Posts

Today in AI for life science: genome foundation models learn to read and write DNA, an AI infers a spatial protein map from an ordinary pathology slide, and machine learning moves into 3D organoids — three layers of the virtual cell.

Today in AI for life science: industry pours billions into agentic labs and biology foundation models, virtual-cell models keep scaling while simple baselines stay stubbornly competitive, and image-based profiling races toward open, assay-agnostic foundation models.

Today in AI for life science: Biohub’s protein world model designs cancer binders that work in the wet lab, ’thinking microscopes’ turn instruments into co-scientists, and a sharp reminder that predictions still have to survive the bench.

Meet Safe Colab, the lab’s privacy-preserving collaboration platform — a way to learn from sensitive data like clinical records without ever moving it.

Today in AI for life science: CZI’s rBio learns to reason about cells with a virtual cell as its verifier, self-driving labs cross into industrial infrastructure, and a sharp reality check on what virtual-cell models still can’t do.

Nils Mechtel represented the lab at the 26th ELMI in Coimbra, giving the opening talk of the ‘AI in Core Facility Life’ session on BioEngine, and joining a lively panel on AI, agents and experimental design.

Today in AI for life science: agent benchmarks converge on a lesson we felt firsthand — the system around the model matters as much as the model — virtual-cell ‘world models’ arrive with a push for reproducibility standards, and the lab runs its first live agent-driven experiment.

At an invited talk at CZI, we ran a live, fully agent-controlled wet-lab experiment on the REEF Imaging Farm. A single natural-language prompt set an entire lab in motion, on real cells, in real time. We interviewed the agent that ran it.

Today in AI for life science: RNA structure and design catch up to proteins, digital-pathology foundation models mature at the clinical edge, and a new paper from the lab paints the first proteome-wide image of human cells.

Today in AI for life science: smart microscopy closes the loop between seeing and deciding, an AI autonomously ran tens of thousands of biology experiments in a cloud lab, and the rules are racing to keep up.