LibreGrowth: A tumor growth code based on reaction-diffusion equations using shared memory

Abstract

In recent years, in-silico experimentation within the field of oncological medicine has been intensively investigated with the aim of better understanding tumor dynamics and dose–response relationships in cancer treatments. In a series of previous works, Luján et al. (2018, 2017, 2016)we described the micro-environmental influence on micro-tumor infiltration patterns through in-silico/in-vitro experimentation. Here we present the latest version of the software utilized for, but not limited to, those studies: LibreGrowth, a libre tumor growth code able to simulate the core growth and peripheral tumor cell infiltration, considering a benign and a malignant stages. We implemented a reaction–diffusion based model, with spatially variable diffusion coefficient, into a three-dimensional domain, using C++ and OpenMP over a GNU/Linux system. LibreGrowth aims to provide a flexible implementation for depicting heterogeneous tissues and infiltration processes, and to shed light in current therapy optimization strategies. Program summary: Program Title: LibreGrowth Program Files doi: http://dx.doi.org/10.17632/zp2my52xpv.1 Licensing provisions: GPLv3 Programming language: C++, OpenMP Supplementary material: Nature of problem: In the field of computational oncology, mathematical models based on reaction–diffusion equations describing tumor proliferation and invasion into peripheral host tissue have proved to be of clinical relevance. In-silico/in-vitro experimentation could help in the design of new strategies able to predict as much as possible the invasive behavior of a tumor, based on its particular properties and the bio-physicochemical characteristics of its microenvironment. Solution method: We introduced LibreGrowth, the latest version of the codes used in our previous studies to model the growth and infiltration of a tumor. It implements mentioned reaction–diffusion model through the standard finite difference method, using C++ and OpenMP parallelization technology.