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Open hardware for live-cell experiments.

Change something. Watch.

See how it works

The cells never leave the plate. The robot changes their conditions. We watch.

What comes back is a model. Which conditions make these cells work, with the evidence attached.

Every condition the robot sets is a perturbation; every frame is a measurement. From that time series we fit a causal model of how the cells respond, the approach Karen Sachs introduced for single-cell perturbation data in Science (2005).

Founders

Eric Jing Mockler, smiling in glasses and a burgundy shirt

Eric Jing Mockler

Co-founder & Engineer

Bioengineering, cognitive science, neuroscience at UC Santa Cruz. Variant discovery across 10,000+ genomes at Gladstone and Answer ALS. With the Gyori lab, the belief model: does the paper actually say this causes that.

Karen Sachs, smiling, with sunglasses pushed up into her curly hair

Karen Sachs, PhD

Co-founder · CSO/CTO

MIT PhD, biological engineering. Stanford Medicine postdoc. Causal networks from single-cell perturbation data; Science Breakthrough of the Year runner-up. 60+ papers.

If you run a wet lab, or hold perturbation data, write.

Open hardware under CERN-OHL-S-2.0 · Aeon-Bio on GitHub