Interpretable‐AI‐Based Model Structural Transfer Learning to Accelerate Bioprocess Model Construction
Alexander W. Rogers, Fernando Vega‐Ramon, Amanda Lane, Philip A. Martin, Dongda Zhang
Abstract
Determining accurate kinetic models for new biochemical systems is time-intensive, requiring experimental data collection, model construction, validation, and discrimination. Traditional black-box machine learning-based transfer learning methods leverage prior knowledge but lack interpretability and physical insights. To address this, we propose a novel model structural transfer learning approach that combines symbolic regression with artificial neural network feature attribution. The method enables automatic structural modification of an inaccurate or low-fidelity mechanistic model developed for one system when being applied to another system. Through a comprehensive in silico case study, our framework successfully adapted a kinetic model from one biochemical system to a different but related one, improving predictive accuracy. Moreover, the framework can significantly accelerate model identification when being integrated with model-based design of experiments. By comparing the old and new model structures, physical insight can be obtained, altogether highlighting the framework's potential for advancing automated knowledge discovery and facilitating high-fidelity predictive digital twin design for novel biochemical processes.