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How Digital Tools and Automation are Transforming Life Science R&D

Digitalization is poised to reduce inefficiencies and provide greater insight for biologists and scientists

by
Lauren Everett

Lauren Everett is the managing editor for Lab Manager. She holds a bachelor's degree in journalism from SUNY New Paltz and has more than a decade of experience in news...

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Lauren Everett, managing editor, speaks with Markus Gershater, PhD, co-founder of Synthace, about how digital tools, automation, and artificial intelligence can streamline processes and improve the scientific discover process for life science organizations.

Q: What are some of the key challenges that biologists are facing today? 

A: Today, biologists and scientists have to be experts in a number of areas—beyond science itself—to run their experiments. In addition to knowing biology and how to mine data (the core part of their calling) scientists have to know how to design experiments with a budget in mind, manage logistics (bringing all the raw materials together and booking equipment at the right time), and liaise with service engineers. Scientists also have to master tedious and repetitive tasks, fastidiously following thousands of repeated steps. A large chunk of a scientists’ working life can be spent in a purgatory of continuously demanding, high-pressure, and tedious work that might not necessarily move the needle when it comes to actual scientific progress. 

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MARKUS GERSHATER, PHD
MARKUS GERSHATER, PHD

Much of this is being addressed through the digitalization of life sciences R&D. But it’s a very fragmented landscape. This means if scientists want to design experiments, as well as simulate, preview, execute and analyze them, and collaborate with other scientists, they have to use several different platforms. This isn’t efficient or cost-effective and doesn’t lead to the best possible science.

Q: How can these challenges be overcome to speed up the scientific discovery process? 

A: I believe that digital tools and automation are a first step, both to overcoming these challenges as well as speed up the process of scientific discovery. A new kind of digital platform, one that can remove these mundane and tedious tasks, to free scientists so that they can do their best work, will spark a fundamental shift in how science is done. We need to get to a point where scientists need to be experts in only two things: biology and the design of their experiments. However, while digital tools and sophisticated automation are a start, we really need to adopt new mental models and ways to really think through the problems we must solve in the way we conduct research. Specifically, point solutions aren’t efficient and the scientific community needs to find ways to create more powerful experiments. 

The tools that would enable more powerful experiments would, for example, be able to act as multiple point solutions at once—automating, gathering data, acting as a design tool, and acting as the place where experiments are designed in the first place. This would let biologists do dramatically more ambitious experiments than they would ever have contemplated before, a tantalizing prospect for scientists individually, and the scientific community as a whole. 

Q: How has the COVID-19 pandemic and other recent viral emergencies impacted the way biologists and other lab professionals work?  

A: The swift COVID-19 response is an incredible example of what's possible when we are united by a common, urgent cause and when we have a lot of incredible research and preparation at our fingertips.

So much scientific work relies on being there in person, and there's a cruel irony when this is the exact thing you can't do during a pandemic. As a result of working remotely during COVID-19, many scientists were unable to engage with colleagues or access labs or equipment, which delayed and decreased research productivity while increasing stress and burnout.

The pandemic has highlighted the need to adopt tools and change working practices that push more scientific work into digital sphere, so that we're not so reliant on being in-person. For example, some technologies have enabled many scientists to conduct their experiments remotely. In certain instances, scientists that had COVID-19 were able to run their experiments through a digital platform remotely and send directions to other scientists in the lab to perform them.

Looking at the pandemic more broadly, and the accelerated timeline we witnessed for COVID-19 vaccine development; this was made possible by a feat of global collaboration on a scale never before seen. However, using digital tools for these accelerated timelines could hopefully become the norm across the industry and enable scientists to solve the world’s problems not in years or months, but in weeks or days

Q: What needs to be done for the research community to stay ahead of viral threats going forward? 

A: The industry was able to respond rapidly to the COVID-19 crisis due to a lot of work that had already been done in the past couple decades. However, we likely won't get that luxury next time. We need to consider that if we were starting from scratch, we wouldn’t be able to do this unless we were able to access enormous computing power and revolutionary new ways of doing science. 

If the industry had a digital collaborative playbook for knowledge transfer, this would be transformational. It could lay the groundwork for a new gold standard in global scientific research and collaboration. Labs need to start deploying more sophisticated tools, some of which are only now emerging and becoming available, to accelerate the overall response. The pandemic has accelerated the need to digitize labs, and we have already started to see this coming to fruition. Various partnerships have emerged, designed to enable automated experimentation and streamlined insight sharing between scientists to accelerate innovation. 

This push towards digitalization is harnessing and addressing the problems thrown up by the pandemic and transforming them into an opportunity. Life science has long faced a reproducibility crisis since a lack of standardization around biological protocols means that they can be misinterpreted when reproduced, introducing opportunities for error that the human eye will often miss. If the research community has the right tools, they could have the ability to eliminate this element of human error by automating the experiment, making it easily repeatable and the results more reliable.

Q: What trends are you seeing in the life sciences/biology industries? 

A: The buzz around artificial intelligence (AI) and machine learning (ML) is remarkably strong and, without a doubt, it will be transformational in bringing new insight to biology. But for all the fanfare, we have yet to see the full realization of its potential. The truth is, you’ve got to walk before you can run: the work of biology and the data/metadata that it produces is difficult to represent in code and difficult to digitize. If we can’t do it, AI/ML remains a pipe dream that remains the preserve of “big tech.”

The volume of data, and the quality of data we can provide to those AI and ML tools determines the likelihood of uncovering anything interesting—so this should be another priority for the industry. If we can make those connections successfully, there will likely come a time in this decade when AI can predict the best possible experiment design before we even step into the lab. Should this come to pass, the upshot will be scientific breakthroughs that defy belief by today’s standards.

Without making a direct connection to the reality of what is going on in the lab, it takes much longer to get data and metadata into the cloud, and to connect with other software solutions. Additionally, this decade, advances in life science won’t just impact drug development, they’ll alter the landscape for any industry involving biology. Food production, agriculture, and climate tech will all be affected. These changes will occur because everything in life science will happen much quicker due in large part to the technologies that companies are implementing. Those who develop drugs will be able to do it much faster. Those seeking to solve food inequities, agricultural challenges, and climate change will be able to condense a year of work into one month.

Q: Anything else to add? 

A: We hope that through a digitalization revolution that the 2020s will be the decade that the industry as a whole will be able to solve life science’s biggest challenges, not in months or years, but in weeks or days and will give scientists more control over their work while alleviating their administrative time burden.


Markus Gershater, PhD, is a co-founder of Synthace and one of the UK’s leading visionaries for how we, as a society, can do better biology. Originally establishing Synthace as a synthetic biology company, he was struck with the conviction that so much potential progress is held back by tedious, one-dimensional, error-prone, manual work. Instead, what if we could lift the whole experimental cycle into the cloud and make software and machines carry more of the load? He’s been answering this question ever since.

Markus holds a PhD in plant biochemistry from Durham University. He was previously a research associate in synthetic biology at University College London and a Biotransformation Scientist at Novacta Biosystems.