SBINN: Systems-biology-informed neural network

The source code for the book chapter M. Daneker, Z. Zhang, G. E. Karniadakis, & L. Lu. Systems biology: Identifiability analysis and parameter identification via systems-biology-informed neural networks. Computational Modeling of Signaling Networks, Springer, 87--105, 2023.

Code

• Structural identifiability analysis
• SBINN
  • Data generation
  • TensorFlow code
  • PyTorch code
• Practical identifiability analysis

Cite this work

If you use this code for academic research, you are encouraged to cite the following paper:

@Inbook{Daneker2023,
  title     = {Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks},
  author    = {Daneker, Mitchell and Zhang, Zhen and Karniadakis, George Em and Lu, Lu},
  editor    = {Nguyen, Lan K.},
  bookTitle = {Computational Modeling of Signaling Networks},
  year      = {2023},
  publisher = {Springer US},
  pages     = {87--105},
  doi       = {10.1007/978-1-0716-3008-2_4}
}

Questions

To get help on how to use the data or code, simply open an issue in the GitHub "Issues" section.