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.

翻译：在生物研究中,经常使用计算系统和方法,包括对癌症的理解和治疗的发展。肿瘤生长的模拟及其对不同药物的反应特别重要,但也具有挑战性的复杂性。主要的挑战首先是校准模拟器以便复制真实世界案例,其次是寻找有效药物治疗参数空间的具体值。在这项工作中,我们结合了肿瘤细胞生长的多尺度模拟器和遗传阿尔戈里希姆(GA)作为在合理时间内寻找良好参数配置的超理论搜索方法。两个模块被整合到一个单一工作流程中,可以同时在高性能计算基础设施上执行。实际上,GA用来校准模拟器,然后探索不同的药物交付计划。在这些计划中,我们的目标是找到那些尽量减少肿瘤细胞大小和今后出现抗药细胞的可能性的方法。实验结果说明了这种方法的有效性和计算效率。