Abstract
The objective of this work was to estimate parameters of a model for diode-laser heating of a culture of cancer cells under the effects of a chemotherapy drug. Two mathematical models were proposed to represent the physical problem during heating: natural convection was considered in the high-fidelity model, while the low-fidelity model was given by a lumped system. The thermal damage caused in the cells by the heating was modeled as a first-order reaction. A Bayesian approach was applied to estimate model parameters with the Markov Chain Monte Carlo (MCMC) method, which was implemented with the Metropolis-Hastings algorithm. The Approximation Error Model (AEM) approach was used to speed up calculations for the inverse problem solution when the high-fidelity model was replaced by the low-fidelity model for the computation of dependent variables. Monte Carlo direct simulations were also performed to compute the transient variation of the number of cells during periods before and after the imposed heating.
Disclosure statement
There are no relationships of all authors with any people or organizations that could inappropriately influence (bias) this work.