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Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching.

ABSTRACT: We present two different methods to estimate parameters within a partial differential equation 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; 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.

SUBMITTER: Xiao Y

PROVIDER: S-EPMC8206694 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching.

Xiao Yunchen Y Thomas Len L Chaplain Mark A J MAJ

Royal Society open science 20210616 6

We present two different methods to estimate parameters within a partial differential equation 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; the second is a two-stage gradient matching method based on smoothing the data with a ...[more]

PMID: 34150312

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