@article{3063121,
    title = "Parallel model exploration for tumor treatment simulations",
    author = "Akasiadis, C. and Ponce-de-Leon, M. and Montagud, A. and Michelioudakis, E. and Atsidakou, A. and Alevizos, E. and Artikis, A. and Valencia, A. and Paliouras, G.",
    journal = "Studies in Computational Intelligence",
    year = "2022",
    publisher = "John Wiley and Sons Inc",
    doi = "10.1111/coin.12515",
    keywords = "Cell proliferation;  Computational efficiency;  Drug delivery;  Heuristic algorithms;  Heuristic methods;  Simulators;  Tumors, Biological research;  Computational system;  High performance computing;  Model exploration;  Parallel models;  Performance computing;  Treatment simulation;  Tumor growth;  Tumor treatment;  Tumour cells, Genetic algorithms",
    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. © 2022 Wiley Periodicals LLC."
}