In order to generate artificial scale-free networks, we used two generation models: the preferential attachment model, and the duplication model.
In the duplication model the duplication of the information is considered as a dominant evolutionary force for the growth of a network, such as in many biological networks.
Thus, like nearly every other genome rearrangement model, the duplication distance model makes some simplifying assumptions about the underlying biology to achieve computational tractability.
Under the better-fit model, the duplication rate in Sternorrhyncha increased ~11X above the background rate of the phylogeny for the AAAP family (Table 3).
One model, the duplication-degeneration-complementation model (DDC) describing the fate of duplicates was proposed by Force et al. [ 19] and illustrated in the works of van Hoof [ 24].
Compared to the preferential attachment model, the duplication-divergence model may be more promising.
This general model manages the duplication of units in series to perform a given operation in the process, which is an alternative that has not been considered in previous general approaches.
Although it is possible to make the general distribution of subgraphs in an artificial model (more specifically the duplication model) very similar to that of a specific PPI network, there are a number of subgraphs, for example, in the Yeast PPI network, which occur much more frequently than that in the associated artificial model.
The arrangement of the Platysternon genome can be modelled by the "duplication-random loss" model [ 26] whereby a duplication and transposition of part of the genome occurred, then additional rearrangements resulted from the loss of supernumerary genes.
Orthologs, paralogs, and xenologs can be examined simultaneously using complex birth/death models that characterize the duplication and lateral transfer processes together with sequence evolution (15- 16).
