Machines are great in freeing people from jobs they don’t want to do. These include physically demanding, complex, hazardous or painstakingly boring tasks. New, shiny machines are popping up in all industrial sectors, and research labs are no exception. In this era of self-checkouts, self-driving cars and auto-responding emails, we are heading into an auto-research revolution. Automated machines can now help scientists to handle complex data and perform mundane laboratory tasks. But how will this automation affect today’s researchers? How about the nature of research itself?
Bionic basics – what is automation?
First, let’s make sure we know the difference between machines, robots and automation. Throwing these terms around aimlessly will make most think of the Terminator or R2D2. Put simply, machines are specialised tools:
Machine = piece of equipment that uses electricity or an engine to do a specific task
A robot, on the other hand, is an autonomous machine. They function according to programmed commands to move and perform given tasks. Think of your washing machine, that runs a specific program to not shrink your clothing. And just like your mom has kindly reminded you, the laundry doesn’t wash itself, and so human intervention is key. Machines are a type of augmentation, and an enhancement of human capability to work more effectively.
By contrast, automation refers to machines working without or with very little human intervention.
Automation = processes operated by machines or computers to reduce the amount, time or cost efficiency of work
Automation is critical for scaling up industries. Automated machines helped us jump from handmade cars into Ford’s mass factory production lines, transforming the motor industry. Fast forward to 2017, when the mobile phone company Changying replaced 90% of its employees with automated assembly lines, resulting in a 250% production increase and higher product quality. Machines equal cheaper production cost and higher output. So what happens if a research takes a similar leap?
Hello productive science (and goodbye pipetting)
The first tasks replaced by machines include repetitive tasks, like preparing stocks solutions and pipetting. Lab workers often see pipetting as ‘love or dread it’ task, with multiple online guides to help scientists improve their accuracy. Placing 0.01-0.05 μl of see-through liquids into 96 well plates can even be relaxing once you get to the flow of it. However, pipetting can cause physical cramps, sore thumbs, and a vast amounts of human experimental error – especially past 6 pm when you want to go home for tea.
However, what if a machine provided by Opentrons could pipet for you? Automated pipetting could help perform 20 PCR experiments per day instead of two, with perfect precision. Meanwhile, a Hudson Robotics colony picker would pick up to 2,400 cell colonies per hour in your culture room, and a BioXpTM 3200 DNA printer could quietly generate your custom plasmids in the corner. Such automated equipment can free up thousands of working hours and generate more accurate results in the end. Less PhD students and postgraduates would report being depressed if these miracles were the obvious everyday norm.
Software to solve the reproducibility crisis
Despite their pop-culture depiction, scientists are not robots. In addition to experimental error, biased cherry picking deliciously publishable results can lead to misrepresenting biological reality. These scientific errors and selective bias are so significant that most of scientists are unable to reproduce the work of other scientists, and often not even their own.
However, laboratory automation would be the ultimate solution to this reproducibility crisis. One strategy is to introduce a universal programming language, such as Antha OS. Antha is an operating system for biology created by the UK bioengineering company Synthace to transform lab productivity. Antha allows lab hardware to crosstalk seamlessly, allowing ease in design, execution and monitoring complex experiments. Similar operating systems could then help handle the vast amounts of data collected. These include the MiniKNOW data handling software for the automated DNA sequencing platform MinION by Oxford Nanopore, which can easily obtain data, analysis and identification of experimental samples.
In addition to Antha and MiniKNOW, other powerful lab programming and visualisation systems include:
- Aquarium OS for standardising workflows for lab technicians
- AutoBio CAD for designing bacterial circuits
- TinkerCell for simulating biological processes and devices
But won’t automation disrupt the lab workforce?
However, most lab workers are not looking forward to a lab filled with data-churning conveyor belts. Yet this is the exact model of a fully automated laboratory, such as the automated synthetic biology lab Edinburgh Genome Foundry. In this research factory, digital screens and robotic arms intervene in ever experimental stage. Total automation would lay off most technicians, who would be replaced by software developers and engineers. As automation causes decline in some jobs, it will thus open completely new specialised professions. Further, due to the fast adaptation of new technologies, the market demand of scientific professionals has been predicted to increase, especially in developing countries.
How about the communal culture and craftsmanship that scientists have developed through decades of lab work? A Michelin-star chef would be upset if automated stoves and spatula waving robotic arms replaced his kitchen staff. Similarly, the hand-on work of pipetting and measuring is why some lab workers took the job in the first place.
However, automation is a modern stepping stone to better understand biological systems and disease processes, to develop human therapies and drugs for waiting patients, and to create industrial solutions to help the environment. Traditional techniques such as Western Blotting gives us a mere glimpse of the full complexity of living cells. Visualisation and AI prediction software could predict what actually happens inside a cell in real time, and laboratory pipelines could help engineer designed organisms with desirable traits faster than ever. Can you imagine what an impact these would have on society?
Creating a healthy human-machine relationship
The key to scientist-machine collaboration is trust. All users should practice with the robots themselves, to transform techno-fear into techno-excitement. This would help lab workers to become aware of the true abilities and limitations of the machines.
Overall, laboratory machines are complementary rather than substitutes to human labor. Not all lab tasks in a laboratory are replaceable; some need a high degree of reasoning, cognitive thinking, creativity and innovation. Automation could allow us to find answers to complex questions unreachable by a lifetime of conventional lab work. Instead of looking down into the tube, scientists can look up and around them. Teamwork, passion and emotional skills are tough to outsource, and they are in higher demand today than ever.
[Post based on an essay by Fernanda Bolaños]