AI-Optimized Stem Cell Differentiation

Using the CellOS virtual cell model to predict how gene perturbations may affect iPSC differentiation trajectories and cell states, we help optimize induced differentiation protocols, reduce inefficient trial and error, and improve the efficiency and reproducibility of differentiation process development.

Project Background

A leading Chinese regenerative medicine company focused on pluripotent stem cell therapeutics, with multiple pipelines entering process scale-up. Its conventional induced differentiation process relied heavily on extensive wet-lab gradient experiments and faced fluctuating differentiation efficiency, complex process parameters, and long development cycles. The company looked to AI virtual cell technology for a breakthrough to improve process stability and R&D efficiency and to support subsequent scale-up and quality control.

The Challenge

1	High consumable and labor costs from trial-and-error testing of multi-pathway factor combinations such as WNT and BMP;
2	Temporal dynamics of stem cell differentiation cannot be predicted in advance, leading to marked batch-to-batch variation in differentiation efficiency;
3	Each round of protocol validation takes days, so pipeline iteration cannot keep pace with commercialization timelines;
4	Experimental data cannot be captured in a standardized way, making it difficult to continuously refine a proprietary induction process.

Solution

1	Build a dedicated temporal digital twin of stem cells on the CellOS foundation model, recreating multi-day differentiation trajectories;
2	Use the AuraPert virtual perturbation engine to simulate tens of thousands of signaling factor combinations in batch;
3	Intelligently converge on the optimal induction process and output a standardized, ready-to-implement operating procedure;
4	Connect the wet-lab data feedback pipeline and establish an active learning loop that continuously improves prediction accuracy.