A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development

2026-06-17

AI Summary

This study addresses the challenge in bioprocess development of simultaneously optimizing performance, constraint satisfaction, and operational robustness—a task traditionally reliant on expert judgment. To this end, the authors propose a human-in-the-loop multi-objective Bayesian optimization framework that explicitly incorporates the probability of constraint satisfaction and robustness under input perturbations into the Pareto optimization objectives. The approach integrates Gaussian process surrogate models, Monte Carlo–based robustness evaluation, and Pareto-guided sampling, complemented by an interactive four-dimensional visualization interface to support dynamic expert decision-making. Demonstrated on an eight-dimensional fed-batch CHO cell culture simulation, the method efficiently identifies high-performing, feasible, and robust operating conditions, substantially improving experimental resource efficiency and enabling more intelligent termination criteria for iterative optimization.

Abstract

This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single automated recommendation. The framework is extended in two directions: constrained optimization is addressed by incorporating the posterior probability of satisfying output specification limits as an explicit Pareto objective, computed analytically from the GP posterior distribution; robust optimization is addressed by a Monte Carlo sampling strategy that estimates expected lower-confidence performance over a user-defined variability of input perturbations, capturing performance degradation under likely implementation deviations. The resulting multi-dimensional Pareto representation renders trade-offs between predicted performance, model uncertainty, probabilistic constraint satisfaction, and input robustness simultaneously visible through pairwise two-dimensional projections on an interactive dashboard, enabling selection criteria to be iteratively refined as the surrogate model improves and development objectives evolve. The framework is showcased on an eight-dimensional fed-batch Chinese Hamster Ovary (CHO) cell culture simulator demonstrating systematic identification of high-performing, feasibility-compliant, and perturbation-resilient operating conditions, and illustrating how expert-defined requirements provide a principled stopping criterion and support informed allocation of experimental resources.

Problem

• constrained optimization
• robust optimization
• bioprocess development
• human-in-the-loop
• Bayesian optimization

Innovation

• Human-in-the-Loop
• Bayesian Optimization
• Constrained Optimization
• Robust Optimization
• Pareto Front