Multi-objective Bayesian algorithm automatically discovers low-cost high-growth serum-free media for cellular agriculture application.

In this work, we applied a multi-information source modeling technique to solve a multi-objective Bayesian optimization problem involving the simultaneous minimization of cost and maximization of growth for serum-free C2C12 cells using a hyper-volume improvement acquisition function. In sequential batches of custom media experiments designed using our Bayesian criteria, collected using multiple assays targeting different cellular growth dynamics, the algorithm learned to identify the trade-off relationship between long-term growth and cost. We were able to identify several media with more growth of C2C12 cells than the control, as well as a medium with 23% more growth at only 62.