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In 2008, scientists did something extraordinary. For the first time, they built the full genetic code of a bacterium in the lab. That laid the foundation for being able to place the synthetic genome inside a cell and in a way “restart” the biological machinery. Many scientists described this as the first form of synthetic life.
Now that scientists are armed with AI, can they take a step forward? We know that AI is capable of learning patterns across trillions of DNA letters. Could that shift genome design from manual engineering to machine generation?
That is exactly what a team of researchers led by computational biologist Brian Hie at the Arc Institute in Palo Alto, California, together with bioengineer Patrick Hsu, set out to explore.
In a new Nature paper, the researchers introduced Evo2, a new AI model trained on trillions of DNA letters from organisms across the tree of life. Using the system, the team generated complete genome sequences, including one inspired by the bacterium Mycoplasma genitalium.
Why does this matter? If AI can design working genomes, it could dramatically speed up synthetic biology. This would allow scientists to create entirely new organisms for medicine, energy, biotechnology, and other use cases.
The details of the Evo2 model were published on March 4 in a paper in the journal Nature. The model works by treating DNA like language. However, instead of using words, it analyzes long strings of genetic letters that make up genomes. The researchers claim that the model is trained on trillions of DNA bases collected from thousands of organisms across bacteria, plants, animals, and other life forms.
(Gorodenkoff/Shutterstock)
Evo2 studies these sequences to learn how genes and other genomic features tend to appear and interact within real genomes. So instead of predicting the next word in text, like LLMs do, Evo2 predicts which DNA sequences are biologically plausible.
Many genomic AI models do something similar, but Evo2 is different in a few important ways. Earlier genomic AI models often focus on short DNA segments, Evo2 is designed to operate at a far larger scale. The system can model sequences millions of letters long. This allows it to capture patterns that span entire genomic regions.
Working at this scale allows the model to capture how different parts of the genome interact with each other. That capability is critical when attempting to generate long DNA sequences that resemble complete genomes.
“These AI models are the ‘ChatGPT moment’ for synthetic genomics,” says genome engineer Patrick Yizhi Cai at the University of Manchester, UK. “You can start writing things that never existed in nature.” Cai is an independent expert commenting on the work.
The developers of Evo2 put it to test asking the model to generate genome-scale DNA sequences inspired by Mycoplasma genitalium – a bacterium known for having one of the smallest genomes of any free living organism. The bacterium is often used in synthetic biology research because its genome is small and relatively simple. That makes it a useful starting point for experiments aimed at building or redesigning genomes.
According to the researchers, Evo2 was successful in generating long stretches of DNA that follow the structural patterns seen in real genomes. This offers a possible blueprint for designing new organisms.
It’s worth keeping in mind that designing DNA in a lab is just the first step. A sequence that looks plausible to a model may not necessarily function inside a living cell.
“It’s cool, but it’s not there yet,” says Nico Claassens, a synthetic biologist at Wageningen University in the Netherlands. One challenge is that AI designed genomes still need to be synthesized and tested in the lab. Another is designing DNA that can control all the essential functions of a living cell.
Scientists have spent decades learning how to read DNA. More recently, technologies such as CRISPR have allowed researchers to edit genes with increasing precision. Systems like Evo2 hint at a new stage where AI could help design entire genomes from scratch.
If these tools mature and are tested across different environments, synthetic biology may gradually shift from modifying existing organisms toward designing new biological systems directly from data. Whether AI will eventually help create fully functional synthetic life remains to be seen, but the direction of travel is becoming clearer – and a lot faster.
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The post A New AI Model Could Help Scientists Design New Forms of Life appeared first on BigDATAwire.
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