Adjoint method for a tumor invasion PDE-constrained optimization problem using FEM

TL;DR

This paper develops an adjoint-based finite element method to estimate parameters in a nonlinear PDE model of cancer invasion, integrating experimental data to improve understanding of tumor microenvironment interactions.

Contribution

It introduces an adjoint method combined with FEM for parameter estimation in a complex reaction-diffusion cancer model, validated with real experimental data.

Findings

Successful parameter estimation aligning with experimental data

Enhanced understanding of tumor invasion dynamics

Validated numerical approach for biological modeling

Abstract

In this paper we present a method for estimating unknown parameter that appear on a non-linear reaction-diffusion model of cancer invasion. This model considers that tumor-induced alteration of micro-enviromental pH provides a mechanism for cancer invasion. A coupled system reaction-diffusion describing this model is given by three partial differential equations for the non dimensional spatial distribution and temporal evolution of the density of normal tissue, the neoplastic tissue growth and the excess concentration of H$^+$ ions. Each of the model parameters has a corresponding biological interpretation, for instance, the growth rate of neoplastic tissue, the diffusion coefficient, the reabsorption rate and the destructive influence of H$^+$ ions in the healthy tissue. After solving the forward problem properly, we use the model for the estimation of parameters by fitting the numerical solution with real data, obtained via in vitro experiments and fluorescence ratio imaging microscopy. We define an appropriate functional to compare both the real data and the numerical solution using the adjoint method for the minimization of this functional. We apply Finite Element Method (FEM) to solve both the direct and inverse problem, computing the \emph{a posteriori} error.