Parallel model exploration for tumor treatment simulations

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