This repository contains several Jupyter notebooks with Julia code that are associated with "Parameter identifiability, parameter estimation and model prediction for differential equation models" by Matthew J Simpson and Ruth E Baker.
These worksheets were presented as a minitutorial at the 2024 SIAM Life Sciences Conference in Portland, Oregon.
ODE.ipynb: Jupyter notebook containing Jula code for the calculations in Section 2: Modelling with ODEs.
PDE_Additive.ipynb: Jupyter notebook containing Jula code for the calculations in Section 3 with addivie Gaussian noise: Modelling with PDEs.
PDE_Lognormal.ipynb: Jupyter notebook containing Jula code for the calculations in Section 3 with multiplicative log normal noise: Modelling with PDEs.
BVP.ipynb: Jupyter notebook containing Jula code for the calculations in Section 4 with the standard model parameterization: Modelling with a BVP: Dealing with non-identifiability.
BVP_Rescaled.ipynb: Jupyter notebook containing Jula code for the calculations in Section 4 with the re-scaled model parameterization: Modelling with a BVP: Dealing with non-identifiability.
These codes were written in Julia within the Jupyter notebook format. Notebooks within the Python folder are analagous codes that have been re-written in python. We suggest working with the Julia codes in the first instance.
A summary write-up of the results is available on the arXiv at https://arxiv.org/abs/2405.08177 This preprint has been revised for publication in SIAM Review after an initial round of referee reports (February, 2025).