AI-Driven Optimization
Exploring media and process conditions through predictive models and experimental feedback.
We explore combinations of medium components using Bayesian optimization and evaluate recommended conditions in CHO cell-culture experiments. In this collaboration, the laboratory defines formulation ranges, validates cultures and supplies experimental data; the collaborating company provides model-based recommendations and model updates.
WHY IT MATTERS
Combining culture data with biological knowledge helps interpret relationships among process variables and guide experiments.
Research directions
- Cell-line-specific media formulation
- Bayesian optimization and sequential design
- Culture validation and experimental feedback