A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development
Researchers have developed an enhanced Human-in-the-Loop Bayesian Optimization framework called Pareto Front Guided Sampling (PFGS). This framework allows domain experts to interactively select optimal candidates by reformulating Gaussian process surrogate-derived quantities into a multi-objective optimization problem. The system now incorporates constrained optimization by considering the probability of meeting specification limits and robust optimization by estimating performance degradation under input perturbations. The extended PFGS framework was demonstrated on a Chinese Hamster Ovary (CHO) cell culture simulator, successfully identifying operating conditions that are high-performing, feasible, and resilient to perturbations. AI
IMPACT This framework could improve efficiency and success rates in complex bioprocess development by integrating expert knowledge with advanced optimization techniques.