Journal
COMPUTATIONAL INTELLIGENCE
Volume 38, Issue 4, Pages 1379-1401Publisher
WILEY
DOI: 10.1111/coin.12515
Keywords
calibration; drug treatment; genetic algorithm; high performance computing; model exploration
Categories
Funding
- EU [825070]
Ask authors/readers for more resources
Computational systems and methods play an important role in biological research, particularly in the understanding and treatment of cancer. This study combines a simulator for tumor cell growth and a genetic algorithm to find effective parameter configurations for drug treatments. Experimental results demonstrate the effectiveness and computational efficiency of the approach.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available