A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development
Samuel Stricker, Claus Wirnsperger, Alessandro Butté, Laura Helleckes, Gonzalo Guillén Gosálbez, Antonio del Rio Chanona, Mehmet Mercangöz
cs.LG, cs.HC, stat.ML
Submitted: 2026-08-24
Updated: 2026-08-25
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
Terminology
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.
Sources
- A Tutorial on Bayesian Optimization
- Multi-fidelity batch Bayesian optimization for bioprocess development across scales
- Safe Bayesian Optimization using Interior-Point Methods -- Applied to Personalized Insulin Dose Guidance
- Bayesian Optimization with Unknown Constraints
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