  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.

Quantitative Methods;Distributed, Parallel, and Cluster Computing;Tissues and Organs