nutrients are continually added to cells and metabolic end products are continually removed. These will enable Genesis to run more sophisticated experiments in parallel. “We need an AI system to plan so many experiments and especially hypothesis-led experiments, rather than just altering a component and seeing what happens,” says King. “Here, the robot is saying ‘I think change Y will do X to this model, and then it conducts the experiment to see if the hypothesis is true.”

How AI-Powered Analytics Transforms LIMS Data into Valuable, Action-Ready Insights

Using data science algorithms, AI and ML rapidly process structured and unstructured LIMS data, integrating it with external enterprise data and serving lab managers and analysts with valuable insights and timely, meaningful recommendations.Download this whitepaper to learn how advanced analytics can increase your scope of research and/or testing, improve speed to market and enhance lab profitability.

View Whitepaper

Sponsored Content

Moving towards a digitized laboratory

除了采用机器人解决即兴表演ve efficiency and reproducibility in the lab of the future, many researchers are moving towards digitizing their labs, switching from paper-based systems to informatics solutions such as laboratory information management systems (LIMS) and electronic notebooks (ELNs). LIMS enable researchers to keep track of data associated with samples, experiments and instruments efficiently, as well as actively manage lab processes, while ELNs digitize note taking and can automate the data review process. Guidance on good records and data management practices from the World Health Organization (WHO)recommends that hybrid systems– a combination of manual and electronic systems – should be replaced by fully digitized systems at the earliest opportunity.

Adopting informatics solutions such as LIMS can offer laboratories several benefits, including helping to improve performance, maximizing quality and ensuring compliance requirements and regulations are met. They can also remove repetitive, laborious steps in workflows and reduce human error.The time savings can empower scientists, allowing them to focus on more complex and meaningful work.

Despite the benefits offered, barriers to adopting these solutions and digitizing a laboratory remain. The cost of subscriptions, new equipment and software, as well as time to implement the solutions, can be prohibitive for many laboratories, particularly in academia. “Accessibility is also a huge barrier. Many academic laboratories aren’t set up for the digital capture of laboratory information, both from a hardware and software perspective,”Dr. Samantha Kanza, senior enterprise fellow at the University of Southampton, told188金宝搏备用previously. Problems with outdated equipment and software compatibility can further limit the adoption of digital technologies. In addition, “The lab can often be a hostile place for technology,” said Kanza. Space for using laptops or tablets may be limited, and researchers may be concerned about spills and accidents occurring. Even things such as removing gloves to type notes rather than jotting them in a notebook can be seen as prohibitive.

However, the continuing advancement of technologies is likely to reduce these barriers and encourage greater adoption of digital solutions in the lab of the future.

“Much like smart homes have become commonplace in today’s society, so will smart labs. Users will be able to control their laboratories by voice using smart lab assistants, all of the laboratory systems will be seamlessly linked together and users will have multiple options to record their data via voice, tablets, phones or computers if they wish,” envisaged Kanza.

Understanding the Foundational Needs To Support Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly entering the day-to-day operations of many organizations, and data efforts are in full force. Watch this webinar to learn about what AI-ML is and how it can help your organization, how you can create an effective data science infrastructure for AI/ML and cloud vs on-premises considerations.

Watch Webinar

Sponsored Content

Taking small steps towards the lab of the future

It might be another two decades before fully autonomous robots are designing and conducting experiments in the lab, but it’s never too early for academic labs to start their journey towards automation, says Holland. “As an engineer in a biology lab you can see the potential opportunities to use technology to improve processes. However, I think too often in academia, researchers strive for a magic machine that does everything. But that is never how you develop automation as an engineer, you build prototypes that carry out each part of the process.”

Holland advocates starting small, by automating something simple such as fluid dispensing that can bring substantial gains in efficiency and reproducibility. In the tissue biofabrication lab, just making this change has reduced a protocol from 25 to 5 minutes, freeing up time for other tasks.

Another advantage of adopting automation early is it can help researchers looking to translate discoveries from bench to bedside. “The earlier you can include automation in your process and start thinking about that, the better chance you have of convincing people to invest in your product, because they can see it will be easy to scale up quickly.”

在Spjuth的实验室y are hoping for additional collaboration with other researchers working on their own robotic and automated solutions for the lab, sharing protocols and code. “With major advances in technology such as 3D printing and people now sharing code for these and other applications, it is becoming possible for researchers to do much more independently. The do-it-yourself movement is advancing and that means you can build your own microfluidic chips and microscopes, and as prices for robots come down there is an opportunity for many biological labs to adopt some sort of lab automation.”

However, an important consideration as this movement advances, notes Holland, is sustainability. “There is already a real problem with automated processes generating high amounts of waste – a machine generating millions of waste pipette tips, for example. I think this needs to be considered more and certainly in the design stage, both from an environmental perspective and to ensure supply chains can meet demand.”

Reference:

1.Holland I, Davies JA. Automation in the Life Science Research Laboratory.Front Bioeng Biotechnol。2020; 8:571777。doi:10.3389/fbioe.2020.571777