  Cellular functions are based on the complex interplay of proteins, therefore
the structure and dynamics of these protein-protein interaction (PPI) networks
are the key to the functional understanding of cells. In the last years,
large-scale PPI networks of several model organisms were investigated.
Methodological improvements now allow the analysis of PPI networks of multiple
organisms simultaneously as well as the direct modeling of ancestral networks.
This provides the opportunity to challenge existing assumptions on network
evolution. We utilized present-day PPI networks from integrated datasets of
seven model organisms and developed a theoretical and bioinformatic framework
for studying the evolutionary dynamics of PPI networks. A novel filtering
approach using percolation analysis was developed to remove low confidence
interactions based on topological constraints. We then reconstructed the
ancient PPI networks of different ancestors, for which the ancestral proteomes,
as well as the ancestral interactions, were inferred. Ancestral proteins were
reconstructed using orthologous groups on different evolutionary levels. A
stochastic approach, using the duplication-divergence model, was developed for
estimating the probabilities of ancient interactions from today's PPI networks.
The growth rates for nodes, edges, sizes and modularities of the networks
indicate multiplicative growth and are consistent with the results from
independent static analysis. Our results support the duplication-divergence
model of evolution and indicate fractality and multiplicative growth as general
properties of the PPI network structure and dynamics.
