Thousands of GPT-Rosalind Agents Analyze Mouse Brain Data in 32 Minutes

Distributed biological reasoning at scale

The team split the analysis across thousands of agents powered by GPT-Rosalind, an approach OpenAI calls distributed biological reasoning. The agents worked on a whole-brain CRISPR atlas, a dataset showing how gene disruptions affect different types of cells across the mouse brain.

Each agent investigated one gene disruption, analyzed cellular responses, searched scientific literature and proposed explanations for the results. Their reports were then combined into a reasoning atlas that links genes, cellular effects, relevant publications and possible biological mechanisms.

The research team estimated that comparable human analysis and literature review, done one perturbation at a time, would take months to a year. According to OpenAI, the agents reproduced findings from human analyses while uncovering additional leads, and their assessments of novelty and scientific value aligned with judgments from blinded human experts.

Leads on Tsc1, Tsc2 and DCTN1

One finding concerned Tsc1 and Tsc2, two genes that work together to control mTOR, a major regulator of cell growth, metabolism and aging. Disrupting either gene had similar effects in several groups of neurons. In the cerebellum, however, Tsc2 disruption altered the activity of dozens of genes involved in neuronal communication, while Tsc1 disruption produced little change.

OpenAI notes that in people with tuberous sclerosis, cerebellar abnormalities are strongly linked to TSC2 mutations. Medicines that reduce mTOR activity can treat certain brain tumors and seizures, but learning and behavioral difficulties linked to the disease remain hard to address. The agents pointed to a specific group of neurons and cellular changes worth investigating.

A second lead involved DCTN1, a gene whose mutations cause Perry syndrome, a neurodegenerative disease with Parkinson-like symptoms. In the atlas, disrupting Dctn1 increased the expression of genes involved in making cholesterol. The agents tied this to research suggesting that problems with cholesterol transport may cause cells to make more cholesterol. That raised a follow-up question: could DCTN1 disruption change how cholesterol is distributed within neurons, and could this contribute to neurodegeneration? Human experts considered the hypothesis novel, consequential and worth following up on, according to OpenAI.

Many agents versus one

The team also tested whether a single agent given the whole dataset could match thousands working in parallel, keeping the model and resources fixed. As the workload grew, the single agent searched less of the literature. At the largest workload it cited only 11 references, while the distributed agents cited more than 700.

In blinded comparisons, human experts preferred distributed-agent reports over single-agent reports 75% of the time, according to OpenAI. The company argues that parallel agents can examine a wider range of responses, including subtle or unexpected ones, instead of focusing only on the strongest signals.

What it means for researchers

The reasoning atlas is available now, so scientists can compare related genes, investigate unexpected responses and pick leads to pursue. The findings are hypotheses, and OpenAI says they help prioritize further experiments needed to test the proposed mechanisms.

Tomasz Nowakowski of UCSF said of the possible link between neuronal function and cholesterol metabolism that it "shows how those pieces, once brought together, can give us a new way to think about disease biology." OpenAI stresses that scientists remain at the center of discovery, with AI helping them decide which ideas to pursue, design experiments and interpret results.