LLM-Guided Parameter Optimization for Mechanistic CHO Cell Bioreactor Models

Mechanistic models of Chinese Hamster Ovary (CHO) cell cultures are essential for efficient bioprocess development and control, yet parameter estimation remains a critical challenge in model calibration. As model complexity increases, the number of parameters grows and their values become highly system-specific due to biological variability across cell lines and operating conditions. Consequently, estimation requires broad search spaces, leading to inefficient exploration and increased risk of entrapment in local minima during numerical optimization. To address this, a structure-aware optimization framework is proposed that integrates large language model (LLM) reasoning into the parameter estimation loop. The LLM analyzes governing equations and trajectory discrepancies to recommend directional adjustments for parameter boundaries. These insights are combined with correlation-based scaling to adaptively refine the search space, which is explored using stochastic global search methods (Latin hypercube sampling [LHS] and Covariance matrix adaptation evolutionary strategy [CMA-ES]). Applied to fed-batch CHO cell culture data, the framework escapes local minima and resolves complex, nonlinear metabolite dynamics, including lactate and ammonium shifts. Statistical validation across multiple batches and random seeds shows consistent reduction in prediction error and improved optimization robustness. This study demonstrates the potential of integrating structure-aware LLM guidance with numerical optimization to improve the reliability and efficiency of bioprocess modeling.