  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.
