mlsanityData to 7 Oct 2026

Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks

arXiv 2202.01723 ↗First posted 3 Feb 2022q-bio.QM cs.LGcited by 23

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Abstract (arXiv)

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 ultridian endocrine model for glucose-insulin interaction as the example to demonstrate all these methods and their implementation.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 3 of its 12 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

3 of 12 references.

References · 3

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019DeepXDE: A deep learning library for solving differential equationscited by 617

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 6; 2024: 5; 2025: 4; 2026: 4.

23 papers.

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  1. 2022A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networkscited by 233
  2. 2022MIONet: Learning multiple-input operators via tensor productcited by 180
  3. 2022Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transportcited by 92
  4. 2022Blending Neural Operators and Relaxation Methods in PDE Numerical Solverscited by 53
  5. 2023Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestrationcited by 46
  6. 2023AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identificationcited by 29
  7. 2024Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestrationcited by 11
  8. 2025Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacologycited by 10
  9. 2024Speeding up and reducing memory usage for scientific machine learning via mixed precisioncited by 8
  10. 2023Deep Learning for Solving and Estimating Dynamic Macro-Finance Modelscited by 6
  11. 2023Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactorscited by 3
  12. 2023Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networkscited by 2
11 more
  1. 2024A Digital Twin for Diesel Engines: Operator-infused Physics-Informed Neural Networks with Transfer Learning for Engine Health Monitoringcited by 2
  2. 2025FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor designcited by 2
  3. 2024HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety Assessmentcited by 1
  4. 2025Physics Informed Constrained Learning of Dynamics from Static Datacited by 1
  5. 2023The Lost Art of Mathematical Modellingcited by 0
  6. 2024jinns: a JAX Library for Physics-Informed Neural Networkscited by 0
  7. 2025Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integrationcited by 0
  8. 2026Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in firescited by 0
  9. 2026Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approachcited by 0
  10. 2026Physics-Informed Neural Networks for Chemotherapy Pharmacokinetics: Benchmarking the Clinical Estimator and Exposing Parameter Identifiabilitycited by 0
  11. 2026Identifiability Limits of Physics-Informed Inference for Spatial Stochastic Dynamics from Static Snapshotscited by 0