ArticlePLoS computational biology2024
AI-Aristotle: A physics-informed framework for systems biology gray-box identification.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- A Practical Tutorial on Physics-Informed Networks for Pharmacometrics and Quantitative Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2026Article
- Improvement in model flexibility reveals signaling pathway in T cell responses to pulsatile stimuli.NPJ systems biology and applications · 2026Article
- Learning dynamical systems with biochemically informed neural ordinary differential equations.bioRxiv : the preprint server for biology · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- A Multiscale Signaling-Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease.bioRxiv : the preprint server for biology · 2026Article
- Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Toxicological Outcomes via Symbolic Regression with Novel Feature Scoring.Chemical research in toxicology · 2026Article
- FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design.Chemical engineering science · 2026Article
- Representation meets optimization: Training PINNs and PIKANs for gray-box discovery in systems pharmacology.Computers in biology and medicine · 2026Article
- Network biology to artificial intelligence: building the next generation of predictive drug discovery.Frontiers in immunology · 2026Review
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Harnessing Nonlinear Mechanics to Transform Medical Diagnostics.Applied mechanics reviews · 2025Article
- In Silico Post-screening of Anti-polymerization Agents to Treat Sickle Cell Disease.bioRxiv : the preprint server for biology · 2025Article
- Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks.Science advances · 2025Article
- Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology.ArXiv · 2025Article
- Challenges and opportunities in uncertainty quantification for healthcare and biological systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- CMINNs: Compartment model informed neural networks - Unlocking drug dynamics.Computers in biology and medicine · 2025Article
- Integrating QSP and ML to Facilitate Drug Development and Personalized Medicine.Handbook of experimental pharmacology · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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 the 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. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.