Characterization and modeling of protein interaction networks dynamics

Cell organization is governed and maintained via specific interactions between its constituent macromolecules. In this thesis I investigate the role of the topological characteristics of protein interaction networks in promoting cell organization. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks – namely non random degree distribution and high clustering coefficient – are shared by networks of distantly related organisms. I have used ProtNet, a stochastic model representing a computer stylized cell to ask questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks. By using a novel metric of cell organization, I show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the composition of the ensemble of protein complexes that form in pseudo-cells, that self organize under the interaction rules of natural networks, are more robust to perturbations. This analysis carried out by using networks with a variety of topological characteristics led me to conclude that self organization is a consequence of the high clustering coefficient, while the scale free degree distribution is probably a relic of the evolutionary processes that lead to interactome evolution and has little functional relevance.