Ising model for distribution networks

An elementary Ising spin model is proposed for demonstrating cascading failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in realistic networks for distribution and delivery by suppliers to consumers. A ferromagnetic Hamiltonian with quenched random fields results from policies that maximize the gap between demand and delivery. Such policies can arise in a competitive market where firms artificially create new demand, or in a solidary environment where too high a demand cannot reasonably be met. Network failure in the context of a policy of solidarity is possible when an initially active state becomes metastable and decays to a stable inactive state. We explore the characteristics of the demand and delivery, as well as the topological properties, which make the distribution network susceptible of failure. An effective temperature is defined, which governs the strength of the activity fluctuations which can induce a collapse. Numerical results, obtained by Monte Carlo simulations of the model on (mainly) scale-free networks, are supplemented with analytic mean-field approximations to the geometrical random field fluctuations and the thermal spin fluctuations. The role of hubs versus poorly connected nodes in initiating the breakdown of network activity is illustrated and related to model parameters

Network analysis of human protein location

<p>Abstract</p> <p>Background</p> <p>Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology.</p> <p>Results</p> <p>Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ²) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ²= 1270.19, <it>P-value </it>< 2.2 × 10^-16), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ²= 110.02, <it>P-value </it>< 2.2 x10^-16). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (<it>P-value </it>< 6.0e-07), the lysosome (<it>P-value </it>< 4.7e-05) and the Golgi apparatus (<it>P-value </it>< 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments.</p> <p>Conclusions</p> <p>The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.</p

Combinations of newly confirmed Glioma-Associated loci link regions on chromosomes 1 and 9 to increased disease risk

<p>Abstract</p> <p>Background</p> <p>Glioblastoma multiforme (GBM) tends to occur between the ages of 45 and 70. This relatively early onset and its poor prognosis make the impact of GBM on public health far greater than would be suggested by its relatively low frequency. Tissue and blood samples have now been collected for a number of populations, and predisposing alleles have been sought by several different genome-wide association (GWA) studies. The Cancer Genome Atlas (TCGA) at NIH has also collected a considerable amount of data. Because of the low concordance between the results obtained using different populations, only 14 predisposing single nucleotide polymorphism (SNP) candidates in five genomic regions have been replicated in two or more studies. The purpose of this paper is to present an improved approach to biomarker identification.</p> <p>Methods</p> <p>Association analysis was performed with control of population stratifications using the EIGENSTRAT package, under the null hypothesis of "no association between GBM and control SNP genotypes," based on an additive inheritance model. Genes that are strongly correlated with identified SNPs were determined by linkage disequilibrium (LD) or expression quantitative trait locus (eQTL) analysis. A new approach that combines meta-analysis and pathway enrichment analysis identified additional genes.</p> <p>Results</p> <p>(i) A meta-analysis of SNP data from TCGA and the Adult Glioma Study identifies 12 predisposing SNP candidates, seven of which are reported for the first time. These SNPs fall in five genomic regions (5p15.33, 9p21.3, 1p21.2, 3q26.2 and 7p15.3), three of which have not been previously reported. (ii) 25 genes are strongly correlated with these 12 SNPs, eight of which are known to be cancer-associated. (iii) The relative risk for GBM is highest for risk allele combinations on chromosomes 1 and 9. (iv) A combined meta-analysis/pathway analysis identified an additional four genes. All of these have been identified as cancer-related, but have not been previously associated with glioma. (v) Some SNPs that do not occur reproducibly across populations are in reproducible (invariant) pathways, suggesting that they affect the same biological process, and that population discordance can be partially resolved by evaluating processes rather than genes.</p> <p>Conclusion</p> <p>We have uncovered 29 glioma-associated gene candidates; 12 of them known to be cancer related (<it>p </it>= 1. 4 × 10^-6), providing additional statistical support for the relevance of the new candidates. This additional information on risk loci is potentially important for identifying Caucasian individuals at risk for glioma, and for assessing relative risk.</p

A global view of drug-therapy interactions

Network science is already making an impact on the study of complex systems and offers a promising variety of tools to understand their formation and evolution (1-4) in many disparate fields from large communication networks (5,6), transportation infrastructures (7) and social communities (8,9) to biological systems (1,10,11). Even though new highthroughput technologies have rapidly been generating large amounts of genomic data, drug design has not followed the same development, and it is still complicated and expensive to develop new single-target drugs. Nevertheless, recent approaches suggest that multi-target drug design combined with a network-dependent approach and large-scale systems-oriented strategies (12-14) create a promising framework to combat complex multigenetic disorders like cancer or diabetes. Here, we investigate the human network corresponding to the interactions between all US approved drugs and human therapies, defined by known drug-therapy relationships. Our results show that the key paths in this network are shorter than three steps, indicating that distant therapies are separated by a surprisingly low number of chemical compounds. We also identify a sub-network composed by drugs with high centrality measures (15), which represent the structural back-bone of the drug-therapy system and act as hubs routing information between distant parts of the network. These findings provide for the first time a global map of the largescale organization of all known drugs and associated therapies, bringing new insights on possible strategies for future drug development. Special attention should be given to drugs which combine the two properties of (a) having a high centrality value and (b) acting on multiple targets.Comment: 16 pages, 4 figures. It was submitted to peer review on August 15, 200

A Molecular Epidemiological and Genetic Diversity Study of Tuberculosis in Ibadan, Nnewi and Abuja, Nigeria

Background Nigeria has the tenth highest burden of tuberculosis (TB) among the 22 TB high-burden countries in the world. This study describes the biodiversity and epidemiology of drug-susceptible and drug-resistant TB in Ibadan, Nnewi and Abuja, using 409 DNAs extracted from culture positive TB isolates. Methodology/Principal Findings DNAs extracted from clinical isolates of Mycobacterium tuberculosis complex were studied by spoligotyping and 24 VNTR typing. The Cameroon clade (CAM) was predominant followed by the M. africanum (West African 1) and T (mainly T2) clades. By using a smooth definition of clusters, 32 likely epi-linked clusters related to the Cameroon genotype family and 15 likely epi-linked clusters related to other “modern” genotypes were detected. Eight clusters concerned M. africanum West African 1. The recent transmission rate of TB was 38%. This large study shows that the recent transmission of TB in Nigeria is high, without major regional differences, with MDR-TB clusters. Improvement in the TB control programme is imperative to address the TB control problem in Nigeria

The Guinea-Bissau Family of Mycobacterium tuberculosis Complex Revisited

The Guinea-Bissau family of strains is a unique group of the Mycobacterium tuberculosis complex that, although genotypically closely related, phenotypically demonstrates considerable heterogeneity. We have investigated 414 M. tuberculosis complex strains collected in Guinea-Bissau between 1989 and 2008 in order to further characterize the Guinea-Bissau family of strains. To determine the strain lineages present in the study sample, binary outcomes of spoligotyping were compared with spoligotypes existing in the international database SITVIT2. The major circulating M. tuberculosis clades ranked in the following order: AFRI (n = 195, 47.10%), Latin-American-Mediterranean (LAM) (n = 75, 18.12%), ill-defined T clade (n = 53, 12.8%), Haarlem (n = 37, 8.85%), East-African-Indian (EAI) (n = 25, 6.04%), Unknown (n = 12, 2.87%), Beijing (n = 7, 1.68%), X clade (n = 4, 0.96%), Manu (n = 4, 0.97%), CAS (n = 2, 0.48%). Two strains of the LAM clade isolated in 2007 belonged to the Cameroon family (SIT61). All AFRI isolates except one belonged to the Guinea-Bissau family, i.e. they have an AFRI_1 spoligotype pattern, they have a distinct RFLP pattern with low numbers of IS6110 insertions, and they lack the regions of difference RD7, RD8, RD9 and RD10, RD701 and RD702. This profile classifies the Guinea-Bissau family, irrespective of phenotypic biovar, as part of the M. africanum West African 2 lineage, or the AFRI_1 sublineage according to the spoligtyping nomenclature. Guinea-Bissau family strains display a variation of biochemical traits classically used to differentiate M. tuberculosis from M. bovis. Yet, the differential expression of these biochemical traits was not related to any genes so far investigated (narGHJI and pncA). Guinea-Bissau has the highest prevalence of M. africanum recorded in the African continent, and the Guinea-Bissau family shows a high phylogeographical specificity for Western Africa, with Guinea-Bissau being the epicenter. Trends over time however indicate that this family of strains is waning in most parts of Western Africa, including Guinea-Bissau (p = 0.048)

Cells of the human intestinal tract mapped across space and time

The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. Here, to comprehensively map cell lineages, we use single-cell RNA sequencing and antigen receptor analysis of almost half a million cells from up to 5 anatomical regions in the developing and up to 11 distinct anatomical regions in the healthy paediatric and adult human gut. This reveals the existence of transcriptionally distinct BEST4 epithelial cells throughout the human intestinal tract. Furthermore, we implicate IgG sensing as a function of intestinal tuft cells. We describe neural cell populations in the developing enteric nervous system, and predict cell-type-specific expression of genes associated with Hirschsprung’s disease. Finally, using a systems approach, we identify key cell players that drive the formation of secondary lymphoid tissue in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. This catalogue of intestinal cells will provide new insights into cellular programs in development, homeostasis and disease

The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks

<p>Abstract</p> <p>Background</p> <p>Currently, there is a gap between purely theoretical studies of the topology of large bioregulatory networks and the practical traditions and interests of experimentalists. While the theoretical approaches emphasize the global characterization of regulatory systems, the practical approaches focus on the role of distinct molecules and genes in regulation. To bridge the gap between these opposite approaches, one needs to combine 'general' with 'particular' properties and translate abstract topological features of large systems into testable functional characteristics of individual components. Here, we propose a new topological parameter – the pairwise disconnectivity index of a network's element – that is capable of such bridging.</p> <p>Results</p> <p>The pairwise disconnectivity index quantifies how crucial an individual element is for sustaining the communication ability between connected pairs of vertices in a network that is displayed as a directed graph. Such an element might be a vertex (i.e., molecules, genes), an edge (i.e., reactions, interactions), as well as a group of vertices and/or edges. The index can be viewed as a measure of topological redundancy of regulatory paths which connect different parts of a given network and as a measure of sensitivity (robustness) of this network to the presence (absence) of each individual element. Accordingly, we introduce the notion of a path-degree of a vertex in terms of its corresponding incoming, outgoing and mediated paths, respectively. The pairwise disconnectivity index has been applied to the analysis of several regulatory networks from various organisms. The importance of an individual vertex or edge for the coherence of the network is determined by the particular position of the given element in the whole network.</p> <p>Conclusion</p> <p>Our approach enables to evaluate the effect of removing each element (i.e., vertex, edge, or their combinations) from a network. The greatest potential value of this approach is its ability to systematically analyze the role of every element, as well as groups of elements, in a regulatory network.</p

Factors associated with nursing home placement of all patients admitted for inpatient rehabilitation in Singapore community hospitals from 1996 to 2005: A disease stratified analysis

10.1371/journal.pone.0082697PLoS ONE812-POLN

A Single-Step Sequencing Method for the Identification of Mycobacterium tuberculosis Complex Species

The Mycobacterium tuberculosis complex (MTC) comprises several closely related species responsible for strictly human and zoonotic tuberculosis. Some of the species are restricted to Africa and were responsible for the high prevalence of tuberculosis. However, their identification at species level is difficult and expansive. Accurate species identification of all members is warranted in order to distinguish between strict human and zoonotic tuberculosis, to trace source exposure during epidemiological studies, and for the appropriate treatment of patients. In this paper, the Exact Tandem Repeat D (ETR-D) intergenic region was investigated in order to distinguish MTC species. The ETR-D sequencing unambiguously identified MTC species type strain except M. pinnipedii and M. microti, and the results agreed with phenotypic and molecular identification. This finding offers a new tool for the rapid and accurate identification of MTC species in a single sequencing reaction, replacing the current time-consuming polyphasic approach. Its use could assist public health interventions and aid in the control of zoonotic transmission in African countries, and could be of particular interest with the current emergence of multidrug-resistant and extended-resistance isolates