Cancer research now involves the use of interdisciplinary science to find optimal solutions to some of its associated problems. Different modeling approaches could be used to identify patterns in the growth of cancer cells.

In this research, the authors use cellular automata. The authors first introduce the problem of cancer cell growth and regrowth, and describe cellular automata. Then, they argue that cellular automata should be effective in describing various forms of cancers and their responses to various therapies.

The authors have used event model simulation as a method to validate their claims. The methods section provides information on the use of this method. A good explanation of mitosis tests is also given in this section. An algorithm for differential evolution is provided as well.

One of the hallmarks of this paper is the description of the results using clear diagrams. Three clear diagrams describe the results. These figures focus on accumulative treatment intensity and multicellular system evolutions. Overall, this is a very well-written paper and will certainly be useful in advancing the science of cancer diagnostics.