Calibrating models of cancer invasion : parameter estimation using approximate Bayesian computation and gradient matching
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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.
Xiao , Y , Thomas , L & Chaplain , M A J 2021 , ' Calibrating models of cancer invasion : parameter estimation using approximate Bayesian computation and gradient matching ' , Royal Society Open Science , vol. 8 , no. 6 , 202237 . https://doi.org/10.1098/rsos.202237
Royal Society Open Science
Copyright © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
DescriptionFunding: Y.X. is funded by a Doctoral Training Partnership grant from the Engineering and Physical Sciences ResearchCouncil (EPSRC) and a University of St Andrews St Leonard’s International Fee Scholarship.
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