Bilevel Optimisation for Targeted Metabolic Network Reduction

Metabolic network models are powerful tools for understanding cellular functions and guiding biotechnological applications. Yet, the complexity of these models poses challenges in accurately predicting intracellular flux distributions, considering limited measurement availability. To address this, we propose a novel bilevel optimisation framework to metabolic network reduction using Bayesian optimisation. Our method assigns continuous probability values to reactions and iteratively refines a reduced model that balances network simplification with predictive performance. The upper-level Bayesian optimisation process selects reaction removal probabilities, while the lower level evaluates model feasibility and performance through flux sampling. A Gaussian Process surrogate is trained to approximate the impact of reaction removals on model accuracy, guiding the optimisation toward a minimal yet representative network. We applied our methodology to a Chinese Hamster Ovary (CHO) cell metabolic model using multiple datasets, demonstrating its ability to adapt to different datasets and suggest targeted measurements. By unifying lumping and sensitivity analysis concepts in a data-driven framework, our approach systematically simplifies metabolic models, increasing their applicability in both development and manufacturing processes.