Parallel Model Exploration for Tumor Treatment Simulations

TL;DR

This paper presents a parallel computational approach combining a multi-scale tumor growth simulator with a genetic algorithm to efficiently calibrate models and explore optimal drug treatment strategies, reducing tumor size and resistance.

Contribution

It introduces an integrated workflow that leverages high-performance computing to calibrate tumor models and optimize drug delivery schemes using genetic algorithms.

Findings

The approach effectively calibrates tumor growth simulators.

It identifies drug schemes that reduce tumor size.

It minimizes the emergence of drug-resistant cells.

Abstract

Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a Genetic Algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.