Computational systems and methods are being applied to solve biological problems for many years. Incorporating methods of this kind in the research for cancer treatment and related drug discovery in particular, is shown to be challenging due to the complexity and the dynamic nature of the related factors. Usually, there are two objectives in such settings; 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. 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 a parallel manner on high performance computing infrastructures, since large-scale computational and storage capabilities are necessary in this domain. After using the GA for calibration, our goal is 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. Results from experiments on high performance computing infrastructure illustrate the effectiveness and timeliness of the approach.
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