Understanding complex networks in the real world is a nontrivial task. In the study of community structures we normally encounter several examples of these networks, which makes any statistical inferencing a challenging endeavor. Researchers resort to computer-generated networks that resemble networks encountered in the real world as a means to generate many networks with different sizes, while maintaining the real-world characteristics of interest. The generation of networks that resemble the real world turns out in itself to be a complex search problem. We present a new rewiring algorithm for the generation of networks with unique characteristics that combine the scale-free effects and community structures encountered in the real world. The algorithm is inspired by social interactions in the real world, whereby people tend to connect locally while occasionally they connect globally. This local-global coupling turns out to be a powerful characteristics that is required for our proposed rewiring algorithm to generate networks with community structures, power law distributions both in degree and in community size, positive assortative mixing by degree, and the rich-club phenomenon.

Biomolecular studies point increasingly to the importance of modularity in the organization of the genome. Processes such as the maintenance of metabolism are controlled by suites of genes that act as distinct, self-contained units, or modules . One effect is to promote stability of inherited characters. Despite the obvious importance of genetic modules, the mechanisms by which they form and persist are not understood. One clue is that functionally related genes tend to cluster together. Here we show that genetic translocation, recombination, and natural selection play a central role in this process. We distill the question of emerging genetic modularity into three simulation experiments that show: (1) a tendency, under natural selection, for essential genes to co-locate on the same chromosome and to settle in fixed loci; (2) that genes associated with a particular function tend to form functional clusters; and (3) that genes within a functional cluster tend to become arranged in transcription order. The results also imply that high proportions of junk DNA are essential to the process.

This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated.