Scale free networks by preferential depletion

We show that not only preferential attachment but also preferential depletion leads to scale-free networks. The resulting degree distribution exponents is typically less than two (5/3) as opposed to the case of the growth models studied before where the exponents are larger. Our approach applies in particular to biological networks where in fact we find interesting agreement with experimental measurements. We investigate the most important properties characterizing these networks, as the cluster size distribution, the average shortest path and the clustering coefficient.Comment: 8 pages, 4 figure

Fanny Copeland and the geographical imagination

Raised in Scotland, married and divorced in the English south, an adopted Slovene, Fanny Copeland (1872 – 1970) occupied the intersection of a number of complex spatial and temporal conjunctures. A Slavophile, she played a part in the formation of what subsequently became the Kingdom of Yugoslavia that emerged from the First World War. Living in Ljubljana, she facilitated the first ‘foreign visit’ (in 1932) of the newly formed Le Play Society (a precursor of the Institute of British Geographers) and guided its studies of Solčava (a then ‘remote’ Alpine valley system) which, led by Dudley Stamp and commended by Halford Mackinder, were subsequently hailed as a model for regional studies elsewhere. Arrested by the Gestapo and interned in Italy during the Second World War, she eventually returned to a socialist Yugoslavia, a celebrated figure. An accomplished musician, linguist, and mountaineer, she became an authority on (and populist for) the Julian Alps and was instrumental in the establishment of the Triglav National Park. Copeland’s role as participant observer (and protagonist) enriches our understanding of the particularities of her time and place and illuminates some inter-war relationships within G/geography, inside and outside the academy, suggesting their relative autonomy in the production of geographical knowledge

Link prediction in complex networks: a local na\"{\i}ve Bayes model

Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions, and propose a local na\"{\i}ve Bayes model accordingly. Extensive experiments were carried out on eight real networks. Compared with the common-neighbor-based methods, the present method can provide more accurate predictions. Finally, we gave a detailed case study on the US air transportation network.Comment: 6 pages, 2 figures, 2 table

Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are a superfamily of seven transmembrane-spanning proteins involved in a wide array of physiological functions and are the most common targets of pharmaceuticals. This study aims to identify a cohort or clique of positions that share high mutual information. Using a multiple sequence alignment of the transmembrane (TM) domains, we calculated the mutual information between all inter-TM pairs of aligned positions and ranked the pairs by mutual information. A mutual information graph was constructed with vertices that corresponded to TM positions and edges between vertices were drawn if the mutual information exceeded a threshold of statistical significance. Positions with high degree (i.e. had significant mutual information with a large number of other positions) were found to line a well defined inter-TM ligand binding cavity for class A as well as class C GPCRs. Although the natural ligands of class C receptors bind to their extracellular N-terminal domains, the possibility of modulating their activity through ligands that bind to their helical bundle has been reported. Such positions were not found for class B GPCRs, in agreement with the observation that there are not known ligands that bind within their TM helical bundle. All identified key positions formed a clique within the MI graph of interest. For a subset of class A receptors we also considered the alignment of a portion of the second extracellular loop, and found that the two positions adjacent to the conserved Cys that bridges the loop with the TM3 qualified as key positions. Our algorithm may be useful for localizing topologically conserved regions in other protein families

Unraveling adaptive evolution: How a single point mutation affects the protein coregulation network

Understanding the mechanisms of evolution requires identification of the molecular basis of the multiple (pleiotropic) effects of specific adaptive mutations. We have characterized the pleiotropic effects on protein levels of an adaptive single-base pair substitution in the coding sequence of a signaling pathway gene in the bacterium Pseudomonas fluorescens SBW25. We find 52 proteomic changes, corresponding to 46 identified proteins. None of these proteins is required for the adaptive phenotype. Instead, many are found within specific metabolic pathways associated with fitness-reducing (that is, antagonistic) effects of the mutation. The affected proteins fall within a single coregulatory network. The mutation 'rewires' this network by drawing particular proteins into tighter coregulating relationships. Although these changes are specific to the mutation studied, the quantitatively altered proteins are also affected in a coordinated way in other examples of evolution to the same niche