A novel interactive tool, to simulate and visualize genetic networks, is discussed in this very well-written and clear paper. The authors carefully provide a background on genetic networks, so readers, even if they are amateurs, can not only get familiarized with the terminology, but can also get a basic understanding of how proteins regulate the formation of genetic networks, and of the inter-dependencies in the dynamics within these networks. The discussion flows very systematically, from presenting the objective, to understanding the background, and, finally, to specifics of the gene-protein simulation environment.

A key feature of the simulation tool is the authors’ handling of the complications involved in designing the simulation environment. They originally started with a tree-like representation, only to realize that it was imposing a structure on the process, and not truly reflecting actual interaction patterns between genes and proteins. This was rectified by assuming the chromosome to be centered in a two-dimensional (2D) grid, with eight possible directions for random movement within the grid. This symbolic representation of the simulation environment not only eliminated the imposed structure, but also ensured that activation of a gene was triggered only on contact by a protein, as opposed to all genes being turned on at the same time, which was the apparent case in the tree-like structure.

The simulation environment facilitates several modes of visualization, as well as concurrent viewing and transitioning between the different modes using three types of lenses: fuzzy lenses, a base-pair lens, and a ring lens. (There is, unfortunately, no introductory paragraph that explains the role of the lenses in the simulation environment, prior to discussing them.) A provision for animating the path of a gene is also available.

The paper has a few negatives. The section on related work was included just before conclusions, which is generally not the norm. But, this seems appropriate, considering the nature of the research presented. It might have been difficult to understand this information in context if it were included in the beginning. There is also a typographical error in equation (1): “Number of expressed proteins = BA (1 + (AF x AB) + (IF x IB)).” In addition, there is no indication if the grid size can be changed at any time during the simulation.

Overall, this is a well-written, comprehensive paper, and it will be interesting to see the implementation of the simulation tool in future research.