A Billion Fruit Flies

Justice for the fruit flies. For more than a century, Drosophila melanogaster, the humble fruit fly, has been one of biology’s standard experimental organisms. Researchers have bred them, mutated them, exposed them to chemicals, altered their neurons and dissected them by the millions because they are cheap, prolific and biologically useful enough to answer questions that would be impractical to ask of larger animals. Now researchers are building AI models detailed enough to ask some of those questions before reaching for another living fly.

Researchers led by the Howard Hughes Medical Institute’s Janelia Research Campus, working with Google Research and other collaborators, recently published the finished connectome of an entire male fruit fly central nervous system. The map contains 166,691 neurons, more than 125 million synaptic connections and 11,691 identified cell types, stretching from the brain and optic lobes through the ventral nerve cord. For the first time in a male fly, researchers can trace an essentially complete structural path from sensory inputs toward the neurons that ultimately control movement.

The wiring diagram is already useful for more than anatomy. In 2024, another team used measured connectivity from the fruit fly visual system to construct a model of more than 45,000 neurons, then used deep learning to infer properties of neurons and synapses that hadn’t been measured directly. When researchers tested the model against decades of neuroscience experiments, it reproduced neural responses measured across 26 previous studies.

A separate team working with Google DeepMind has built an anatomically detailed virtual fruit fly body inside a physics simulator. The model, based on an adult female fly, can walk and fly with realistic movement; its nervous system is controlled by an artificial neural network trained on real fly behavior rather than a biological reconstruction of the fly brain.

The new male connectome hasn’t been integrated with that anatomically detailed body model into a validated whole-animal simulation, but researchers are already trying.

Biologists, like nature itself, have, until now, always been constrained by biology, itself. Change a gene, breed the animal, administer the compound, grow the cells, wait for the response and measure what happened. Every additional test consumes animals, reagents, equipment, technicians and time; researchers therefore spend enormous effort deciding which experiments are worth running before anything reaches the bench.

A laboratory that can physically test a limited number of ideas can now inspect far more of them, first, eliminate weak candidates and concentrate its equipment and people on experiments more likely to produce useful information. Even better, the model doesn’t have to reproduce an organism perfectly to save considerable time and money; it only has to become good enough at identifying dead ends that researchers stop testing all of them individually in the “real world.”

The Arc Institute’s Virtual Cell Challenge is pushing directly at that problem. Its 2026 competition asks models to predict how cells respond to genetic changes in biological contexts they haven’t previously seen perturbed. That is much closer to the problem confronting an actual laboratory, where the possible experiments vastly outnumber the experiments anyone has the time or money to perform.

One living fly can still provide the observation that settles a question, except researchers may reach that experiment after a computer has already discarded thousands of less promising ones. The physical experiment becomes more targeted, and the data generated by it can feed the next generation of models.

That makes high-quality experimental data more valuable as well. The new connectome required years of electron microscopy, reconstruction and validation; virtual-cell models depend on similarly expensive biological datasets. An experiment that once answered a specific research question can also produce data used across thousands of later computational experiments, allowing the value of the original laboratory work to extend well beyond the study that paid for it.

Eventually, this runs into another constraint: laboratories still have to test what the models produce. If computation can generate useful hypotheses faster than scientists can validate them, automated laboratories, robotics, better assays, organoids and faster experimental systems become more important. The amount of physical biology may not fall in every field; researchers may instead run better-selected experiments at much greater scale.

The FDA is expanding its use of New Approach Methodologies, including computational models and human-derived systems, when they can provide reliable evidence without automatically requiring an animal study. In March, the agency released draft guidance on validating and incorporating these methods into drug development; NIH committed more than $150 million this year to human-based research methods and created a dedicated office in June to develop and scale computational tools, 3D human tissue systems and other alternatives to animal models.

Whole-organism physiology is still hard to reproduce on a computer, so some questions will continue to require living animals. What can change much faster is how many experiments have to reach an animal in the first place. A drug company choosing among 10,000 compounds has a very different animal-testing requirement if computational screening can eliminate 9,500 of them before the remaining candidates ever leave the computer.

Animal experimentation has long served two purposes at once: testing serious hypotheses and searching for the hypotheses worth taking seriously. Computation is beginning to take over more of the search.

There are obvious economic reasons to accelerate that process. Animals cost money, experiments take time and failed candidates consume laboratory capacity that could have been used somewhere else. There are also obvious ethical benefits if fewer animals are needed to produce the same or better scientific results.

We’re unlikely to reach the Lab Rat Utopia anytime soon. But a growing share of the work once performed by breeding an animal, administering something to it and waiting to see what happens can move upstream into computation, reserving living systems for the questions that survive the screening process.

The fruit fly has spent more than a century helping scientists search for answers one generation and one experiment at a time. We’re getting better at searching before the experiment begins; for a billion future fruit flies, that could make all the difference.