ArticleMethods in molecular biology (Clifton, N.J.)2023
Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks.
Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A Likelihood-Based Framework for Simultaneously Learning Both Noise and Growth Dynamics Using Biologically-Informed Neural Networks.Bulletin of mathematical biology · 2026Article
- 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
- Representation meets optimization: Training PINNs and PIKANs for gray-box discovery in systems pharmacology.Computers in biology and medicine · 2026Article
- FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design.Chemical engineering science · 2026Article
- One-shot learning for solution operators of partial differential equations.Nature communications · 2025Article
- A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks.Acta biomaterialia · 2025Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology.ArXiv · 2025Article
- Article
- Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.Small methods · 2025Article
- ACognitive neurodynamics · 2024Article
- Article
- Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta.Nexus (New York, N.Y.) · 2024Article
- AI-Aristotle: A physics-informed framework for systems biology gray-box identification.PLoS computational biology · 2024Article
- Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies.Theranostics · 2024Review
- In silico model development and optimization of in vitro lung cell population growth.PloS one · 2024Article
- Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem.Scientific reports · 2023Article
- Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics.Applied mathematics and mechanics · 2023Article
- Learning Interactions in Reaction Diffusion Equations by Neural Networks.Entropy (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dynamics of systems biological processes are usually modeled by a system of ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from noisy and sparse measurements. Here, we introduce systems-biology-informed neural networks for parameter estimation by incorporating the system of ODEs into the neural networks. To complete the workflow of system identification, we also describe structural and practical identifiability analysis to analyze the identifiability of parameters. We use the ultradian endocrine model for glucose-insulin interaction as the example to demonstrate all these methods and their implementation.
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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.