Rogers Alexander W, Vega-Ramon Fernando, Lane Amanda, Martin Philip, Zhang Dongda
Department of Chemical Engineering, The University of Manchester, Manchester, UK.
Unilever R&D Port Sunlight, Wirral, Liverpool, UK.
Biotechnol Bioeng. 2025 Oct;122(10):2819-2831. doi: 10.1002/bit.70026. Epub 2025 Jul 18.
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.
为新的生化系统确定准确的动力学模型需要耗费大量时间，这需要进行实验数据收集、模型构建、验证和甄别。传统的基于黑箱机器学习的迁移学习方法利用了先验知识，但缺乏可解释性和物理洞察力。为了解决这个问题，我们提出了一种新颖的模型结构迁移学习方法，该方法将符号回归与人工神经网络特征归因相结合。当应用于另一个系统时，该方法能够对为一个系统开发的不准确或低保真的机理模型进行自动结构修改。通过全面的计算机模拟案例研究，我们的框架成功地将一个生化系统的动力学模型应用于另一个不同但相关的系统，提高了预测准确性。此外，当与基于模型的实验设计相结合时，该框架可以显著加速模型识别。通过比较新旧模型结构，可以获得物理洞察力，这突出了该框架在推进自动化知识发现以及促进新型生化过程的高保真预测数字孪生设计方面的潜力。
