RESOLVING THE LEDGER
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Add as preferred source Scientists have crossed an important line in biological engineering. In a recent study , researchers used artificial intelligence to design complete sets of genetic instructions for bacteriophages, viruses that infect bacteria. Some of those computer-generated designs produced working viruses when they were built and tested in the laboratory.
These viruses were designed to infect bacteria rather than people. The researchers worked with bacteriophages that target E. coli, and one of the AI models they used, Evo 2 , was developed with safety restrictions. Its developers excluded viruses that infect organisms such as animals, plants and humans from the data used to train it.
Even so, the experiment raises an important question: If AI becomes increasingly able to help design biological systems, are our safeguards developing fast enough?
As engineers working in vaccine manufacturing and public health security, we see good reasons to continue this research. Bacteriophages could potentially be used to treat bacterial infections, including those that are becoming harder to treat with antibiotics. But advances in biological design also create new challenges for biosecurity.
Genome language models work in a broadly similar way to AI language models that learn patterns in text. Instead of learning from words and sentences, they learn patterns in genetic code. Evo 2, for example, was trained on trillions of DNA building blocks taken from many different forms of life.
This gives researchers a powerful new way to study biology and generate possible DNA sequences. It also means that part of biological design can now happen on a computer, before anything has been made in a laboratory.
An AI-generated DNA sequence is still several steps away from becoming a real biological threat. A digital design has to be turned into physical genetic material, assembled correctly and tested under the right laboratory conditions. Each of those stages also creates an opportunity to reduce risk.
One safeguard can be built into the AI model itself. The developers of Evo 2 deliberately removed viruses that infect humans and other similar organisms from its training data for safety reasons. Their research found that the model performed poorly when tested on proteins from viruses that infect humans.
Such precautions will need to be tested and strengthened as AI tools for biology become more capable. Researchers have already argued that AI and biosecurity need to be considered together , so that safeguards develop alongside new capabilities.
Another checkpoint comes when digital DNA is turned into physical DNA. Companies that manufacture DNA to order can check both the requested genetic sequence and the person or organization ordering it for potential security concerns. Researchers working on DNA synthesis screening have argued for common international approaches as this technology becomes more widely available.
Screening may also need to become more sophisticated. A system designed mainly to recognize DNA that resembles known dangerous pathogens may struggle if AI produces something genuinely new. Recent research on identifying " sequences of concern " has therefore considered what a genetic sequence might do, as well as whether it resembles one already linked to a dangerous organism.
Laboratories and research institutions provide another layer of protection. Potentially risky research can be assessed before experiments begin, including questions about how biological material will be contained, who will have access to it and whether the expected benefits justify the risks.
Research funders can also influence how security risks are managed. UK Research and Innovation (UKRI), for example, has established a Trusted Research and Innovation team to help researchers and institutions identify and manage security risks in collaborative research.
Public health preparedness belongs in this discussion too. If biological design becomes faster and more accessible, health authorities will need to be able to detect and investigate unusual outbreaks quickly. The Metagenomics Surveillance Collaboration and Analysis Program (mSCAPE) , led by the UK Health Security Agency, analyzes genetic material from samples to help detect and track emerging pathogens.
The program is not specifically designed to find AI-created viruses. But its approach is relevant because scientists do not have to know exactly which pathogen they are looking for in advance. Systems capable of detecting unusual or previously unseen organisms could become increasingly valuable as biological technologies develop. Preparedness also means being able to develop diagnostic tests, treatments and vaccines quickly when a new threat appears.
In our view, one of the biggest weaknesses in global biosecurity is that countries are not equally prepared. Some already have established systems for identifying and responding to biological risks, while others are still developing them. We see a similar gap in vaccine regulation, where countries vary considerably in their ability to assess new products. Biological threats can cross borders, so effective preparedness will depend on strengthening these capabilities internationally.
There are difficult trade-offs. Heavy restrictions can slow useful research, including work aimed at understanding disease or developing treatments. Weak safeguards can allow scientific capability to develop faster than the systems intended to prevent misuse.
Open science makes this harder. Evo 2 was released openly, including the code and technical information needed for other researchers to use and develop it. Openness can accelerate discovery and widen access to powerful research tools. But once s...
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