The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.

For mitochondrial phylogenetic analysis, the best result comes from complete sequences. We therefore decided to sequence the entire mitochondrial DNA (mtDNA) (coding and D-loop regions) of 63 individuals selected in 3 small Ogliastra villages, an isolated area of eastern Sardinia: Talana, Urzulei, and Perdasdefogu. We studied at least one individual for each of the most frequent maternal genealogical lineages belonging to haplogroups H, V, J, K, T, U, and X. We found in our 63 samples, 172 and 69 sequence changes in the coding and in the D-loop region, respectively. Thirteen out of 172 sequence changes in the coding region are novel. It is our hypothesis that some of them are characteristic of the Ogliastra region and/or Sardinia. We reconstructed the phylogenetic network of the 63 complete mtDNA sequences for the 3 villages. We also drew a network including a large number of European sequences and calculated various indices of genetic diversity in Ogliastra. It appears that these small populations remained extremely isolated and genetically differentiated compared with other European populations. We also identified in our samples a never previously described subhaplogroup, U5b3, which seems peculiar to the Ogliastra region.

Background: Due to the strict relation between protein function and structure, the prediction of protein 3D-structure has become one of the most important tasks in bioinformatics and proteomics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences and known tertiary structures is constantly increasing. The need for automatic methods has brought the development of several prediction and modelling tools, but a general methodology able to solve the problem has not yet been devised, and most methodologies concentrate on the simplified task of predicting secondary structure.

We describe several algorithms with winning performance in the Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Reverse Engineering Competition 2007. After the gold standards for the challenges were released, the performance of the algorithms could be thoroughly evaluated under different parameters or alternative ways of solving systems of equations. For the analysis of Challenge 4, the "In-silico" challenges, we employed methods to explicitly deal with perturbation data and time-series data. We show that original methods used to produce winning submissions could easily be altered to substantially improve performance. For Challenge 5, the genome-scale Escherichia coli network, we evaluated a variety of measures of association. These data are troublesome, and no good solutions could be produced, either by us or by any other teams. Our best results were obtained when analyzing subdatasets instead of considering the dataset as a whole.

We present an investigation on the structural and dynamical properties of a ‘human protein signalling network’ (HPSN). This biological network is composed of nodes that correspond to proteins and directed edges that represent signal flows. In order to gain insight into the organization of cell information processing this network is analysed taking into account explicitly the edge directions. We explore the topological properties of the HPSN at the global and the local scale, further applying the generating function formalism to provide a suitable comparative model. The relationship between the node degrees and the distribution of signals through the network is characterized using degree correlation profiles. Finally, we analyse the dynamical properties of small sub-graphs showing high correlation between their occurrence and dynamic stability.