AI models trained on genomic data promise shortcuts to understanding disease, but biologists warn that the genome's regulatory complexity may resist the computational approach these algorithms assume.

What happened: Genomic foundation models such as Evo 2, Genos, and Google DeepMind's AlphaGenome are being trained on vast quantities of genomic data to make predictions about how DNA differences affect biological processes and disease risk. These algorithms use patterns learned from known cases rather than simulating the actual regulatory mechanisms.

Why it matters: The human genome is far more complicated than a blueprint or algorithm. While only about 2% of the 3 billion DNA building blocks code for proteins, gene regulation—how genes are turned on and off—involves overlapping systems of control across hundreds of thousands or millions of regulatory elements called enhancers. Biologists have known about gene regulation since the 1960s, but in complex organisms like humans the logic operates on an 'AND' basis, integrating many signals at once, rather than the simple 'OR' logic of bacteria. This may mean a computational black box, however accurate, will not satisfy researchers who seek genuine understanding of how the genome actually works.

What to watch: A leading genome biologist stated it is 'embarrassing that 25 years after the Human Genome Project, we don't know where all the enhancers are in the genome, let alone what they do when they act and which genes they control.' The mismatch between enhancers' physical distance from the genes they regulate—sometimes millions of nucleotides apart—poses a foundational puzzle that AI-driven pattern-matching may not resolve.