We’ve developed proprietary gene editing and screening modalities in-house that allow us to create thousands of custom cell designs simultaneously.

We test our cells in the most therapeutically relevant in vivo animal models. By challenging our candidate designs with biologically representative conditions, we are making the translation gap smaller.

We employ genomics technologies such as single-cell RNA-seq in order to explore how our candidate designs perform. These technologies enable us to achieve both breadth and depth of analyses and execute thousands of micro-experiments in parallel.

We develop custom machine learning workflows to collapse the large-scale data generated from our screens, learning regulatory relationships between genes, and predicting better-performing cell designs.

Repeating these steps in a systematic and repeatable process enables us to converge on the design of optimal cell therapeutics. We envision this to be scalable and repeatable across diseases and cell types.