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

2026-06-18 · Source: stat.ML updates on arXiv.org · Field: Science & Research — Artificial Intelligence & Machine Learning, Engineering & Applied Sciences, Research Methodology & Innovation · Depth: Expert, extended

Summary

This work introduces an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework. The enhanced framework addresses constrained optimization by integrating the posterior probability of satisfying output specification limits as an explicit Pareto objective, derived analytically from the Gaussian process (GP) posterior distribution. It also tackles robust optimization through a Monte Carlo sampling strategy, which estimates expected lower-confidence performance under user-defined input perturbations. This multi-dimensional Pareto representation makes trade-offs between predicted performance, model uncertainty, probabilistic constraint satisfaction, and input robustness simultaneously visible on an interactive dashboard. The framework is demonstrated on an eight-dimensional fed-batch Chinese Hamster Ovary (CHO) cell culture simulator, successfully identifying high-performing, feasibility-compliant, and perturbation-resilient operating conditions.

Key takeaway

For bioprocess development engineers optimizing complex, expensive systems, this framework offers a powerful way to integrate domain expertise directly into the optimization loop. You can make informed trade-offs between performance, uncertainty, constraint satisfaction, and robustness by interactively selecting candidates from a multi-dimensional Pareto front. Consider implementing a Human-in-the-Loop Bayesian Optimization approach to systematically identify optimal operating conditions while accounting for real-world variability and regulatory requirements.

Key insights

Human-in-the-Loop Bayesian Optimization integrates expert knowledge for constraint-aware and robust bioprocess development.

Principles

• Reformulate GP-derived quantities as multi-objective problems.
• Expose Pareto fronts for interactive expert candidate selection.
• Probabilistic constraint satisfaction can be a Pareto objective.

Method

PFGS extends BO by reformulating GP-derived quantities (mean, uncertainty, constraint probability, robustness) as multi-objectives. Experts interactively select candidates from the Pareto front, refining criteria as the model improves.

In practice

• Use probabilistic CQA satisfaction as a Pareto objective.
• Estimate robustness via Monte Carlo sampling for CPP deviations.
• Apply expert-defined thresholds to filter Pareto front candidates.

Topics

• Bayesian Optimization
• Human-in-the-Loop AI
• Bioprocess Development
• Constrained Optimization
• Robust Optimization
• Pareto Front Guided Sampling
• Gaussian Processes

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.