AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification

• URL: http://arxiv.org/abs/2310.01433v1
• Date: Fri, 29 Sep 2023 14:45:51 GMT
• Title: AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification
• Authors: Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla, George Em Karniadakis
• Abstract summary: We present a new framework for parameter estimation and missing physics identification (gray-box) in Systems Biology. The proposed framework -- named AI-Aristotle -- combines eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) We test the accuracy, speed, flexibility and robustness of AI-Aristotle based on two benchmark problems in Systems Biology.
• Score: 1.8434042562191815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identification (gray-box) in the field of Systems Biology. The proposed framework -- named AI-Aristotle -- combines eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR) techniques for parameter discovery and gray-box identification. We test the accuracy, speed, flexibility and robustness of AI-Aristotle based on two benchmark problems in Systems Biology: a pharmacokinetics drug absorption model, and an ultradian endocrine model for glucose-insulin interactions. We compare the two machine learning methods (X-TFC and PINNs), and moreover, we employ two different symbolic regression techniques to cross-verify our results. While the current work focuses on the performance of AI-Aristotle based on synthetic data, it can equally handle noisy experimental data and can even be used for black-box identification in just a few minutes on a laptop. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond.

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