How our genome is like a generative AI model

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What does the genome do? You might have heard that it is a blueprint for an organism. Or that it’s a bit like a recipe. But building an organism is much more complex than constructing a house or baking a cake.

This week I came across an idea for a new way to think about the genome—one that borrows from the field of artificial intelligence. Two researchers are arguing that we should think about it as being more like a generative model, a form of AI that can generate new things.

You might be familiar with such AI tools—they’re the ones that can create text, images, or even films from various prompts. Do our genomes really work in the same way? It’s a fascinating idea. Let’s explore.

When I was at school, I was taught that the genome is essentially a code for an organism. It contains the instructions needed to make the various proteins we need to build our cells and tissues and keep them working. It made sense to me to think of the human genome as being something like a program for a human being.

But this metaphor falls apart once you start to poke at it, says Kevin Mitchell, a neurogeneticist at Trinity College in Dublin, Ireland, who has spent a lot of time thinking about how the genome works.

A computer program is essentially a sequence of steps, each controlling a specific part of development. In human terms, this would be like having a set of instructions to start by building a brain, then a head, and then a neck, and so on. That’s just not how things work.

Another popular metaphor likens the genome to a blueprint for the body. But a blueprint is essentially a plan for what a structure should look like when it is fully built, with each part of the diagram representing a bit of the final product. Our genomes don’t work this way either.

It’s not as if you’ve got a gene for an elbow and a gene for an eyebrow. Multiple genes are involved in the development of multiple body parts. The functions of genes can overlap, and the same genes can work differently depending on when and where they are active. It’s far more complicated than a blueprint.

Then there’s the recipe metaphor. In some ways, this is more accurate than the analogy of a blueprint or program. It might be helpful to think about our genes as a set of ingredients and instructions, and to bear in mind that the final product is also at the mercy of variations in the temperature of the oven or the type of baking dish used, for example. Identical twins are born with the same DNA, after all, but they are often quite different by the time they’re adults.

But the recipe metaphor is too vague, says Mitchell. Instead, he and his colleague Nick Cheney at the University of Vermont are borrowing concepts from AI to capture what the genome does. Mitchell points to generative AI models like Midjourney and DALL-E, both of which can generate images from text prompts. These models work by capturing elements of existing images to create new ones.

Say you write a prompt for an image of a horse. The models have been trained on a huge number of images of horses, and these images are essentially compressed to allow the models to capture certain elements of what you might call “horsiness.” The AI can then construct a new image that contains these elements.

We can think about genetic data in a similar way. According to this model, we might consider evolution to be the training data. The genome is the compressed data—the set of information that can be used to create the new organism. It contains the elements we need, but there’s plenty of scope for variation. (There are lots more details about the various aspects of the model in the paper, which has not yet been peer-reviewed.)

Mitchell thinks it’s important to get our metaphors in order when we think about the genome. New technologies are allowing scientists to probe ever deeper into our genes and the roles they play. They can now study how all the genes are expressed in a single cell, for example, and how this varies across every cell in an embryo.

“We need to have a conceptual framework that will allow us to make sense of that,” says Mitchell. He hopes that the concept will aid the development of mathematical models that might help us better understand the intricate relationships between genes and the organisms they end up being part of—in other words, exactly how components of our genome contribute to our development.

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