
Gain-of-Function Research
Gain-of-function research refers to laboratory experiments in which pathogens are deliberately altered so that they acquire new capabilities – for example, spreading more easily. The goal is to better understand future outbreaks; at the same time, this research is considered particularly high-risk and is strictly regulated.
Gain of Function translates to “gaining a function.” It refers to research in which scientists deliberately alter viruses or bacteria in the lab so that they can do something new. For example, a virus that previously only infected birds can be modified so that it also attacks mammalian cells. Such experiments take place in high-security laboratories, where the air is filtered and staff work in protective suits. The idea behind this: whoever knows which changes make a pathogen dangerous can warn earlier and develop countermeasures faster. But these very experiments are also the reason for one of the fiercest disputes in modern science.
The dispute between early warning and lab accidents
Proponents argue in favor of preparedness. A new flu virus constantly arises in nature by chance. If you can determine in the lab beforehand which mutations make it contagious, you can specifically test environmental samples for them. Vaccines and medications could then be prepared months earlier.
Critics counter that this creates dangers that did not exist before. Lab accidents are not a theoretical problem. There are documented cases in which staff at research facilities became infected or samples were shipped incorrectly. With a pathogen that spreads through the air, a single mistake would not stop at the laboratory fence.
There is also concern about misuse. Published blueprints for more dangerous pathogens could theoretically be used by states or groups for weapons. Experts call this dilemma “dual use”: the same knowledge protects and threatens at the same time. That is why some researchers withhold entire results or publish them only in abbreviated form.
How pathogens are modified in the lab
There are two basic approaches. The first imitates evolution: a virus is grown for many generations in cell cultures or laboratory animals, and each time the variants that prevailed best are selected. After dozens of rounds, the pathogen has adapted to the new environment. This procedure is called passage and requires no targeted intervention in the genetic material.
The second approach is direct genetic engineering. The genetic material of a pathogen is a long chain of chemical building blocks, comparable to a text made of four letters. Tools like the gene-editing scissors CRISPR can be used to swap out individual points in this text. Researchers use this, for instance, to alter the surface protein with which a virus docks onto human cells.
Not every modification is automatically sensitive. Often a pathogen is deliberately weakened in order to turn it into a vaccine – that is the reverse case, a loss of function. Only experiments with pathogens that can already cause severe diseases on their own are considered sensitive. In the US, this group is summarized under the abbreviation PPP, for potential pandemic pathogens.
From the bird flu debate to AI safety testing
The topic became publicly known in 2011. Two research groups made the H5N1 bird flu virus transmissible between ferrets. Scientific journals hesitated to publish, and the US halted funding for such projects for several years. Since the COVID-19 pandemic and the debate over a possible lab origin, gain of function has definitively become a political term.
In the news today, the term appears mainly in two contexts. First, in funding rules: governments determine which experiments receive funding at all and who reviews them beforehand. Second, in biotechnology investments, because stricter requirements can make entire research fields more expensive or slower.
The term also comes up in AI discussions. Large language models are tested before release to see whether they could help in building biological weapons. Companies like OpenAI and Anthropic publish reports on exactly this question. Gain-of-function knowledge serves as a benchmark for which knowledge a model is better off not freely disclosing.