Enabling life science

Markus Gershater explains why we need to help biologists turn their work into code

If we can’t make biological work programmable, we’re doomed to fail. This is partly because of the urgency of the situation we find ourselves in. The progress we could make 50 years ago with pen, paper and hard work is no longer possible with those means alone. The enormous complexity of the challenge, our desperate need to move faster towards scientific insight and the ever-present fight for reproducibility are pressures that will only ever increase. As the old adage goes: if we don’t change something, nothing will change.But how do we do this? To put it simply: we must be able to represent biological work with code. This is easier said than done. Do we ask biologists to become computer scientists? Maybe, but not without enormous cost.

Asking them to take on the burden of programming puts pressure on them to be something they perhaps never wanted to be. Even worse, it reduces the time they can spend thinking about science itself. So although it might be one route forwards, we’d only get there by exhausting and distracting our brightest scientific minds.

What do we stand to gain if we can make this possible without leaning on biologists? If we succeed, we open the door to the dazzling possibilities we’ve long chased after. But we may go further than that.

In the most practical terms, it means that we can better integrate with the automation hardware we use in R&D. Hardware that alleviates the burden of mechanical work for the scientist isn’t always easy to control but a seamless, standardised, cloud-based way to do this would be revolutionary. We would see increased ‘walk away’ time, greater machine utilisation and we would lower the barriers to entry for automation. Imagine a world where automated, high throughput Design of Experiments work becomes a reality in even the most basic of labs.

What else? Methods become easier to design, develop, track and share. Let’s say your colleague in another hemisphere strikes gold with a new method. So they share it with you, and it runs just the same in your lab as in theirs. We’d also be able to make more direct connections between the experiment as it is designed and the data and metadata that endows its full contextual value. This closes an important loop: as we design experiments expressed in code, so do we design the structure and output of our data, increasing the speed to insight in the work we do. This would make it easier to automate the process of automation itself; automation engineers are only going to become more important.

On a more fundamental level, we will begin to shift how we think about the process of experimentation. In the future, a single biologist will be able to design enormously complex multifactorial experiments from the comfort of their own home, execute them in a cloud-based laboratory, receive structured data in the way they imagined it from the very beginning, and then use that data to iterate at lightning speed. This will unleash a creative potential that we simply cannot imagine today.

Others have made attempts to standardise biology into code before, but this still demands a biologist spend their time coding. Although we’ve seen the advance of electronic lab notebooks (ELNs) that have helped digitise record-keeping, they only go so far. They represent the end result of the scientific process rather than the full cycle and context-rich reality of experiments in the real world. Instead, we must use the power of code without needing biologists to write it themselves. We will make this a reality by placing an abstraction layer on top of that code so biologists can work in the space between their knowledge and their equipment.

Synthace has built that abstraction layer and the means of producing that code. Based entirely in the cloud, the company’s platform gives scientists a new and more powerful way to think through and design the science they want to do. With a simple interface, they can create complex multifactorial experiments and then execute them on their lab equipment. Translating instructions into machine code, the platform bridges the gap between design and the data we need to move forward.

It’s no silver bullet. Nothing is. But it moves us past spreadsheets and notebooks into a world of greater possibilities. A change like this is what the life sciences desperately needs, and that’s what’s most important here: enabling scientists and helping them towards their next breakthrough.

Markus Gershater is Chief Scientific Officer, Synthace

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