How Machine Learning Made Hops-Free Hoppy Beer (and Other SynBio Wonders) Possible

Singularity Hub Daily

Synthetic biology is like a reality-altering version of Minecraft. Rather than digital blocks, synthetic biology rejiggers the basic building blocks of life—DNA, proteins, biochemical circuits—to rewire living organisms or even build entirely new ones. In theory, the sky’s the limit on rewriting life: lab-grown meat that tastes like the real thing with far less impact on our environment. Yeast cells that pump out life-saving drugs. Recyclable biofuel. But there’s a catch: to get there, we first need to be able to predict how changing a gene or a protein ultimately changes a cell. It’s a tough problem. A human cell carries over 20,000 genes, each of which can be turned on, shut off, or changed in expression levels. So far, synthetic biologists have taken the trial-and-error approach. Part of the reason is that life’s biological circuits are incredibly difficult to decipher. Changes to one gene or protein may trigger a “butterfly effect” type of repercussion that propagates unpredictably through the cell. Rather than getting yeast to pump out insulin, for example, the cell could produce a bastardized, non-working version, or just die off. Designing new biological circuits takes time—lots of it. But maybe there’s another way. This month, a team at the Department of Energy’s Lawrence Berkeley National Laboratory, led by Dr. Hector Garcia Martin, suggested it might not be necessary to meticulously tease apart the molecular dance inside a cell to be able to manipulate it. Instead, the team tapped into the power of machine learning and showed that even with a limited dataset, the AI was able to predict how changes to a cell’s genes can affect its biochemistry and behavior. What’s more, the algorithm could also make recommendations on how to further improve the next bioengineering cycle using simulations. The program provides predictions on how likely an additional genetic change is to lead to a syn-bio project goal—for example, making hoppy Indian Pale Ales (IPAs) but without actual hops in the mix. “The possibilities are revolutionary,” said Martin. “Right now, bioengineering is a very slow process. It took 150 person-years to create the anti-malarial drug artemisinin. If you’re able to create new cells to specification in a couple weeks or months instead of years, you could really revolutionize what you can do with bioengineering.” Limits to Power Similar to germline genome editing in humans and AI, synthetic biology has the power to change the world. Considered one of the “Top Ten Emerging Technologies” by the World Economic Forum in 2016, syn-bio includes many branches of research—wiping out all mosquitoes with gene drives, or designing microbiomes for agriculture to replace environment-damaging fertilizers. However, metabolic engineering is its current golden child. Everything alive requires metabolism. The concept in science is a bit different than the everyday vernacular. If you think of the cell as a car manufacturing facility, and every cellular component as raw material, then “metabolism” is the process of making a car out of these raw materials but at a cellular scale. Tweaking the manufacturing process, as had happened during Covid-19, can change a car manufacturer into one that makes ventilators without fundamentally altering the factory. In essence, synthetic biology does the same thing. It tweaks a cell so that its normal production is now directed to something else—a yeast that has no concept of blood sugar can now pump out insulin. Yet due to its complexity, reprogramming a cell is far harder than rewriting software code. Here’s where AI can help. “Machine learning arises as an effective tool to predict biological system behavior,” said the team. Rather than fully characterizing how molecular circuits work together, machine learning can extract trends from experimental data, and in turn provide predictions on how a synthetic biology tweak changes a cell. Better yet, it can do so even without ...