Synbio Startups Tap Evolution, Prep Biomanufacturing Flows

Newly launched companies are developing technologies to automate adaptive laboratory evolution and eliminate bioindustrial bottlenecks

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Most bacteria replicate quickly. For example, Escherichia coli double their number in just 20 minutes. If the bacteria are infectious human pathogens, such rapid replication can be problematic. But microbes’ fast growth also means rapid evolution, that is, the quick and efficient rise of well-adapted mutants through natural selection—a mechanism that some synthetic biology researchers are using to their advantage.

A cornerstone of synthetic biology is the construction of microbial strains (bacterial and yeast) with novel traits created through genome editing. But some complex traits, such as the ability to grow in a specific condition or utilize a particular feedstock, may be too complicated for rational engineering. Rather than take a targeted genome engineering approach to introduce the desired traits, some companies are looking to evolution to do it.

Adaptive laboratory evolution (ALE) has been shown to be effective at selecting microbes with desired traits. But the process takes a long time and can be laborious. Now, evolutionary startup companies are working to automate ALE. By applying machine learning, these companies are developing ALE into a more practical way to evolve microbes.

One of synthetic biology’s Achilles’ heels is scale. Although the challenges are multifactorial, one of the reasons is that the requirements of microbial strains may be different when growing in a 100 mL Erlenmeyer flask than in a 1,000 L bioreactor. And it can be unclear, from the researcher’s side, what the bacteria need to thrive in the larger environment. Now, researchers at evolutionary startups are asking the bacteria to figure it out on their own. By encouraging the microbes to adapt to a large-scale environment, and selecting the strains that succeed, the companies are hoping to establish a paradigm that will lead to a reduction in the speed and cost of commercialization of synthetic biology products.

Sheffield, a city in the north of England, is an unlikely place to find a synthetic biology startup. According to Joe Price, the CEO of Evolutor, the city is known mostly for beer. And once upon a time, it was known for steel. As for the future, Price’s hope is that the city will add a new, synthetic biology–themed chapter to its industrial heritage. And he hopes to help make that happen while eliminating some of synthetic biology’s biomanufacturing bottlenecks.

Evolutor is doing its part. The company has developed the Accelerated Evolution Platform to automate and accelerate ALE processes. Specifically, the platform subjects microbes inside bioreactors to selective pressures, driving their evolution.

Price says that the processes instigated by Evolutor do not compete with rational genome engineering. In fact, the typical process will start with a genetically edited strain. Evolutor’s platform will then evolve the genetically engineered strain to acquire additional, desired traits. For example, a strain may evolve to grow on new feedstocks. This is important because, although some strains of bacteria or yeast can grow well in certain sugars at a small scale, it is not economically viable to use those same sugars on a large scale. Evolutor can evolve those strains to use food waste, or other material, as new feedstocks.

Perhaps Evolutor’s proximity to good beer is a coincidence, but the same coincidence can be seen with a second synthetic biology company, Brooklyn-based Melonfrost. Like Sheffield, Brooklyn has a beer heritage. And like Evolutor, Melonfrost has an ALE platform. It consists of proprietary hardware, the Evolution Reactor, and the company’s artificial intelligence (AI)-powered software, Maia. Melonfrost notes that Maia “learns how organisms evolve in response to selection pressures and guides the Evolution Reactor by applying new pressures—steering organismal evolution in real time without knowing anything about their genetics.”

Why not just continually edit the genomes of microbes? The advantage to letting evolution do the work is that the process can dive into a deeper pool of biological potential and uncover results that are not necessarily intuitive. It should also reduce the time needed to optimize traits and, in turn, produce organisms that are commercially viable.

Both Melonfrost and Evolutor are new arrivals on the synbio scene. Melonfrost recently raised $7 million. At only about a year old, Evolutor is just getting started. At both companies, the goal is to build AI evolutionary engines that can churn through evolutionary data and indicate (statistically) how to provide a certain function.

At Evolutor, the plan is to scale the company’s technologies and then build a biofoundry with a plug-and-play model. The ultimate goal, Price says, is to “digitally twin the evolutionary process.”

Price notes that his role is to help get Evolutor’s business off the ground, but he adds that the “brains” behind the company’s technology belong to Tuck Seng Wong, PhD, and Kang Lan Tee, PhD. They share the CSO role at Evolutor, and they are faculty members and share laboratory space at the University of Sheffield, where Wong is professor of biomanufacturing and Tee is associate professor of chemical and biological engineering.

Although the company currently operates on funding from the U.K. government, it plans to fundraise next year. And Price has even bigger ambitions. Besides helping Evolutor grow as a synthetic biology company, he wants to help Sheffield evolve as a synthetic biology ecosystem.

The synthetic biology field, a key player in the bioeconomy, is taking on some of today’s biggest challenges—from developing and manufacturing sustainable materials to helping solve the climate crisis. It is fitting that, in order to do that, the field is looking to evolution—a bedrock of biology—as part of the solution.

“Evolution is at the center of everything around us,” Evolutor declares on its website. “Now, evolution will be at the heart of the next great economic paradigm shift: the Bio-Industrial Revolution.”

Evolutionary startups—which are just beginning their growth cycle themselves—are banking on evolution to get their businesses off the ground. If these companies realize the dream of all startups—exponential growth—they may end up driving the next generation of industrial biomanufacturing.

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