Proteins physically interact with each other in physiological conditions. Individual protein interactions can be direct physical binding or membership within a multiprotein complex, and can be either permanent or transient . It is believed that the diversity of protein-protein interactions (PPI) contribute to the genetic complexity of organisms [2, 3]. Thanks to the development of high throughput technology, human PPI data has been greatly accumulated, which provides an opportunity to study that network systematically.
One important question to ask is, "How did the human PPI network emerge and evolve?" Given that the most significant property of the network is that the degree distribution follows a power law , several evolutionary models have been proposed to account for this attribute. These include the preferential attachment model, which asserts that a new protein is more likely to interact with well-connected nodes [5, 6], and the duplication-divergence model, which emulates gene duplication and the subsequent loss of inherited interactions [7–9]. Both models successfully reproduce the power law degree distribution. Researchers, however, found that the exponent of the degree distribution generated by the preferential attachment model is higher than that from the empirical network  and, more importantly, the preferential attachment model fails to reproduce the modularity structure that is observed in most biological networks . Alternatively, the proposed duplication-divergence model is more biologically motivated. With proper parameters, it can reproduce both the power law degree distribution and the modularity structure (through interaction rewiring and/or homomeric duplication, that is, duplication of self-interacting nodes); hence it receives extensive attention as a better candidate mechanism [12, 13]. Except studying pure topology, Kim  recently found that proteins of close age tend to interact with each other in yeast and proposed a new stochastic model which grows the network analogous to the process of growing protein crystals in solution. The authors claimed that the new model better explains many features of PPI networks. Although increasing features of empirical PPI networks had been captured by current models, all these stochastic models proposed do not require the intervention of natural selection to reproduce the intended topology, nor does it use biological function as a parameter.
On the other hand, people have realized that network structure is relevant for biological function [15, 16]. Many efforts have been made to find a relationship between network topology and functional and/or evolutionary properties. It has been reported that interacting proteins tend to be co-evolving , co-functional  and co-expressed [19, 20]. Highly interacting nodes in the network are generally more evolutionarily conserved  and tend to be essential and disease causing [4, 22]. Based on this information, the PPI network has been successfully used for predicting or prioritizing candidate genes of interest [23–26]. Given that, however, systematic functional analysis of PPI networks is still lacking. Using different datasets and techniques, Yook and Pandey both found correlation between the functional roles and topological structure, indicating that PPI networks are functionally organized [27, 28]. In a separate study, by comparing changes of interaction degree in functional classes and the time of origin of proteins, as well as functional heterogeneity at the time of origin, Kunin suggested that functional evolution might be the underlying reason for observed PPI network topological evolution . That study, however did not show in detail how function evolves, nor its relationship with the evolution of network topologies. In opposition to these findings, Wang et al., by breaking down a PPI network into structure modules, found that the network is not functionally organized at the modular level and suggested it evolves neutrally . Whether a PPI network is functionally organized and whether that organization's implication in the evolution of PPI networks is currently inconclusive.
Because functionality is an important aspect of molecular evolution, it is important to clearly address this question in order to have a better understanding of how the PPI network evolves. In this paper, we examine the evolution of a PPI network by dividing human proteins into temporal groups using known phylogenetic information. After doing this, we were able to track the evolutionary changes of the human PPI network at both the topological and functional level. We show the human PPI network functionally organized. In addition, we find that the topological and functional evolution of the human PPI network are not independent of each other. Function affects network topology during evolution, especially on local modularity. This is further supported by the finding that the topological unit is also the functional unit of the human PPI network. Based on our observations, we suggest that an extended model be developed that considers functional significance.