The Heckscher-Ohlin Model and the Network Structure of International Trade

This paper estimates for 28 product groups a characteristic parameter that reflects the topological structure of its trading network. Using these estimates, it then describes how the structure of international trade has evolved during the 1980-2000 period. Thereafter, it demonstrates the importance of networks in international trade by explicitly accounting for their scaling properties when testing the prediction of the Heckscher-Ohlin model that factor endowment differentials determine bilateral trade flows. The results suggest that differences in factor endowments increase bilateral trade in goods that are traded in "dispersed" networks. For goods that are traded in "concentrated" networks, factor endowment differentials are less important.Networks, international trade, gravity model

Which Sectors of a Modern Economy are most Central?

We analyze input-output matrices for a wide set of countries as weighted directed networks. These graphs contain only 47 nodes, but they are almost fully connected and many have nodes with strong self-loops. We apply two measures: random walk centrality and one based on count-betweenness. Our findings are intuitive. For example, in Luxembourg the most central sector is “Finance and Insurance” and the analog in Germany is “Wholesale and Retail Trade” or “Motor Vehicles”, according to the measure. Rankings of sectoral centrality vary by country. Some sectors are often highly central, while others never are. Hierarchical clustering reveals geographical proximity and similar development status.

The Heckscher-Ohlin Model and the Network Structure of International Trade

This paper estimates for 28 product groups a characteristic parameter that reflects the topological structure of its trading network. Using these estimates, it then describes how the structure of international trade has evolved during the 1980-2000 period. Thereafter, it demonstrates the importance of networks in international trade by explicitly accounting for their scaling properties when testing the prediction of the Heckscher-Ohlin model that factor endowment differentials determine bilateral trade flows. The results suggest that differences in factor endowments increase bilateral trade in goods that are traded in "dispersed" networks. For goods that are traded in "concentrated" networks, factor endowment differentials are less important

The Heckscher-Ohlin Model and the Network Structure of International Trade

This paper estimates for 28 product groups a characteristic parameter that reflects the topological structure of its trading network. Using these estimates, it then describes how the structure of international trade has evolved during the 1980-2000 period. Thereafter, it demonstrates the importance of networks in international trade by explicitly accounting for their scaling properties when testing the prediction of the Heckscher-Ohlin model that factor endowment differentials determine bilateral trade flows. The results suggest that differences in factor endowments increase bilateral trade in goods that are traded in "dispersed" networks. For goods that are traded in "concentrated" networks, factor endowment differentials are less important

An evolutionary and structural characterization of mammalian protein complex organization

Background: We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes. Results: As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tend to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins. Conclusions: We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes

The Heckscher-Ohlin Model and the Network Structure of International Trade

This paper estimates for 28 product groups a characteristic parameter that reflects the topological structure of its trading network. Using these estimates, it then describes how the structure of international trade has evolved during the 1980-2000 period. Thereafter, it demonstrates the importance of networks in international trade by explicitly accounting for their scaling properties when testing the prediction of the Heckscher-Ohlin model that factor endowment differentials determine bilateral trade flows. The results suggest that differences in factor endowments increase bilateral trade in goods that are traded in "dispersed" networks. For goods that are traded in "concentrated" networks, factor endowment differentials are less important

Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation

<p>Abstract</p> <p>Background</p> <p>External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analyses based on clustering or gene set enrichment only partly reveal this information, matrix factorization techniques are well suited for a detailed temporal analysis. In signal processing, factorization techniques incorporating data properties like spatial and temporal correlation structure have shown to be robust and computationally efficient. However, such correlation-based methods have so far not be applied in bioinformatics, because large scale biological data rarely imply a natural order that allows the definition of a delayed correlation function.</p> <p>Results</p> <p>We therefore develop the concept of graph-decorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways in a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the features (e.g. genes) and are thus able to define a graph-delayed correlation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph-decorrelation algorithm (GraDe). To analyze alterations in the gene response in <it>IL-6 </it>stimulated primary mouse hepatocytes, we performed a time-course microarray experiment and applied GraDe. In contrast to standard techniques, the extracted time-resolved gene expression profiles showed that <it>IL-6 </it>activates genes involved in cell cycle progression and cell division. Genes linked to metabolic and apoptotic processes are down-regulated indicating that <it>IL-6 </it>mediated priming renders hepatocytes more responsive towards cell proliferation and reduces expenditures for the energy metabolism.</p> <p>Conclusions</p> <p>GraDe provides a novel framework for the decomposition of large-scale 'omics' data. We were able to show that including prior knowledge into the separation task leads to a much more structured and detailed separation of the time-dependent responses upon <it>IL-6 </it>stimulation compared to standard methods. A Matlab implementation of the GraDe algorithm is freely available at <url>http://cmb.helmholtz-muenchen.de/grade</url>.</p

Diversity and activity of sugar transporters in nematode-induced root syncytia

The plant-parasitic nematode Heterodera schachtii stimulates plant root cells to form syncytial feeding structures which synthesize all nutrients required for successful nematode development. Cellular re-arrangements and modified metabolism of the syncytia are accompanied by massive intra- and intercellular solute allocations. In this study the expression of all genes annotated as sugar transporters in the Arabidopsis Membrane Protein Library was investigated by Affymetrix gene chip analysis in young and fully developed syncytia compared with non-infected Arabidopsis thaliana roots. The expression of three highly up-regulated (STP12, MEX1, and GTP2) and three highly down-regulated genes (SFP1, STP7, and STP4) was analysed by quantitative RT-PCR (qRT-PCR). The most up-regulated gene (STP12) was chosen for further in-depth studies using in situ RT-PCR and a nematode development assay with a T-DNA insertion line revealing a significant reduction of male nematode development. The specific role of STP12 expression in syncytia of male juveniles compared with those of female juveniles was further shown by qRT-PCR. In order to provide evidence for sugar transporter activity across the plasma membrane of syncytia, fluorescence-labelled glucose was used and membrane potential recordings following the application of several sugars were performed. Analyses of soluble sugar pools revealed a highly specific composition in syncytia. The presented work demonstrates that sugar transporters are specifically expressed and active in syncytia, indicating a profound role in inter- and intracelluar transport processes