Loretta Tioiela is the co-founder & Managing Partner of Next Sequence and co-founder of OpenSynBio, the 1st Open Computing Foundation for Synthetic Biology. In the first episode of the NEXT – SHOW series 5, she explores how advances in data science and computing can open a new world of bioengineering.

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Loretta Tioiela is working to push synthetic biology forward as fast as we can. But what actually is synthetic biology?

“The principle behind synthetic biology is pretty simple,” she explains. “It’s about the idea of applying engineering logic to living systems and life science in general. So, if you think about the way that we’ve been doing research, we’ve been doing it very iteratively.”

At the moment, we investigate diseases by running experiment after experiment, but that process isn’t ideal.

“We are groping in the dark because we are waiting for the feedback from the experiments, and thus the lessons learned, to then know what to do next. Where should we investigate next?”

The limits of bioscience experimentation

This is slow, and sometimes this iterative process, unfortunately, leads nowhere. Using existing data to model possibilities for exploration can significantly speed that process, and open up the field of synthetic biology. It takes the technology we have at our disposal and uses it to start to “reprogram” life. And we can use it as building blocks for other things.

“If you really think about it, this is what we have done with the Covid-19 vaccine because we already knew about the mechanism associated with mRNA,” she says. “But we took that knowledge and used it to change the initial purpose that was given by life to turn it into a machine to produce the antibody that we need to fight Covid.

“So, synthetic biology is all about that. It’s about modifying something that already exists as an organism in life right now to change its original purpose, to achieve something different. And so, that means that it can be applied to pretty much everything that human beings use or consume, from drugs onwards.”

We could also use it to change the way we produce food — or the material for our clothing. Imagine wearing clothes bioengineered from mushrooms…

Computational genetics is the key

This has been facilitated by the rapid evolution of sequencing and computation technology. When the Human Genome project began, it was predicted to take decades to complete. And yet, now we were able to sequence the novel coronavirus in a matter of weeks after its discovery.

“So, by design, life science is actually a very data-intense discipline,” she says. “For each experiment, there’s going to be a swarm of data. When we extract the information out of the experiment, it’s a very analogue process in the sense that it’s always human-interface based. You have a researcher or scientist that’s in charge of actually recording it into a notebook. It’s subjective and faulty because it relies on a human being.”

The challenge right now is that we’re not capturing experimental data in a way that can be transmitted automatically to a computing system that can then start working its magic, as she puts it.

Digitalisation of research data

“You want to take away everything that is done by humans that can be done in a better way by machine. That’s why in labs right now, you have this huge trend in terms of automatisation. Labs are being highly automated because when you do that then suddenly all the acquisition of the information is digitalised straight away.”

And that facilitates more rapid adjustment of manufacturing. The Covid vaccine was a watershed moment in terms of speed of design and manufacture.

“Developing a drug or any kind of therapeutics is very time intensive,” says Tioiela. “Generally, it takes seven, eight, even 10 years before something goes into production, and so it costs billions of dollars to create things that represent 20% of revenue for each pharmaceutical company.”

Can we make that process more effective?

Digitalising bioscience research

She’s actually a computer scientist by training, and after over a decade in pure computing, moved into biotech and the application of AI. In particular, she was interested in AI’s application in neuroscience, and the potential to rewire the brain in useful ways. She worked with a startup based in Paris that was working on finding biomarkers in the eyes which would predict the onset of Alzheimer’s a decade in advance. But she realised that data wasn’t being generated in a useful way from a computer science perspective.

“And I started to realise that that was not actually a one startup issue,” she recalls. “It was a systemic issue with data. It’s coming from the clinical trials, it’s coming from the patients, and it’s coming from the pharmaceutical companies; it’s coming from everywhere in the ecosystem.”

None of this was being coordinated usefully. And she wanted to fix it, so the data was captured in a way that was useful for everyone. A single platform wasn’t going to be enough — the needs of different researchers are diverse enough to make that a problem. So, a whole new ecosystem was needed.

A new foundation for synthetic biology

“If we have a computing foundation dedicated to life sciences, and specifically to synth technology, then we can actually foster an ecosystem that is going to blossom. It will be at the root of creating everything needed to move us to the next stage that I can see happening.”

And it now exists: it’s called OpenSynBio. They now have over 300 startups aligning with the idea of becoming more data-centric, and more computer-orientated.

“But it’s not just for the sake of it, but for the purpose of doing better — and faster — research,” she says.

And the potential is amazing. She mentions one startup that is working on using plants to produce electric vehicle batteries.

“You might say that the idea is insane. But plants have a very basic mechanism for extracting minerals from the soils and making things from them. So, why don’t we make them suck up the specific type of mineral that we need?”

This offers dual benefits. We get the minerals we need for battery production. And we also extract the pollution that we’ve inadvertently put in the soil.

Managing the risk

Are there risks? Of course, people are nervous about the idea of engineering or programming life.

“The dangers are highly hypothetical,” she says. “Part of the Synthetic Biology Committee’s work is about explaining what we’re doing, and the limits of it.”

Existing technology is already raising these issues, including gene editing via CRISPR, and its use on twins in China. And there are deep philosophical questions, too.

“There’s a question of social access to the technology we develop,” she says. “Do we want to create a world where only the rich can afford to live forever? What will happen if they can pay for access to anti-aging technology? You need to acknowledge that there is danger. But then you need to muster up the courage that it takes to really face up to it, address it and talk openly about it.”

For Tioiela, the risks are more than worth the potential rewards.

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This is a summary of an interview with Loretta Tioiela conducted by David Mattin. It was broadcast on the NEXT Show on 9th February 2023. You can catch up with Loretta and her work on Twitter, LinkedIn, and the web.