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All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching

Citation

Xiao, Yunchen; Thomas, Len; Chaplain, Mark (2021), All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching, Dryad, Dataset, https://doi.org/10.5061/dryad.mkkwh70z2

Abstract

We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data.  To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.

Methods

All related data presented in this repository regarding the manuscript Xiao et al. were simulated using R and MATLAB. The detailed methods of applying the two schemes can be found in the README files in the repository and the comments in each MATLAB or R file. 

Usage Notes

The primary repository can be found directly on Github: https://github.com/ycx12341/Data-Code-Figures-ver-4. Updates to the datasets will first be made to this primary repository. 

Funding

Engineering and Physical Sciences Research Council

St Leonard International Fee Scholarship

St Leonard International Fee Scholarship