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Sample records for integrated protein network

  1. Predicting Protein Function via Semantic Integration of Multiple Networks.

    Science.gov (United States)

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

  2. Unveiling network-based functional features through integration of gene expression into protein networks.

    Science.gov (United States)

    Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

    2018-06-01

    Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

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    Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

    2018-06-14

    Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Protein complex detection in PPI networks based on data integration and supervised learning method.

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    Yu, Feng; Yang, Zhi; Hu, Xiao; Sun, Yuan; Lin, Hong; Wang, Jian

    2015-01-01

    Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection. The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks. The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively. The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

  5. Semantic integration to identify overlapping functional modules in protein interaction networks

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    Ramanathan Murali

    2007-07-01

    Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

  6. Dynamic changes in protein functional linkage networks revealed by integration with gene expression data.

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    Shubhada R Hegde

    2008-11-01

    Full Text Available Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein:protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein:protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.

  7. Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

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    Xianjun Shen

    Full Text Available How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment. It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

  8. Integrative Identification of Arabidopsis Mitochondrial Proteome and Its Function Exploitation through Protein Interaction Network

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    Cui, Jian; Liu, Jinghua; Li, Yuhua; Shi, Tieliu

    2011-01-01

    Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome. PMID:21297957

  9. Dynamic Proteomic Characteristics and Network Integration Revealing Key Proteins for Two Kernel Tissue Developments in Popcorn.

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    Yongbin Dong

    Full Text Available The formation and development of maize kernel is a complex dynamic physiological and biochemical process that involves the temporal and spatial expression of many proteins and the regulation of metabolic pathways. In this study, the protein profiles of the endosperm and pericarp at three important developmental stages were analyzed by isobaric tags for relative and absolute quantification (iTRAQ labeling coupled with LC-MS/MS in popcorn inbred N04. Comparative quantitative proteomic analyses among developmental stages and between tissues were performed, and the protein networks were integrated. A total of 6,876 proteins were identified, of which 1,396 were nonredundant. Specific proteins and different expression patterns were observed across developmental stages and tissues. The functional annotation of the identified proteins revealed the importance of metabolic and cellular processes, and binding and catalytic activities for the development of the tissues. The whole, endosperm-specific and pericarp-specific protein networks integrated 125, 9 and 77 proteins, respectively, which were involved in 54 KEGG pathways and reflected their complex metabolic interactions. Confirmation for the iTRAQ endosperm proteins by two-dimensional gel electrophoresis showed that 44.44% proteins were commonly found. However, the concordance between mRNA level and the protein abundance varied across different proteins, stages, tissues and inbred lines, according to the gene cloning and expression analyses of four relevant proteins with important functions and different expression levels. But the result by western blot showed their same expression tendency for the four proteins as by iTRAQ. These results could provide new insights into the developmental mechanisms of endosperm and pericarp, and grain formation in maize.

  10. Gene Prioritization by Integrated Analysis of Protein Structural and Network Topological Properties for the Protein-Protein Interaction Network of Neurological Disorders

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    Yashna Paul

    2016-01-01

    Full Text Available Neurological disorders are known to show similar phenotypic manifestations like anxiety, depression, and cognitive impairment. There is a need to identify shared genetic markers and molecular pathways in these diseases, which lead to such comorbid conditions. Our study aims to prioritize novel genetic markers that might increase the susceptibility of patients affected with one neurological disorder to other diseases with similar manifestations. Identification of pathways involving common candidate markers will help in the development of improved diagnosis and treatments strategies for patients affected with neurological disorders. This systems biology study for the first time integratively uses 3D-structural protein interface descriptors and network topological properties that characterize proteins in a neurological protein interaction network, to aid the identification of genes that are previously not known to be shared between these diseases. Results of protein prioritization by machine learning have identified known as well as new genetic markers which might have direct or indirect involvement in several neurological disorders. Important gene hubs have also been identified that provide an evidence for shared molecular pathways in the neurological disease network.

  11. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

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    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Integration of relational and hierarchical network information for protein function prediction

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    Jiang Xiaoyu

    2008-08-01

    Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased

  13. Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks.

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    Grimes, Mark; Hall, Benjamin; Foltz, Lauren; Levy, Tyler; Rikova, Klarisa; Gaiser, Jeremiah; Cook, William; Smirnova, Ekaterina; Wheeler, Travis; Clark, Neil R; Lachmann, Alexander; Zhang, Bin; Hornbeck, Peter; Ma'ayan, Avi; Comb, Michael

    2018-05-22

    Protein posttranslational modifications (PTMs) have typically been studied independently, yet many proteins are modified by more than one PTM type, and cell signaling pathways somehow integrate this information. We coupled immunoprecipitation using PTM-specific antibodies with tandem mass tag (TMT) mass spectrometry to simultaneously examine phosphorylation, methylation, and acetylation in 45 lung cancer cell lines compared to normal lung tissue and to cell lines treated with anticancer drugs. This simultaneous, large-scale, integrative analysis of these PTMs using a cluster-filtered network (CFN) approach revealed that cell signaling pathways were outlined by clustering patterns in PTMs. We used the t-distributed stochastic neighbor embedding (t-SNE) method to identify PTM clusters and then integrated each with known protein-protein interactions (PPIs) to elucidate functional cell signaling pathways. The CFN identified known and previously unknown cell signaling pathways in lung cancer cells that were not present in normal lung epithelial tissue. In various proteins modified by more than one type of PTM, the incidence of those PTMs exhibited inverse relationships, suggesting that molecular exclusive "OR" gates determine a large number of signal transduction events. We also showed that the acetyltransferase EP300 appears to be a hub in the network of pathways involving different PTMs. In addition, the data shed light on the mechanism of action of geldanamycin, an HSP90 inhibitor. Together, the findings reveal that cell signaling pathways mediated by acetylation, methylation, and phosphorylation regulate the cytoskeleton, membrane traffic, and RNA binding protein-mediated control of gene expression. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  14. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

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    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  15. Integrating atomistic molecular dynamics simulations, experiments and network analysis to study protein dynamics: strength in unity

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    Elena ePapaleo

    2015-05-01

    Full Text Available In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

  16. Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics

    DEFF Research Database (Denmark)

    Papaleo, Elena

    2015-01-01

    that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome...... with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties...... simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations....

  17. Protein Inference from the Integration of Tandem MS Data and Interactome Networks.

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    Zhong, Jiancheng; Wang, Jianxing; Ding, Xiaojun; Zhang, Zhen; Li, Min; Wu, Fang-Xiang; Pan, Yi

    2017-01-01

    Since proteins are digested into a mixture of peptides in the preprocessing step of tandem mass spectrometry (MS), it is difficult to determine which specific protein a shared peptide belongs to. In recent studies, besides tandem MS data and peptide identification information, some other information is exploited to infer proteins. Different from the methods which first use only tandem MS data to infer proteins and then use network information to refine them, this study proposes a protein inference method named TMSIN, which uses interactome networks directly. As two interacting proteins should co-exist, it is reasonable to assume that if one of the interacting proteins is confidently inferred in a sample, its interacting partners should have a high probability in the same sample, too. Therefore, we can use the neighborhood information of a protein in an interactome network to adjust the probability that the shared peptide belongs to the protein. In TMSIN, a multi-weighted graph is constructed by incorporating the bipartite graph with interactome network information, where the bipartite graph is built with the peptide identification information. Based on multi-weighted graphs, TMSIN adopts an iterative workflow to infer proteins. At each iterative step, the probability that a shared peptide belongs to a specific protein is calculated by using the Bayes' law based on the neighbor protein support scores of each protein which are mapped by the shared peptides. We carried out experiments on yeast data and human data to evaluate the performance of TMSIN in terms of ROC, q-value, and accuracy. The experimental results show that AUC scores yielded by TMSIN are 0.742 and 0.874 in yeast dataset and human dataset, respectively, and TMSIN yields the maximum number of true positives when q-value less than or equal to 0.05. The overlap analysis shows that TMSIN is an effective complementary approach for protein inference.

  18. Integration and visualization of non-coding RNA and protein interaction networks

    OpenAIRE

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian; Pan, Xiaoyong; Santos Delgado, Alberto; Anthon, Christian; Alkan, Ferhat; von Mering, Christian; Workman, Christopher; Jensen, Lars Juhl; Gorodkin, Jan

    2015-01-01

    Non-coding RNAs (ncRNAs) fulfill a diverse set of biological functions relying on interactions with other molecular entities. The advent of new experimental and computational approaches makes it possible to study ncRNAs and their associations on an unprecedented scale. We present RAIN (RNA Association and Interaction Networks) - a database that combines ncRNA-ncRNA, ncRNA-mRNA and ncRNA-protein interactions with large-scale protein association networks available in the STRING database. By int...

  19. An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships.

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    Daniel Vaiman

    Full Text Available Preeclampsia (PE is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs. However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

  20. NASA Integrated Network COOP

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    Anderson, Michael L.; Wright, Nathaniel; Tai, Wallace

    2012-01-01

    Natural disasters, terrorist attacks, civil unrest, and other events have the potential of disrupting mission-essential operations in any space communications network. NASA's Space Communications and Navigation office (SCaN) is in the process of studying options for integrating the three existing NASA network elements, the Deep Space Network, the Near Earth Network, and the Space Network, into a single integrated network with common services and interfaces. The need to maintain Continuity of Operations (COOP) after a disastrous event has a direct impact on the future network design and operations concepts. The SCaN Integrated Network will provide support to a variety of user missions. The missions have diverse requirements and include anything from earth based platforms to planetary missions and rovers. It is presumed that an integrated network, with common interfaces and processes, provides an inherent advantage to COOP in that multiple elements and networks can provide cross-support in a seamless manner. The results of trade studies support this assumption but also show that centralization as a means of achieving integration can result in single points of failure that must be mitigated. The cost to provide this mitigation can be substantial. In support of this effort, the team evaluated the current approaches to COOP, developed multiple potential approaches to COOP in a future integrated network, evaluated the interdependencies of the various approaches to the various network control and operations options, and did a best value assessment of the options. The paper will describe the trade space, the study methods, and results of the study.

  1. INTEGRATING GENETIC AND STRUCTURAL DATA ON HUMAN PROTEIN KINOME IN NETWORK-BASED MODELING OF KINASE SENSITIVITIES AND RESISTANCE TO TARGETED AND PERSONALIZED ANTICANCER DRUGS.

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    Verkhivker, Gennady M

    2016-01-01

    The human protein kinome presents one of the largest protein families that orchestrate functional processes in complex cellular networks, and when perturbed, can cause various cancers. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Coupled with the evolution of system biology approaches, genomic and proteomic technologies are rapidly identifying and charactering novel resistance mechanisms with the goal to inform rationale design of personalized kinase drugs. Integration of experimental and computational approaches can help to bring these data into a unified conceptual framework and develop robust models for predicting the clinical drug resistance. In the current study, we employ a battery of synergistic computational approaches that integrate genetic, evolutionary, biochemical, and structural data to characterize the effect of cancer mutations in protein kinases. We provide a detailed structural classification and analysis of genetic signatures associated with oncogenic mutations. By integrating genetic and structural data, we employ network modeling to dissect mechanisms of kinase drug sensitivities to oncogenic EGFR mutations. Using biophysical simulations and analysis of protein structure networks, we show that conformational-specific drug binding of Lapatinib may elicit resistant mutations in the EGFR kinase that are linked with the ligand-mediated changes in the residue interaction networks and global network properties of key residues that are responsible for structural stability of specific functional states. A strong network dependency on high centrality residues in the conformation-specific Lapatinib-EGFR complex may explain vulnerability of drug binding to a broad spectrum of mutations and the emergence of drug resistance. Our study offers a systems-based perspective on drug design by unravelling

  2. Integration of Structural Dynamics and Molecular Evolution via Protein Interaction Networks: A New Era in Genomic Medicine

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    Kumar, Avishek; Butler, Brandon M.; Kumar, Sudhir; Ozkan, S. Banu

    2016-01-01

    Summary Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. PMID:26684487

  3. Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

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    Kumar, Avishek; Butler, Brandon M; Kumar, Sudhir; Ozkan, S Banu

    2015-12-01

    Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Wellbore Integrity Network

    Energy Technology Data Exchange (ETDEWEB)

    Carey, James W. [Los Alamos National Laboratory; Bachu, Stefan [Alberta Innovates

    2012-06-21

    In this presentation, we review the current state of knowledge on wellbore integrity as developed in the IEA Greenhouse Gas Programme's Wellbore Integrity Network. Wells are one of the primary risks to the successful implementation of CO{sub 2} storage programs. Experimental studies show that wellbore materials react with CO{sub 2} (carbonation of cement and corrosion of steel) but the impact on zonal isolation is unclear. Field studies of wells in CO{sub 2}-bearing fields show that CO{sub 2} does migrate external to casing. However, rates and amounts of CO{sub 2} have not been quantified. At the decade time scale, wellbore integrity is driven by construction quality and geomechanical processes. Over longer time-scales (> 100 years), chemical processes (cement degradation and corrosion) become more important, but competing geomechanical processes may preserve wellbore integrity.

  5. Hepatitis C virus infection protein network.

    Science.gov (United States)

    de Chassey, B; Navratil, V; Tafforeau, L; Hiet, M S; Aublin-Gex, A; Agaugué, S; Meiffren, G; Pradezynski, F; Faria, B F; Chantier, T; Le Breton, M; Pellet, J; Davoust, N; Mangeot, P E; Chaboud, A; Penin, F; Jacob, Y; Vidalain, P O; Vidal, M; André, P; Rabourdin-Combe, C; Lotteau, V

    2008-01-01

    A proteome-wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein-protein interactions between HCV and human proteins was identified by yeast two-hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFbeta pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins.

  6. An integrated approach to elucidate the intra-viral and viral-cellular protein interaction networks of a gamma-herpesvirus.

    Directory of Open Access Journals (Sweden)

    Shaoying Lee

    2011-10-01

    Full Text Available Genome-wide yeast two-hybrid (Y2H screens were conducted to elucidate the molecular functions of open reading frames (ORFs encoded by murine γ-herpesvirus 68 (MHV-68. A library of 84 MHV-68 genes and gene fragments was generated in a Gateway entry plasmid and transferred to Y2H vectors. All possible pair-wise interactions between viral proteins were tested in the Y2H assay, resulting in the identification of 23 intra-viral protein-protein interactions (PPIs. Seventy percent of the interactions between viral proteins were confirmed by co-immunoprecipitation experiments. To systematically investigate virus-cellular protein interactions, the MHV-68 Y2H constructs were screened against a cellular cDNA library, yielding 243 viral-cellular PPIs involving 197 distinct cellar proteins. Network analyses indicated that cellular proteins targeted by MHV-68 had more partners in the cellular PPI network and were located closer to each other than expected by chance. Taking advantage of this observation, we scored the cellular proteins based on their network distances from other MHV-68-interacting proteins and segregated them into high (Y2H-HP and low priority/not-scored (Y2H-LP/NS groups. Significantly more genes from Y2H-HP altered MHV-68 replication when their expression was inhibited with siRNAs (53% of genes from Y2H-HP, 21% of genes from Y2H-LP/NS, and 16% of genes randomly chosen from the human PPI network; p<0.05. Enriched Gene Ontology (GO terms in the Y2H-HP group included regulation of apoptosis, protein kinase cascade, post-translational protein modification, transcription from RNA polymerase II promoter, and IκB kinase/NFκB cascade. Functional validation assays indicated that PCBP1, which interacted with MHV-68 ORF34, may be involved in regulating late virus gene expression in a manner consistent with the effects of its viral interacting partner. Our study integrated Y2H screening with multiple functional validation approaches to create

  7. The integrated disease network.

    Science.gov (United States)

    Sun, Kai; Buchan, Natalie; Larminie, Chris; Pržulj, Nataša

    2014-11-01

    The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease-chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease-disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases.

  8. A scored human protein-protein interaction network to catalyze genomic interpretation

    DEFF Research Database (Denmark)

    Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...

  9. Integrating Networking into ATLAS

    CERN Document Server

    Mc Kee, Shawn Patrick; The ATLAS collaboration

    2018-01-01

    Networking is foundational to the ATLAS distributed infrastructure and there are many ongoing activities related to networking both within and outside of ATLAS. We will report on the progress in a number of areas exploring ATLAS's use of networking and our ability to monitor the network, analyze metrics from the network, and tune and optimize application and end-host parameters to make the most effective use of the network. Specific topics will include work on Open vSwitch for production systems, network analytics, FTS testing and tuning, and network problem alerting and alarming.

  10. Protein Networks in Alzheimer's Disease

    DEFF Research Database (Denmark)

    Carlsen, Eva Meier; Rasmussen, Rune

    2017-01-01

    Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans......Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans...

  11. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  12. Spectral affinity in protein networks.

    Science.gov (United States)

    Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

    2009-11-29

    Protein-protein interaction (PPI) networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise than simpler methods based on edge density and shortest path length. We develop a novel affinity measure for pairs of proteins in PPI networks, which uses personalized PageRank, a random walk based method used in context-sensitive search on the Web. Our measure of closeness, which we call PageRank Affinity, is proportional to the number of times the smaller-degree protein is visited in a random walk that restarts at the larger-degree protein. PageRank considers paths of all lengths in a network, therefore PageRank Affinity is a precise measure that is robust to noise in the data. PageRank Affinity is also provably related to cluster co-membership, making it a meaningful measure. In our experiments on protein networks we find that our measure is better at predicting co-complex membership and finding functionally related proteins than other commonly used measures of closeness. Moreover, our experiments indicate that PageRank Affinity is very resilient to noise in the network. In addition, based on our method we build a tool that quickly finds nodes closest to a queried protein in any protein network, and easily scales to much larger biological networks. We define a meaningful way to assess the closeness of two proteins in a PPI network, and show that our closeness measure is more biologically significant than other commonly used methods. We also develop a tool, accessible at http://xialab.bu.edu/resources/pnns, that allows the user to quickly find nodes closest to a queried vertex in any protein

  13. Spectral affinity in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-11-01

    Full Text Available Abstract Background Protein-protein interaction (PPI networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise than simpler methods based on edge density and shortest path length. Results We develop a novel affinity measure for pairs of proteins in PPI networks, which uses personalized PageRank, a random walk based method used in context-sensitive search on the Web. Our measure of closeness, which we call PageRank Affinity, is proportional to the number of times the smaller-degree protein is visited in a random walk that restarts at the larger-degree protein. PageRank considers paths of all lengths in a network, therefore PageRank Affinity is a precise measure that is robust to noise in the data. PageRank Affinity is also provably related to cluster co-membership, making it a meaningful measure. In our experiments on protein networks we find that our measure is better at predicting co-complex membership and finding functionally related proteins than other commonly used measures of closeness. Moreover, our experiments indicate that PageRank Affinity is very resilient to noise in the network. In addition, based on our method we build a tool that quickly finds nodes closest to a queried protein in any protein network, and easily scales to much larger biological networks. Conclusion We define a meaningful way to assess the closeness of two proteins in a PPI network, and show that our closeness measure is more biologically significant than other commonly used methods. We also develop a tool, accessible at http://xialab.bu.edu/resources/pnns, that allows the user to

  14. Integrating networks with Mathematica

    NARCIS (Netherlands)

    Strijkers, R.J.; Meijer, R.J.

    2008-01-01

    We have developed a concept that considers network behavior as a collection of software objects, which can be used or modified in computer programs. The interfaces of these software objects are exposed as web services and enable applications to analyze and manipulate networks, e.g. to find

  15. CRCHD Integrated Networks

    Science.gov (United States)

    INB supports two network-based programs—the National Outreach Network (NON) and the Geographic Management of Cancer Health Disparities Program (GMaP)—as well as advising on women’s health and sexual and gender minority opportunities within and across the NCI.

  16. Functional Module Analysis for Gene Coexpression Networks with Network Integration.

    Science.gov (United States)

    Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

    2015-01-01

    Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

  17. Integration, mentoring & networking

    DEFF Research Database (Denmark)

    Bloksgaard, Lotte

    KVINFOs mentornetværk har siden 2003 anvendt mentoring og networking med det formål at åbne døre til det danske samfund og arbejdsmarked for kvinder med indvandrer-/flygtningebaggrund. I mentoringdelen matches kvinder med flygtninge- og indvandrerbaggrund (mentees) med kvinder, som er solidt...... KVINFOs mentornetværk, at indsamle og analysere disses erfaringer med at indgå i netværket samt opnå større viden om mentoring og networking som integrationsfremmende metoder....

  18. European networks in structural integrity

    International Nuclear Information System (INIS)

    Crutzen, S.; Davies, M.; Hemsworth, B.; Hurst, R.; Kussmaul, K.

    1994-01-01

    Several institutions and electrical utilities in Europe, including the Joint Research Centre (JRC) have the capability to deal problems posed by the operation and ageing of structural components and with their structural integrity assessment. These institutions and the JRC have developed cooperative programmes now organised in networks. They include utilities, engineering companies, R and D laboratories and Regulatory Bodies. Networks are organised and managed like the successful PISC programme: The Institute for Advanced Materials of JRC plays the role of Operating Agent and Manager of these networks: ENIQ, AMES, NESC, each of them dealing with a specific aspect of fitness for purpose of materials in structural components. There exist strong links between the networks and EC Working Groups on Structural Integrity Codes and Standards. (orig.)

  19. European networks in industrial integrity

    International Nuclear Information System (INIS)

    Crutzen, S.

    1995-01-01

    Several institutions and electrical utilities in Europe, including the Joint Research Centre (JRC), have the capability to deal with several of the problems posed by the operation and aging of structural components and with their structural integrity assessment. These institutions and the JRC have developed cooperative programmes and organized themselves into networks. This article describes the structure and objective of the existing networks. 3 figs

  20. Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

    Directory of Open Access Journals (Sweden)

    Guang Hu

    2017-01-01

    Full Text Available The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM and Protein Contact Network (PCN are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

  1. Oligomeric protein structure networks: insights into protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Brinda KV

    2005-12-01

    Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

  2. Integration of omic networks in a developmental atlas of maize.

    Science.gov (United States)

    Walley, Justin W; Sartor, Ryan C; Shen, Zhouxin; Schmitz, Robert J; Wu, Kevin J; Urich, Mark A; Nery, Joseph R; Smith, Laurie G; Schnable, James C; Ecker, Joseph R; Briggs, Steven P

    2016-08-19

    Coexpression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting functional roles of individual genes at a system-wide scale. To enable network reconstructions, we built a large-scale gene expression atlas composed of 62,547 messenger RNAs (mRNAs), 17,862 nonmodified proteins, and 6227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. Networks in which nodes are genes connected on the basis of highly correlated expression patterns of mRNAs were very different from networks that were based on coexpression of proteins. Roughly 85% of highly interconnected hubs were not conserved in expression between RNA and protein networks. However, networks from either data type were enriched in similar ontological categories and were effective in predicting known regulatory relationships. Integration of mRNA, protein, and phosphoprotein data sets greatly improved the predictive power of GRNs. Copyright © 2016, American Association for the Advancement of Science.

  3. Building secure network by integrated technology

    International Nuclear Information System (INIS)

    An Dehai; Xu Rongsheng; Liu Baoxu

    2000-01-01

    The author introduces a method which can realize the most powerful network security prevention by the network security integrated technologies such as firewall, realtime monitor, network scanner, Web detection and security, etc

  4. The Yersinia enterocolitica type three secretion chaperone SycO is integrated into the Yop regulatory network and binds to the Yop secretion protein YscM1

    Directory of Open Access Journals (Sweden)

    Heesemann Jürgen

    2007-07-01

    Full Text Available Abstract Background Pathogenic yersiniae (Y. pestis, Y. pseudotuberculosis, Y. enterocolitica share a virulence plasmid encoding a type three secretion system (T3SS. This T3SS comprises more than 40 constituents. Among these are the transport substrates called Yops (Yersinia outer proteins, the specific Yop chaperones (Sycs, and the Ysc (Yop secretion proteins which form the transport machinery. The effectors YopO and YopP are encoded on an operon together with SycO, the chaperone of YopO. The characterization of SycO is the focus of this study. Results We have established the large-scale production of recombinant SycO in its outright form. We confirm that Y. enterocolitica SycO forms homodimers which is typical for Syc chaperones. SycO overproduction in Y. enterocolitica decreases secretion of Yops into the culture supernatant suggesting a regulatory role of SycO in type III secretion. We demonstrate that in vitro SycO interacts with YscM1, a negative regulator of Yop expression in Y. enterocolitica. However, the SycO overproduction phenotype was not mediated by YscM1, YscM2, YopO or YopP as revealed by analysis of isogenic deletion mutants. Conclusion We present evidence that SycO is integrated into the regulatory network of the Yersinia T3SS. Our picture of the Yersinia T3SS interactome is supplemented by identification of the SycO/YscM1 interaction. Further, our results suggest that at least one additional interaction partner of SycO has to be identified.

  5. TTEthernet for Integrated Spacecraft Networks

    Science.gov (United States)

    Loveless, Andrew

    2015-01-01

    Aerospace projects have traditionally employed federated avionics architectures, in which each computer system is designed to perform one specific function (e.g. navigation). There are obvious downsides to this approach, including excessive weight (from so much computing hardware), and inefficient processor utilization (since modern processors are capable of performing multiple tasks). There has therefore been a push for integrated modular avionics (IMA), in which common computing platforms can be leveraged for different purposes. This consolidation of multiple vehicle functions to shared computing platforms can significantly reduce spacecraft cost, weight, and design complexity. However, the application of IMA principles introduces significant challenges, as the data network must accommodate traffic of mixed criticality and performance levels - potentially all related to the same shared computer hardware. Because individual network technologies are rarely so competent, the development of truly integrated network architectures often proves unreasonable. Several different types of networks are utilized - each suited to support a specific vehicle function. Critical functions are typically driven by precise timing loops, requiring networks with strict guarantees regarding message latency (i.e. determinism) and fault-tolerance. Alternatively, non-critical systems generally employ data networks prioritizing flexibility and high performance over reliable operation. Switched Ethernet has seen widespread success filling this role in terrestrial applications. Its high speed, flexibility, and the availability of inexpensive commercial off-the-shelf (COTS) components make it desirable for inclusion in spacecraft platforms. Basic Ethernet configurations have been incorporated into several preexisting aerospace projects, including both the Space Shuttle and International Space Station (ISS). However, classical switched Ethernet cannot provide the high level of network

  6. Integration of genomic information with biological networks using Cytoscape.

    Science.gov (United States)

    Bauer-Mehren, Anna

    2013-01-01

    Cytoscape is an open-source software for visualizing, analyzing, and modeling biological networks. This chapter explains how to use Cytoscape to analyze the functional effect of sequence variations in the context of biological networks such as protein-protein interaction networks and signaling pathways. The chapter is divided into five parts: (1) obtaining information about the functional effect of sequence variation in a Cytoscape readable format, (2) loading and displaying different types of biological networks in Cytoscape, (3) integrating the genomic information (SNPs and mutations) with the biological networks, and (4) analyzing the effect of the genomic perturbation onto the network structure using Cytoscape built-in functions. Finally, we briefly outline how the integrated data can help in building mathematical network models for analyzing the effect of the sequence variation onto the dynamics of the biological system. Each part is illustrated by step-by-step instructions on an example use case and visualized by many screenshots and figures.

  7. Protein-Protein Interaction Network and Gene Ontology

    Science.gov (United States)

    Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

    Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

  8. Integration of biological networks and gene expression data using Cytoscape

    DEFF Research Database (Denmark)

    Cline, M.S.; Smoot, M.; Cerami, E.

    2007-01-01

    of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules......Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context...... and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape....

  9. Integration of metabolomics data into metabolic networks.

    Science.gov (United States)

    Töpfer, Nadine; Kleessen, Sabrina; Nikoloski, Zoran

    2015-01-01

    Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data.

  10. IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks.

    Science.gov (United States)

    Wong, Aaron K; Krishnan, Arjun; Yao, Victoria; Tadych, Alicja; Troyanskaya, Olga G

    2015-07-01

    IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data. IMP 2.0 integrates updated prior knowledge and data collections from the last three years in the seven supported organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans, and Saccharomyces cerevisiae) and extends function prediction coverage to include human disease. IMP identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms. Additionally, IMP 2.0 implements a new flexible platform for experts to generate custom hypotheses about biological processes or diseases, making sophisticated data-driven methods easily accessible to researchers. IMP does not require any registration or installation and is freely available for use at http://imp.princeton.edu. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  11. Protein Annotation from Protein Interaction Networks and Gene Ontology

    OpenAIRE

    Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

    2011-01-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

  12. Combating Forest Corruption: the Forest Integrity Network

    NARCIS (Netherlands)

    Gupta, A.; Siebert, U.

    2004-01-01

    This article describes the strategies and activities of the Forest Integrity Network. One of the most important underlying causes of forest degradation is corruption and related illegal logging. The Forest Integrity Network is a timely new initiative to combat forest corruption. Its approach is to

  13. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

    Directory of Open Access Journals (Sweden)

    Li Min

    2012-03-01

    Full Text Available Abstract Background Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC, Betweenness Centrality (BC, Closeness Centrality (CC, Subgraph Centrality (SC, Eigenvector Centrality (EC, Information Centrality (IC, Bottle Neck (BN, Density of Maximum Neighborhood Component (DMNC, Local Average Connectivity-based method (LAC, Sum of ECC (SoECC, Range-Limited Centrality (RL, L-index (LI, Leader Rank (LR, Normalized α-Centrality (NC, and Moduland-Centrality (MC. Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN is more than 50% when predicting no more than 500 proteins. Conclusions We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

  14. Unified Alignment of Protein-Protein Interaction Networks.

    Science.gov (United States)

    Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

    2017-04-19

    Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

  15. Integration of RFID and Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Miodrag; Bolic; Amiya; Nayak; Ivan; Stojmenovi.

    2007-01-01

    Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two important wireless technologies that have wide variety of applications and provide limitless future potentials. However,RFID and sensor networks almost are under development in parallel way. Integration of RFID and wireless sensor networks attracts little attention from research community. This paper first presents a brief introduction on RFID,and then investigates recent research works,new products/patents and applications that integrate RFID with sensor networks. Four types of integration are discussed. They are integrating tags with sensors,integrating tags with wireless sensor nodes,integrating readers with wireless sensor nodes and wire-less devices,and mix of RFID and sensors. New challenges and future works are discussed in the end.

  16. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  17. An analysis pipeline for the inference of protein-protein interaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

    2009-12-01

    An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

  18. Functional modules by relating protein interaction networks and gene expression.

    Science.gov (United States)

    Tornow, Sabine; Mewes, H W

    2003-11-01

    Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

  19. The Oncogenic Palmitoyi-Protein Network in Prostate Cancer

    Science.gov (United States)

    2015-06-01

    was performed by comparing LFQ intensities computed by MaxQuant.16 After statistical analysis, we identified 29 significantly downregulated and 32... statistical analysis, 30 candidate palmitoyl-proteins with an H/L ratio cutoff of 0.667 were accepted as candidate DHHC3 substrates (Table 1). Among...proteomics, we identified a gigantic palmitoyl-protein network regulated by caveolin-1. Moreover, by integrating RNA interference (RNAi), triplex SILAC, and

  20. NAPS: Network Analysis of Protein Structures

    Science.gov (United States)

    Chakrabarty, Broto; Parekh, Nita

    2016-01-01

    Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

  1. Load balancing in integrated optical wireless networks

    DEFF Research Database (Denmark)

    Yan, Ying; Dittmann, Lars; Wong, S-W.

    2010-01-01

    In this paper, we tackle the load balancing problem in Integrated Optical Wireless Networks, where cell breathing technique is used to solve congestion by changing the coverage area of a fully loaded cell tower. Our objective is to design a load balancing mechanism which works closely...... with the integrated control scheme so as to maximize overall network throughput in the integrated network architecture. To the best of our knowledge no load balancing mechanisms, especially based on the Multi-Point Control Protocol (MPCP) defined in the IEEE 802.3ah, have been proposed so far. The major research...... issues are outlined and a cost function based optimization model is developed for power management. In particularly, two alternative feedback schemes are proposed to report wireless network status. Simulation results show that our proposed load balancing mechanism improves network performances....

  2. Challenges of Integrating NASA's Space Communications Networks

    Science.gov (United States)

    Reinert, Jessica; Barnes, Patrick

    2013-01-01

    The transition to new technology, innovative ideas, and resistance to change is something that every industry experiences. Recent examples of this shift are changing to using robots in the assembly line construction of automobiles or the increasing use of robotics for medical procedures. Most often this is done with cost-reduction in mind, though ease of use for the customer is also a driver. All industries experience the push to increase efficiency of their systems; National Aeronautics and Space Administration (NASA) and the commercial space industry are no different. NASA space communication services are provided by three separately designed, developed, maintained, and operated communications networks known as the Deep Space Network (DSN), Near Earth Network (NEN) and Space Network (SN). The Space Communications and Navigation (SCaN) Program is pursuing integration of these networks and has performed a variety of architecture trade studies to determine what integration options would be the most effective in achieving a unified user mission support organization, and increase the use of common operational equipment and processes. The integration of multiple, legacy organizations and existing systems has challenges ranging from technical to cultural. The existing networks are the progeny of the very first communication and tracking capabilities implemented by NASA and the Jet Propulsion Laboratory (JPL) more than 50 years ago and have been customized to the needs of their respective user mission base. The technical challenges to integrating the networks are many, though not impossible to overcome. The three distinct networks provide the same types of services, with customizable data rates, bandwidth, frequencies, and so forth. The differences across the networks have occurred in effort to satisfy their user missions' needs. Each new requirement has made the networks more unique and harder to integrate. The cultural challenges, however, have proven to be a

  3. Challenges of Integrating NASAs Space Communication Networks

    Science.gov (United States)

    Reinert, Jessica M.; Barnes, Patrick

    2013-01-01

    The transition to new technology, innovative ideas, and resistance to change is something that every industry experiences. Recent examples of this shift are changing to using robots in the assembly line construction of automobiles or the increasing use of robotics for medical procedures. Most often this is done with cost-reduction in mind, though ease of use for the customer is also a driver. All industries experience the push to increase efficiency of their systems; National Aeronautics and Space Administration (NASA) and the commercial space industry are no different. NASA space communication services are provided by three separately designed, developed, maintained, and operated communications networks known as the Deep Space Network (DSN), Near Earth Network (NEN) and Space Network (SN). The Space Communications and Navigation (SCaN) Program is pursuing integration of these networks and has performed a variety of architecture trade studies to determine what integration options would be the most effective in achieving a unified user mission support organization, and increase the use of common operational equipment and processes. The integration of multiple, legacy organizations and existing systems has challenges ranging from technical to cultural. The existing networks are the progeny of the very first communication and tracking capabilities implemented by NASA and the Jet Propulsion Laboratory (JPL) more than 50 years ago and have been customized to the needs of their respective user mission base. The technical challenges to integrating the networks are many, though not impossible to overcome. The three distinct networks provide the same types of services, with customizable data rates, bandwidth, frequencies, and so forth. The differences across the networks have occurred in effort to satisfy their user missions' needs. Each new requirement has made the networks more unique and harder to integrate. The cultural challenges, however, have proven to be a

  4. Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

    DEFF Research Database (Denmark)

    Bergholdt, R.; Størling, Zenia, Marian; Hansen, Kasper Lage

    2007-01-01

    We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We...... identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases....

  5. Integrated analysis of multiple data sources reveals modular structure of biological networks

    International Nuclear Information System (INIS)

    Lu Hongchao; Shi Baochen; Wu Gaowei; Zhang Yong; Zhu Xiaopeng; Zhang Zhihua; Liu Changning; Zhao, Yi; Wu Tao; Wang Jie; Chen Runsheng

    2006-01-01

    It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks

  6. Trends in Integrated Ship Control Networking

    DEFF Research Database (Denmark)

    Jørgensen, N.; Nielsen, Jens Frederik Dalsgaard

    1997-01-01

    Integrated Ship Control systems can be designed as robust, distributed, autonomous control systems. The EU funded ATOMOS and ATOMOS II projects involves both technical and non technical aspects of this process. A reference modelling concept giving an outline of a generic ISC system covering...... the network and the equipment connected to it, a framework for verification of network functionality and performance by simulation and a general distribution platform for ISC systems, The ATOMOS Network, are results of this work....

  7. Chaperone-protease networks in mitochondrial protein homeostasis.

    Science.gov (United States)

    Voos, Wolfgang

    2013-02-01

    As essential organelles, mitochondria are intimately integrated into the metabolism of a eukaryotic cell. The maintenance of the functional integrity of the mitochondrial proteome, also termed protein homeostasis, is facing many challenges both under normal and pathological conditions. First, since mitochondria are derived from bacterial ancestor cells, the proteins in this endosymbiotic organelle have a mixed origin. Only a few proteins are encoded on the mitochondrial genome, most genes for mitochondrial proteins reside in the nuclear genome of the host cell. This distribution requires a complex biogenesis of mitochondrial proteins, which are mostly synthesized in the cytosol and need to be imported into the organelle. Mitochondrial protein biogenesis usually therefore comprises complex folding and assembly processes to reach an enzymatically active state. In addition, specific protein quality control (PQC) processes avoid an accumulation of damaged or surplus polypeptides. Mitochondrial protein homeostasis is based on endogenous enzymatic components comprising a diverse set of chaperones and proteases that form an interconnected functional network. This review describes the different types of mitochondrial proteins with chaperone functions and covers the current knowledge of their roles in protein biogenesis, folding, proteolytic removal and prevention of aggregation, the principal reactions of protein homeostasis. This article is part of a Special Issue entitled: Protein Import and Quality Control in Mitochondria and Plastids. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Protein annotation from protein interaction networks and Gene Ontology.

    Science.gov (United States)

    Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

    2011-10-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  10. Protein enriched pasta: structure and digestibility of its protein network.

    Science.gov (United States)

    Laleg, Karima; Barron, Cécile; Santé-Lhoutellier, Véronique; Walrand, Stéphane; Micard, Valérie

    2016-02-01

    Wheat (W) pasta was enriched in 6% gluten (G), 35% faba (F) or 5% egg (E) to increase its protein content (13% to 17%). The impact of the enrichment on the multiscale structure of the pasta and on in vitro protein digestibility was studied. Increasing the protein content (W- vs. G-pasta) strengthened pasta structure at molecular and macroscopic scales but reduced its protein digestibility by 3% by forming a higher covalently linked protein network. Greater changes in the macroscopic and molecular structure of the pasta were obtained by varying the nature of protein used for enrichment. Proteins in G- and E-pasta were highly covalently linked (28-32%) resulting in a strong pasta structure. Conversely, F-protein (98% SDS-soluble) altered the pasta structure by diluting gluten and formed a weak protein network (18% covalent link). As a result, protein digestibility in F-pasta was significantly higher (46%) than in E- (44%) and G-pasta (39%). The effect of low (55 °C, LT) vs. very high temperature (90 °C, VHT) drying on the protein network structure and digestibility was shown to cause greater molecular changes than pasta formulation. Whatever the pasta, a general strengthening of its structure, a 33% to 47% increase in covalently linked proteins and a higher β-sheet structure were observed. However, these structural differences were evened out after the pasta was cooked, resulting in identical protein digestibility in LT and VHT pasta. Even after VHT drying, F-pasta had the best amino acid profile with the highest protein digestibility, proof of its nutritional interest.

  11. The DIMA web resource--exploring the protein domain network.

    Science.gov (United States)

    Pagel, Philipp; Oesterheld, Matthias; Stümpflen, Volker; Frishman, Dmitrij

    2006-04-15

    Conserved domains represent essential building blocks of most known proteins. Owing to their role as modular components carrying out specific functions they form a network based both on functional relations and direct physical interactions. We have previously shown that domain interaction networks provide substantially novel information with respect to networks built on full-length protein chains. In this work we present a comprehensive web resource for exploring the Domain Interaction MAp (DIMA), interactively. The tool aims at integration of multiple data sources and prediction techniques, two of which have been implemented so far: domain phylogenetic profiling and experimentally demonstrated domain contacts from known three-dimensional structures. A powerful yet simple user interface enables the user to compute, visualize, navigate and download domain networks based on specific search criteria. http://mips.gsf.de/genre/proj/dima

  12. Tools and Models for Integrating Multiple Cellular Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gerstein, Mark [Yale Univ., New Haven, CT (United States). Gerstein Lab.

    2015-11-06

    In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed

  13. Growing functional modules from a seed protein via integration of protein interaction and gene expression data

    Directory of Open Access Journals (Sweden)

    Dimitrakopoulou Konstantina

    2007-10-01

    Full Text Available Abstract Background Nowadays modern biology aims at unravelling the strands of complex biological structures such as the protein-protein interaction (PPI networks. A key concept in the organization of PPI networks is the existence of dense subnetworks (functional modules in them. In recent approaches clustering algorithms were applied at these networks and the resulting subnetworks were evaluated by estimating the coverage of well-established protein complexes they contained. However, most of these algorithms elaborate on an unweighted graph structure which in turn fails to elevate those interactions that would contribute to the construction of biologically more valid and coherent functional modules. Results In the current study, we present a method that corroborates the integration of protein interaction and microarray data via the discovery of biologically valid functional modules. Initially the gene expression information is overlaid as weights onto the PPI network and the enriched PPI graph allows us to exploit its topological aspects, while simultaneously highlights enhanced functional association in specific pairs of proteins. Then we present an algorithm that unveils the functional modules of the weighted graph by expanding a kernel protein set, which originates from a given 'seed' protein used as starting-point. Conclusion The integrated data and the concept of our approach provide reliable functional modules. We give proofs based on yeast data that our method manages to give accurate results in terms both of structural coherency, as well as functional consistency.

  14. Evaluation of clustering algorithms for protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    van Helden Jacques

    2006-11-01

    Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This

  15. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  16. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  17. Influence of degree correlations on network structure and stability in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zimmer Ralf

    2007-08-01

    Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

  18. Network Compression as a Quality Measure for Protein Interaction Networks

    Science.gov (United States)

    Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

    2012-01-01

    With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

  19. Networking of integrated pest management

    NARCIS (Netherlands)

    Lamichhane, Jay Ram; Aubertot, Jean Noël; Begg, Graham; Birch, Andrew Nicholas E.; Boonekamp, Piet; Dachbrodt-Saaydeh, Silke; Hansen, Jens Grønbech; Hovmøller, Mogens Støvring; Jensen, Jens Erik; Jørgensen, Lise Nistrup; Kiss, Jozsef; Kudsk, Per; Moonen, Anna Camilla; Rasplus, Jean Yves; Sattin, Maurizio; Streito, Jean Claude; Messéan, Antoine

    2016-01-01

    Integrated pest management (IPM) is facing both external and internal challenges. External challenges include increasing needs to manage pests (pathogens, animal pests and weeds) due to climate change, evolution of pesticide resistance as well as virulence matching host resistance. The complexity

  20. Data management of protein interaction networks

    CERN Document Server

    Cannataro, Mario

    2012-01-01

    Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

  1. Measuring the degree of integration for an integrated service network

    Directory of Open Access Journals (Sweden)

    Chenglin Ye

    2012-09-01

    Full Text Available Background: Integration involves the coordination of services provided by autonomous agencies and improves the organization and delivery of multiple services for target patients. Current measures generally do not distinguish between agencies' perception and expectation. We propose a method for quantifying the agencies' service integration. Using the data from the Children's Treatment Network (CTN, we aimed to measure the degree of integration for the CTN agencies in York and Simcoe.  Theory and Methods: We quantified the integration by the agreement between perceived and expected levels of involvement and calculated four scores from different perspectives for each agency. We used the average score to measure the global network integration and examined the sensitivity of the global score.  Results: Most agencies' integration scores were less than 65%. As measured by the agreement between every other agency's perception and expectation, the overall integration of CTN in Simcoe and York was 44% (95% CI: 39% - 49% and 52% (95% CI: 48% - 56%, respectively. The sensitivity analysis showed that the global scores were robust.  Conclusion: Our method extends existing measures of integration and possesses a good extent of validity. We can also apply the method in monitoring improvement and linking integration with other outcomes.

  2. Measuring the degree of integration for an integrated service network

    Directory of Open Access Journals (Sweden)

    Chenglin Ye

    2012-09-01

    Full Text Available Background: Integration involves the coordination of services provided by autonomous agencies and improves the organization and delivery of multiple services for target patients. Current measures generally do not distinguish between agencies' perception and expectation. We propose a method for quantifying the agencies' service integration. Using the data from the Children's Treatment Network (CTN, we aimed to measure the degree of integration for the CTN agencies in York and Simcoe. Theory and Methods: We quantified the integration by the agreement between perceived and expected levels of involvement and calculated four scores from different perspectives for each agency. We used the average score to measure the global network integration and examined the sensitivity of the global score. Results: Most agencies' integration scores were less than 65%. As measured by the agreement between every other agency's perception and expectation, the overall integration of CTN in Simcoe and York was 44% (95% CI: 39% - 49% and 52% (95% CI: 48% - 56%, respectively. The sensitivity analysis showed that the global scores were robust. Conclusion: Our method extends existing measures of integration and possesses a good extent of validity. We can also apply the method in monitoring improvement and linking integration with other outcomes. 

  3. Integration of a network aware traffic generation device into a computer network emulation platform

    CSIR Research Space (South Africa)

    Von Solms, S

    2014-07-01

    Full Text Available Flexible, open source network emulation tools can provide network researchers with significant benefits regarding network behaviour and performance. The evaluation of these networks can benefit greatly from the integration of realistic, network...

  4. Finding local communities in protein networks.

    Science.gov (United States)

    Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

    2009-09-18

    Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to

  5. Finding local communities in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-09-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. Results We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. Conclusion The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent

  6. Retroviral DNA Integration Directed by HIV Integration Protein in Vitro

    Science.gov (United States)

    Bushman, Frederic D.; Fujiwara, Tamio; Craigie, Robert

    1990-09-01

    Efficient retroviral growth requires integration of a DNA copy of the viral RNA genome into a chromosome of the host. As a first step in analyzing the mechanism of integration of human immunodeficiency virus (HIV) DNA, a cell-free system was established that models the integration reaction. The in vitro system depends on the HIV integration (IN) protein, which was partially purified from insect cells engineered to express IN protein in large quantities. Integration was detected in a biological assay that scores the insertion of a linear DNA containing HIV terminal sequences into a λ DNA target. Some integration products generated in this assay contained five-base pair duplications of the target DNA at the recombination junctions, a characteristic of HIV integration in vivo; the remaining products contained aberrant junctional sequences that may have been produced in a variation of the normal reaction. These results indicate that HIV IN protein is the only viral protein required to insert model HIV DNA sequences into a target DNA in vitro.

  7. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An

  8. Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

    Directory of Open Access Journals (Sweden)

    Wan Li

    Full Text Available The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial. Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.

  9. A conserved mammalian protein interaction network.

    Directory of Open Access Journals (Sweden)

    Åsa Pérez-Bercoff

    Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

  10. Evolution of an intricate J-protein network driving protein disaggregation in eukaryotes.

    Science.gov (United States)

    Nillegoda, Nadinath B; Stank, Antonia; Malinverni, Duccio; Alberts, Niels; Szlachcic, Anna; Barducci, Alessandro; De Los Rios, Paolo; Wade, Rebecca C; Bukau, Bernd

    2017-05-15

    Hsp70 participates in a broad spectrum of protein folding processes extending from nascent chain folding to protein disaggregation. This versatility in function is achieved through a diverse family of J-protein cochaperones that select substrates for Hsp70. Substrate selection is further tuned by transient complexation between different classes of J-proteins, which expands the range of protein aggregates targeted by metazoan Hsp70 for disaggregation. We assessed the prevalence and evolutionary conservation of J-protein complexation and cooperation in disaggregation. We find the emergence of a eukaryote-specific signature for interclass complexation of canonical J-proteins. Consistently, complexes exist in yeast and human cells, but not in bacteria, and correlate with cooperative action in disaggregation in vitro. Signature alterations exclude some J-proteins from networking, which ensures correct J-protein pairing, functional network integrity and J-protein specialization. This fundamental change in J-protein biology during the prokaryote-to-eukaryote transition allows for increased fine-tuning and broadening of Hsp70 function in eukaryotes.

  11. Digital telephony and network integration

    CERN Document Server

    Keiser, Bernhard E

    1995-01-01

    What is "digital telephony"? To the authors, the term digital telephony denotes the technology used to provide a completely digital telecommunication system from end-to-end. This implies the use of digital technology from one end instru­ ment through transmission facilities and switching centers to another end instru­ ment. Digital telephony has become possible only because of the recent and on­ going surge of semiconductor developments, allowing microminiaturization and high reliability along with reduced costs. This book deals with both the future and the present. Thus, the first chapter is entitled, "A Network in Transition." As baselines, Chapters 2 and 11 provide the reader with the present status of teler-hone technology in terms of voice digiti­ zation as well as switching principles. The book is an outgrowth of the authors' consulting and teaching experience in the field since the early 1980s. The book has been written to provide both the engineering student and the practicing engineer a working k...

  12. AVID: An integrative framework for discovering functional relationships among proteins

    Directory of Open Access Journals (Sweden)

    Keating Amy E

    2005-06-01

    Full Text Available Abstract Background Determining the functions of uncharacterized proteins is one of the most pressing problems in the post-genomic era. Large scale protein-protein interaction assays, global mRNA expression analyses and systematic protein localization studies provide experimental information that can be used for this purpose. The data from such experiments contain many false positives and false negatives, but can be processed using computational methods to provide reliable information about protein-protein relationships and protein function. An outstanding and important goal is to predict detailed functional annotation for all uncharacterized proteins that is reliable enough to effectively guide experiments. Results We present AVID, a computational method that uses a multi-stage learning framework to integrate experimental results with sequence information, generating networks reflecting functional similarities among proteins. We illustrate use of the networks by making predictions of detailed Gene Ontology (GO annotations in three categories: molecular function, biological process, and cellular component. Applied to the yeast Saccharomyces cerevisiae, AVID provides 37,451 pair-wise functional linkages between 4,191 proteins. These relationships are ~65–78% accurate, as assessed by cross-validation testing. Assignments of highly detailed functional descriptors to proteins, based on the networks, are estimated to be ~67% accurate for GO categories describing molecular function and cellular component and ~52% accurate for terms describing biological process. The predictions cover 1,490 proteins with no previous annotation in GO and also assign more detailed functions to many proteins annotated only with less descriptive terms. Predictions made by AVID are largely distinct from those made by other methods. Out of 37,451 predicted pair-wise relationships, the greatest number shared in common with another method is 3,413. Conclusion AVID provides

  13. The integration of weighted human gene association networks based on link prediction.

    Science.gov (United States)

    Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

    2017-01-31

    Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

  14. The Proteins API: accessing key integrated protein and genome information.

    Science.gov (United States)

    Nightingale, Andrew; Antunes, Ricardo; Alpi, Emanuele; Bursteinas, Borisas; Gonzales, Leonardo; Liu, Wudong; Luo, Jie; Qi, Guoying; Turner, Edd; Martin, Maria

    2017-07-03

    The Proteins API provides searching and programmatic access to protein and associated genomics data such as curated protein sequence positional annotations from UniProtKB, as well as mapped variation and proteomics data from large scale data sources (LSS). Using the coordinates service, researchers are able to retrieve the genomic sequence coordinates for proteins in UniProtKB. This, the LSS genomics and proteomics data for UniProt proteins is programmatically only available through this service. A Swagger UI has been implemented to provide documentation, an interface for users, with little or no programming experience, to 'talk' to the services to quickly and easily formulate queries with the services and obtain dynamically generated source code for popular programming languages, such as Java, Perl, Python and Ruby. Search results are returned as standard JSON, XML or GFF data objects. The Proteins API is a scalable, reliable, fast, easy to use RESTful services that provides a broad protein information resource for users to ask questions based upon their field of expertise and allowing them to gain an integrated overview of protein annotations available to aid their knowledge gain on proteins in biological processes. The Proteins API is available at (http://www.ebi.ac.uk/proteins/api/doc). © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Electric Vehicle Integration into Modern Power Networks

    DEFF Research Database (Denmark)

    software tools to assess the impacts resulting from the electric vehicles deployment on the steady state and dynamic operation of electricity grids, identifies strategies to mitigate them and the possibility to support simultaneously large-scale integration of renewable energy sources. New business models......Electric Vehicle Integration into Modern Power Networks provides coverage of the challenges and opportunities posed by the progressive integration of electric drive vehicles. Starting with a thorough overview of the current electric vehicle and battery state-of-the-art, this work describes dynamic...... and control management architectures, as well as the communication infrastructure required to integrate electric vehicles as active demand are presented. Finally, regulatory issues of integrating electric vehicles into modern power systems are addressed. Inspired by two courses held under the EES...

  16. Electric Vehicle Integration into Modern Power Networks

    DEFF Research Database (Denmark)

    Electric Vehicle Integration into Modern Power Networks provides coverage of the challenges and opportunities posed by the progressive integration of electric drive vehicles. Starting with a thorough overview of the current electric vehicle and battery state-of-the-art, this work describes dynamic...... software tools to assess the impacts resulting from the electric vehicles deployment on the steady state and dynamic operation of electricity grids, identifies strategies to mitigate them and the possibility to support simultaneously large-scale integration of renewable energy sources. New business models...... and control management architectures, as well as the communication infrastructure required to integrate electric vehicles as active demand are presented. Finally, regulatory issues of integrating electric vehicles into modern power systems are addressed. Inspired by two courses held under the EES...

  17. Human Systems Integration Assessment of Network Centric Command and Control

    National Research Council Canada - National Science Library

    Quashnock, Dee; Kelly, Richard T; Dunaway, John; Smillie, Robert J

    2004-01-01

    .... FORCEnet is the operational construct and architectural framework for Naval Network Centric Warfare in the information age that integrates warriors, sensors, networks, command and control, platforms...

  18. Integrated network design and scheduling problems :

    Energy Technology Data Exchange (ETDEWEB)

    Nurre, Sarah G.; Carlson, Jeffrey J.

    2014-01-01

    We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.

  19. Integrating neural network technology and noise analysis

    International Nuclear Information System (INIS)

    Uhrig, R.E.; Oak Ridge National Lab., TN

    1995-01-01

    The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check values and bearings illustrate the usefulness of the methodology developed. (Author)

  20. Integrating Data and Networks: Human Factors

    Science.gov (United States)

    Chen, R. S.

    2012-12-01

    The development of technical linkages and interoperability between scientific networks is a necessary but not sufficient step towards integrated use and application of networked data and information for scientific and societal benefit. A range of "human factors" must also be addressed to ensure the long-term integration, sustainability, and utility of both the interoperable networks themselves and the scientific data and information to which they provide access. These human factors encompass the behavior of both individual humans and human institutions, and include system governance, a common framework for intellectual property rights and data sharing, consensus on terminology, metadata, and quality control processes, agreement on key system metrics and milestones, the compatibility of "business models" in the short and long term, harmonization of incentives for cooperation, and minimization of disincentives. Experience with several national and international initiatives and research programs such as the International Polar Year, the Group on Earth Observations, the NASA Earth Observing Data and Information System, the U.S. National Spatial Data Infrastructure, the Global Earthquake Model, and the United Nations Spatial Data Infrastructure provide a range of lessons regarding these human factors. Ongoing changes in science, technology, institutions, relationships, and even culture are creating both opportunities and challenges for expanded interoperability of scientific networks and significant improvement in data integration to advance science and the use of scientific data and information to achieve benefits for society as a whole.

  1. Topology-function conservation in protein-protein interaction networks.

    Science.gov (United States)

    Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

    2015-05-15

    Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

  2. Integration Strategy Is a Key Step in Network-Based Analysis and Dramatically Affects Network Topological Properties and Inferring Outcomes

    Science.gov (United States)

    Jin, Nana; Wu, Deng; Gong, Yonghui; Bi, Xiaoman; Jiang, Hong; Li, Kongning; Wang, Qianghu

    2014-01-01

    An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches. PMID:25243127

  3. Pythoscape: A framework for generation of large protein similarity networks

    OpenAIRE

    Babbitt, Patricia; Barber, AE; Babbitt, PC

    2012-01-01

    Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among pr

  4. Interplay between chaperones and protein disorder promotes the evolution of protein networks.

    Directory of Open Access Journals (Sweden)

    Sebastian Pechmann

    2014-06-01

    Full Text Available Evolution is driven by mutations, which lead to new protein functions but come at a cost to protein stability. Non-conservative substitutions are of interest in this regard because they may most profoundly affect both function and stability. Accordingly, organisms must balance the benefit of accepting advantageous substitutions with the possible cost of deleterious effects on protein folding and stability. We here examine factors that systematically promote non-conservative mutations at the proteome level. Intrinsically disordered regions in proteins play pivotal roles in protein interactions, but many questions regarding their evolution remain unanswered. Similarly, whether and how molecular chaperones, which have been shown to buffer destabilizing mutations in individual proteins, generally provide robustness during proteome evolution remains unclear. To this end, we introduce an evolutionary parameter λ that directly estimates the rate of non-conservative substitutions. Our analysis of λ in Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens sequences reveals how co- and post-translationally acting chaperones differentially promote non-conservative substitutions in their substrates, likely through buffering of their destabilizing effects. We further find that λ serves well to quantify the evolution of intrinsically disordered proteins even though the unstructured, thus generally variable regions in proteins are often flanked by very conserved sequences. Crucially, we show that both intrinsically disordered proteins and highly re-wired proteins in protein interaction networks, which have evolved new interactions and functions, exhibit a higher λ at the expense of enhanced chaperone assistance. Our findings thus highlight an intricate interplay of molecular chaperones and protein disorder in the evolvability of protein networks. Our results illuminate the role of chaperones in enabling protein evolution, and underline the

  5. Completing sparse and disconnected protein-protein network by deep learning.

    Science.gov (United States)

    Huang, Lei; Liao, Li; Wu, Cathy H

    2018-03-22

    Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network

  6. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  7. InSilico Proteomics System: Integration and Application of Protein and Protein-Protein Interaction Data using Microsoft .NET

    Directory of Open Access Journals (Sweden)

    Straßer Wolfgang

    2006-12-01

    Full Text Available In the last decades, biological databases became the major knowledge resource for researchers in the field of molecular biology. The distribution of information among these databases is one of the major problems. An overview about the subject area of data access and representation of protein and protein-protein interaction data within public biological databases is described. For a comprehensive and consistent way of searching and analysing integrated protein and protein-protein interaction data, the InSilico Proteomics (ISP project has been initiated. Its three main objectives are (1 to provide an integrated knowledge pool for data investigation and global network analysis functions for a better understanding of a cell’s interactome, (2 employment of public data for plausibility analysis and validation of in-house experimental data and (3 testing the applicability of Microsoft’s .NET architecture for bioinformatics applications. Data integrated into the ISP database can be queried through the Web portal PRIMOS (PRotein Interaction and MOlecule Search which is freely available at http://biomis.fh-hagenberg.at/isp/primos.

  8. Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

    Directory of Open Access Journals (Sweden)

    Oyang Yen-Jen

    2010-10-01

    Full Text Available Abstract Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

  9. Integrated Job Scheduling and Network Routing

    DEFF Research Database (Denmark)

    Gamst, Mette; Pisinger, David

    2013-01-01

    We consider an integrated job scheduling and network routing problem which appears in Grid Computing and production planning. The problem is to schedule a number of jobs at a finite set of machines, such that the overall profit of the executed jobs is maximized. Each job demands a number of resou...... indicate that the algorithm can be used as an actual scheduling algorithm in the Grid or as a tool for analyzing Grid performance when adding extra machines or jobs. © 2012 Wiley Periodicals, Inc.......We consider an integrated job scheduling and network routing problem which appears in Grid Computing and production planning. The problem is to schedule a number of jobs at a finite set of machines, such that the overall profit of the executed jobs is maximized. Each job demands a number...... of resources which must be sent to the executing machine through a network with limited capacity. A job cannot start before all of its resources have arrived at the machine. The scheduling problem is formulated as a Mixed Integer Program (MIP) and proved to be NP-hard. An exact solution approach using Dantzig...

  10. Construction of ontology augmented networks for protein complex prediction.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

    2013-01-01

    Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

  11. Machine-to-Machine networks: integration of M2M networks into companies' administrative networks

    OpenAIRE

    Pointereau, Romain

    2013-01-01

    This analysis will address the technical, economic and regulatory aspects and will identify the position taken by the various market actors. Integration of M2M Networks into Companies' Administrative Networks. Integración de redes M2M en redes administrativas de las empresas. Integració de xarxes M2M en xarxes administratives de les empreses.

  12. MINET [momentum integral network] code documentation

    International Nuclear Information System (INIS)

    Van Tuyle, G.J.; Nepsee, T.C.; Guppy, J.G.

    1989-12-01

    The MINET computer code, developed for the transient analysis of fluid flow and heat transfer, is documented in this four-part reference. In Part 1, the MINET models, which are based on a momentum integral network method, are described. The various aspects of utilizing the MINET code are discussed in Part 2, The User's Manual. The third part is a code description, detailing the basic code structure and the various subroutines and functions that make up MINET. In Part 4, example input decks, as well as recent validation studies and applications of MINET are summarized. 32 refs., 36 figs., 47 tabs

  13. Discriminating lysosomal membrane protein types using dynamic neural network.

    Science.gov (United States)

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

    This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

  14. Towards Integrated Marmara Strong Motion Network

    Science.gov (United States)

    Durukal, E.; Erdik, M.; Safak, E.; Ansal, A.; Ozel, O.; Alcik, H.; Mert, A.; Kafadar, N.; Korkmaz, A.; Kurtulus, A.

    2009-04-01

    Istanbul has a 65% chance of having a magnitude 7 or above earthquake within the next 30 years. As part of the preparations for the future earthquake, strong motion networks have been installed in and around Istanbul. The Marmara Strong Motion Network, operated by the Department of Earthquake Engineering of Kandilli Observatory and Earthquake Research Institute, encompasses permanent systems outlined below. It is envisaged that the networks will be run by a single entity responsible for technical management and maintanence, as well as for data management, archiving and dissemination through dedicated web-based interfaces. • Istanbul Earthquake Rapid Response and Early Warning System - IERREWS (one hundred 18-bit accelerometers for rapid response; ten 24-bit accelerometers for early warning) • IGDAŞ Gas Shutoff Network (100 accelerometers to be installed in 2010 and integrated with IERREWS) • Structural Monitoring Arrays - Fatih Sultan Mehmet Suspension Bridge (1200m-long suspension bridge across the Bosphorus, five 3-component accelerometers + GPS sensors) - Hagia Sophia Array (1500-year-old historical edifice, 9 accelerometers) - Süleymaniye Mosque Array (450-year-old historical edifice,9 accelerometers) - Fatih Mosque Array (237-year-old historical edifice, 9 accelerometers) - Kanyon Building Array (high-rise office building, 5 accelerometers) - Isbank Tower Array (high-rise office building, 5 accelerometers) - ENRON Array (power generation facility, 4 acelerometers) - Mihrimah Sultan Mosque Array (450-year-old historical edifice,9 accelerometers + tiltmeters, to be installed in 2009) - Sultanahmet Mosque Array, (390-year-old historical edifice, 9 accelerometers + tiltmeters, to be installed in 2009) • Special Arrays - Atakoy Vertical Array (four 3-component accelerometers at 25, 50, 75, and 150 m depths) - Marmara Tube Tunnel (1400 m long submerged tunnel, 128 ch. accelerometric data, 24 ch. strain data, to be installed in 2010) - Air-Force Academy

  15. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    DEFF Research Database (Denmark)

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

  17. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    understanding of molecular processes which are fundamental to tumorigenesis. In Article 1, we propose a novel framework for how cancer mutations can be studied by taking into account their effect at the protein network level. In Article 2, we demonstrate how global, quantitative data on phosphorylation dynamics...... can be generated using MS, and how this can be modeled using a computational framework for deciphering kinase-substrate dynamics. This framework is described in depth in Article 3, and covers the design of KinomeXplorer, which allows the prediction of kinases responsible for modulating observed...... phosphorylation dynamics in a given biological sample. In Chapter III, we move into Integrative Network Biology, where, by combining two fundamental technologies (MS & NGS), we can obtain more in-depth insights into the links between cellular phenotype and genotype. Article 4 describes the proof...

  18. Protein-protein interaction network-based detection of functionally similar proteins within species.

    Science.gov (United States)

    Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

    2012-07-01

    Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Copyright © 2012 Wiley Periodicals, Inc.

  19. ISINA: INTEGRAL Source Identification Network Algorithm

    Science.gov (United States)

    Scaringi, S.; Bird, A. J.; Clark, D. J.; Dean, A. J.; Hill, A. B.; McBride, V. A.; Shaw, S. E.

    2008-11-01

    We give an overview of ISINA: INTEGRAL Source Identification Network Algorithm. This machine learning algorithm, using random forests, is applied to the IBIS/ISGRI data set in order to ease the production of unbiased future soft gamma-ray source catalogues. First, we introduce the data set and the problems encountered when dealing with images obtained using the coded mask technique. The initial step of source candidate searching is introduced and an initial candidate list is created. A description of the feature extraction on the initial candidate list is then performed together with feature merging for these candidates. Three training and testing sets are created in order to deal with the diverse time-scales encountered when dealing with the gamma-ray sky. Three independent random forests are built: one dealing with faint persistent source recognition, one dealing with strong persistent sources and a final one dealing with transients. For the latter, a new transient detection technique is introduced and described: the transient matrix. Finally the performance of the network is assessed and discussed using the testing set and some illustrative source examples. Based on observations with INTEGRAL, an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Spain), Czech Republic and Poland, and the participation of Russia and the USA. E-mail: simo@astro.soton.ac.uk

  20. Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling

    Science.gov (United States)

    Shin, Young Shik; Remacle, F.; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R.D.; Heath, James R.

    2011-01-01

    Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571

  1. Hydrocarbons in Argentina: networks, territories, integration

    International Nuclear Information System (INIS)

    Carrizo, S.C.

    2003-12-01

    Argentinean hydrocarbons networks have lived a huge reorganizing the structure, after the State reform in the 90's. Activities deregulation and the privatization of YPF and Gas del Estado forced the sector re-concentration, since then dominated by foreign companies, leaded by Repsol YPF. The hydrocarbons federalization contributed to the weakening and un-capitalization loss of wealth of the State. These changes resulted in an increase of the hydrocarbons production allowing to achieve the self-supply. Nevertheless, the expansion of internal networks has not been large enough to ensure the coverage of new requirements. Besides, several infrastructures have been built up to join external markets. National networks are connected to those of near neighboring countries. This integration is an opportunity for the 'South Cone' countries to enhance their potentials. In the country, hydrocarbons territories undergo the reorganizing the structure effects (unemployment, loss of territorial identity, etc). With many difficulties and very different possibilities, those territories, like Comodoro Rivadavia, Ensenada et and Bahia Blanca, look for their re-invention. (author)

  2. Integrated circuit and method of arbitration in a network on an integrated circuit.

    NARCIS (Netherlands)

    2011-01-01

    The invention relates to an integrated circuit and to a method of arbitration in a network on an integrated circuit. According to the invention, a method of arbitration in a network on an integrated circuit is provided, the network comprising a router unit, the router unit comprising a first input

  3. Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

    This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large

  4. Construction and analysis of protein-protein interaction network correlated with ankylosing spondylitis.

    Science.gov (United States)

    Kanwal, Attiya; Fazal, Sahar

    2018-01-05

    Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts

  5. Interrogating the architecture of protein assemblies and protein interaction networks by cross-linking mass spectrometry

    NARCIS (Netherlands)

    Liu, Fan; Heck, Albert J R

    2015-01-01

    Proteins are involved in almost all processes of the living cell. They are organized through extensive networks of interaction, by tightly bound macromolecular assemblies or more transiently via signaling nodes. Therefore, revealing the architecture of protein complexes and protein interaction

  6. Modelling dendritic ecological networks in space: An integrated network perspective

    Science.gov (United States)

    Erin E. Peterson; Jay M. Ver Hoef; Dan J. Isaak; Jeffrey A. Falke; Marie-Josee Fortin; Chris E. Jordan; Kristina McNyset; Pascal Monestiez; Aaron S. Ruesch; Aritra Sengupta; Nicholas Som; E. Ashley Steel; David M. Theobald; Christian E. Torgersen; Seth J. Wenger

    2013-01-01

    Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of...

  7. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  8. VitisNet: "Omics" integration through grapevine molecular networks.

    Directory of Open Access Journals (Sweden)

    Jérôme Grimplet

    Full Text Available BACKGROUND: Genomic data release for the grapevine has increased exponentially in the last five years. The Vitis vinifera genome has been sequenced and Vitis EST, transcriptomic, proteomic, and metabolomic tools and data sets continue to be developed. The next critical challenge is to provide biological meaning to this tremendous amount of data by annotating genes and integrating them within their biological context. We have developed and validated a system of Grapevine Molecular Networks (VitisNet. METHODOLOGY/PRINCIPAL FINDINGS: The sequences from the Vitis vinifera (cv. Pinot Noir PN40024 genome sequencing project and ESTs from the Vitis genus have been paired and the 39,424 resulting unique sequences have been manually annotated. Among these, 13,145 genes have been assigned to 219 networks. The pathway sets include 88 "Metabolic", 15 "Genetic Information Processing", 12 "Environmental Information Processing", 3 "Cellular Processes", 21 "Transport", and 80 "Transcription Factors". The quantitative data is loaded onto molecular networks, allowing the simultaneous visualization of changes in the transcriptome, proteome, and metabolome for a given experiment. CONCLUSIONS/SIGNIFICANCE: VitisNet uses manually annotated networks in SBML or XML format, enabling the integration of large datasets, streamlining biological functional processing, and improving the understanding of dynamic processes in systems biology experiments. VitisNet is grounded in the Vitis vinifera genome (currently at 8x coverage and can be readily updated with subsequent updates of the genome or biochemical discoveries. The molecular network files can be dynamically searched by pathway name or individual genes, proteins, or metabolites through the MetNet Pathway database and web-portal at http://metnet3.vrac.iastate.edu/. All VitisNet files including the manual annotation of the grape genome encompassing pathway names, individual genes, their genome identifier, and chromosome

  9. Gene, protein and network of male sterility in rice

    Directory of Open Access Journals (Sweden)

    Wang eKun

    2013-04-01

    Full Text Available Rice is one of the most important model crop plants whose heterosis has been well exploited in commercial hybrid seed production via a variety of types of male sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and proteins related to cytoplasmic male sterility, photoperiod sensitive male sterility, self-incompatibility and other types of microspores deterioration have been characterized through genetics or proteomics. Especially the latter, offers us a powerful and high throughput approach to discern the novel proteins involving in male-sterile pathways which may help us to breed artificial male-sterile system. This represents an alternative tool to meet the critical challenge of further development of hybrid rice. In this paper, we reviewed the recent developments in our understanding of male sterility in rice hybrid production across gene, protein and integrated network levels, and also, present a perspective on the engineering of male sterile lines for hybrid rice production.

  10. Integrated control platform for converged optical and wireless networks

    DEFF Research Database (Denmark)

    Yan, Ying

    The next generation of broadband access networks is expected to be heterogeneous. Multiple wired and wireless systems can be integrated, in order to simultaneously provide seamless access with an appropriate Quality of Service (QoS). Wireless networks support ubiquitous connectivity yet low data...... rates, whereas optical networks can offer much higher data rates but only provide fixed connection structures. Their complementary characteristics make the integration of the two networks a promising trend for next generation networks. With combined strengths, the converged network will provide both...... the complementary characteristics of the optical networks and the wireless networks, addresses motivations for their interworking, discusses the current progress in hybrid network architectures as well as the functionalities of a control system, and identifies the achieved research contributions in the integrated...

  11. WATER NETWORK INTEGRATION IN RAW SUGAR PRODUCTION

    Directory of Open Access Journals (Sweden)

    Junior Lorenzo Llanes

    2017-07-01

    Full Text Available One of the main process industries in Cuba is that of the sugarcane. Among the characteristics of this industry is the high demand of water in its processes. In this work a study of water integration was carried out from the different operations of the production process of raw sugar, in order to reduce the fresh water consumption. The compound curves of sources and demands were built, which allowed the determination of the minimum water requirement of the network (1587,84 m3/d, as well as the amount of effluent generated (0,35 m3/tcane.The distribution scheme of fresh water and water reuse among different operations were obtained from the nearest neighbor algorithm. From considering new quality constrains was possible to eliminate the external water consumption, as well as to reduce the amount of effluent in a 37% in relation to the initial constrains.

  12. Biomarker Gene Signature Discovery Integrating Network Knowledge

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    2012-02-01

    Full Text Available Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.

  13. Integrated workflows for spiking neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Ján eAntolík

    2013-12-01

    Full Text Available The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeller's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modellers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity.To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualisation into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organised configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualisation stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modelling studies by relieving the user from manual handling of the flow of metadata between the individual

  14. atBioNet– an integrated network analysis tool for genomics and biomarker discovery

    Directory of Open Access Journals (Sweden)

    Ding Yijun

    2012-07-01

    Full Text Available Abstract Background Large amounts of mammalian protein-protein interaction (PPI data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. Results atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks. The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. Conclusion atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http

  15. atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

    Science.gov (United States)

    Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

    2012-07-20

    Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

  16. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    Science.gov (United States)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  17. Unveiling protein functions through the dynamics of the interaction network.

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

    Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

  18. The architectural design of networks of protein domain architectures.

    Science.gov (United States)

    Hsu, Chia-Hsin; Chen, Chien-Kuo; Hwang, Ming-Jing

    2013-08-23

    Protein domain architectures (PDAs), in which single domains are linked to form multiple-domain proteins, are a major molecular form used by evolution for the diversification of protein functions. However, the design principles of PDAs remain largely uninvestigated. In this study, we constructed networks to connect domain architectures that had grown out from the same single domain for every single domain in the Pfam-A database and found that there are three main distinctive types of these networks, which suggests that evolution can exploit PDAs in three different ways. Further analysis showed that these three different types of PDA networks are each adopted by different types of protein domains, although many networks exhibit the characteristics of more than one of the three types. Our results shed light on nature's blueprint for protein architecture and provide a framework for understanding architectural design from a network perspective.

  19. Integrating Social Networks in Teaching in Higher Education

    Science.gov (United States)

    Abousoliman, Onsy

    2017-01-01

    In response to the emerging and swiftly developing digital tools, this dissertation investigated integrating a specific category of these tools, social networks, in teaching in higher education. The study focused on exploring how social networks integration might impact the teaching/learning process and on investigating the challenges that could…

  20. A joint classification method to integrate scientific and social networks

    NARCIS (Netherlands)

    Neshati, Mahmood; Asgari, Ehsaneddin; Hiemstra, Djoerd; Beigy, Hamid

    In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs,

  1. The Genome-Scale Integrated Networks in Microorganisms

    Directory of Open Access Journals (Sweden)

    Tong Hao

    2018-02-01

    Full Text Available The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types of the molecular networks, for example, genome-scale metabolic network (GMN, transcriptional regulatory network (TRN, and signal transduction network (STN. It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.

  2. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  3. Boolean integration. [applied to switching network synthesis

    Science.gov (United States)

    Tucker, J. H.; Tapia, M. A.; Bennett, A. W.

    1976-01-01

    This paper presents the necessary and sufficient conditions for a given differential expression to be compatibly integrable and it presents the necessary and sufficient conditions for a given expression to be exactly integrable. Methods are given for integrating a differential expression when it is exactly integrable and when it is compatibly integrable. The physical interpretation is given of the integral of order k, of a differential expression, and it is shown that any differential expression of the proper form is integrable by parts.

  4. An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

    Science.gov (United States)

    Mallik, Mrinmay Kumar

    2018-02-07

    Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that

  5. Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

    Science.gov (United States)

    Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean

    2014-01-01

    Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489

  6. Integration versus Outsourcing in Stable Industry Equilibrium with Communication Networks

    OpenAIRE

    Okamoto, Yusuke

    2006-01-01

    For the selection of a firm's structure between vertical integration and arm's-length outsourcing, the importance of the thickness of the market had been emphasized in the previous literature. Here we take account of communication networks such as telephone, telex, fax, and the Internet. By doing so, we could illustrate the relationship between communication networks and the make-or-buy decision. With communication network technology differing in each type of firm, both vertically integrated ...

  7. Classification Method in Integrated Information Network Using Vector Image Comparison

    Directory of Open Access Journals (Sweden)

    Zhou Yuan

    2014-05-01

    Full Text Available Wireless Integrated Information Network (WMN consists of integrated information that can get data from its surrounding, such as image, voice. To transmit information, large resource is required which decreases the service time of the network. In this paper we present a Classification Approach based on Vector Image Comparison (VIC for WMN that improve the service time of the network. The available methods for sub-region selection and conversion are also proposed.

  8. A tensegrity model for hydrogen bond networks in proteins

    OpenAIRE

    Bywater, Robert P.

    2017-01-01

    Hydrogen-bonding networks in proteins considered as structural tensile elements are in balance separately from any other stabilising interactions that may be in operation. The hydrogen bond arrangement in the network is reminiscent of tensegrity structures in architecture and sculpture. Tensegrity has been discussed before in cells and tissues and in proteins. In contrast to previous work only hydrogen bonds are studied here. The other interactions within proteins are either much stronger − c...

  9. Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

    Science.gov (United States)

    Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

    2015-01-01

    In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

  10. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2007-10-01

    Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

  11. Wireless Integrated Network Sensors Next Generation

    National Research Council Canada - National Science Library

    Merrill, William

    2004-01-01

    ..., autonomous networking, and distributed operations for wireless networked sensor systems. Multiple types of sensor systems were developed and provided including capabilities for acoustic, seismic, passive infrared detection, and visual imaging...

  12. Integrating Network Management for Cloud Computing Services

    Science.gov (United States)

    2015-06-01

    Backend Distributed Datastore High-­‐level   Objec.ve   Network   Policy   Perf.   Metrics   SNAT  IP   Alloca.on   Controller...azure.microsoft.com/. 114 [16] Microsoft Azure ExpressRoute. http://azure.microsoft.com/en-us/ services/expressroute/. [17] Mobility and Networking...Networking Technologies, Services, and Protocols; Performance of Computer and Commu- nication Networks; Mobile and Wireless Communications Systems

  13. Integrated Optoelectronic Networks for Application-Driven Multicore Computing

    Science.gov (United States)

    2017-05-08

    AFRL-AFOSR-VA-TR-2017-0102 Integrated Optoelectronic Networks for Application- Driven Multicore Computing Sudeep Pasricha COLORADO STATE UNIVERSITY...AND SUBTITLE Integrated Optoelectronic Networks for Application-Driven Multicore Computing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0110 5c...and supportive materials with innovative architectural designs that integrate these components according to system-wide application needs. 15

  14. Evidence of probabilistic behaviour in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2008-01-01

    Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

  15. Pythoscape: a framework for generation of large protein similarity networks.

    Science.gov (United States)

    Barber, Alan E; Babbitt, Patricia C

    2012-11-01

    Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence-structure-function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.

  16. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

    Science.gov (United States)

    Mistry, Divya; Wise, Roger P; Dickerson, Julie A

    2017-01-01

    Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

  17. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  18. Integral UBL domain proteins: a family of proteasome interacting proteins

    DEFF Research Database (Denmark)

    Hartmann-Petersen, Rasmus; Gordon, Colin

    2004-01-01

    The family of ubiquitin-like (UBL) domain proteins (UDPs) comprises a conserved group of proteins involved in a multitude of different cellular activities. However, recent studies on UBL-domain proteins indicate that these proteins appear to share a common property in their ability to interact...

  19. Modeling of axonal endoplasmic reticulum network by spastic paraplegia proteins.

    Science.gov (United States)

    Yalçın, Belgin; Zhao, Lu; Stofanko, Martin; O'Sullivan, Niamh C; Kang, Zi Han; Roost, Annika; Thomas, Matthew R; Zaessinger, Sophie; Blard, Olivier; Patto, Alex L; Sohail, Anood; Baena, Valentina; Terasaki, Mark; O'Kane, Cahir J

    2017-07-25

    Axons contain a smooth tubular endoplasmic reticulum (ER) network that is thought to be continuous with ER throughout the neuron; the mechanisms that form this axonal network are unknown. Mutations affecting reticulon or REEP proteins, with intramembrane hairpin domains that model ER membranes, cause an axon degenerative disease, hereditary spastic paraplegia (HSP). We show that Drosophila axons have a dynamic axonal ER network, which these proteins help to model. Loss of HSP hairpin proteins causes ER sheet expansion, partial loss of ER from distal motor axons, and occasional discontinuities in axonal ER. Ultrastructural analysis reveals an extensive ER network in axons, which shows larger and fewer tubules in larvae that lack reticulon and REEP proteins, consistent with loss of membrane curvature. Therefore HSP hairpin-containing proteins are required for shaping and continuity of axonal ER, thus suggesting roles for ER modeling in axon maintenance and function.

  20. The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

    Science.gov (United States)

    Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

    2010-01-01

    The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

  1. AN OVERVIEW OF UMTS AND WIMAX NETWORKS INTEGRATION

    Directory of Open Access Journals (Sweden)

    Dewi Wirastuti

    2010-12-01

    Full Text Available This paper gives an overview of the network integration of UMTS (Universal Mobile TelecommunicationSystem with WiMAX (Wordwide Interoperability of Microwave Access. A few proposed interworking solutionsand seamless integration of both networks are explained. The best architecture and key procedures that will enablethe integration both networks and handover mechanism for the seamless mobility are presented. Considering thetrend of the current network evolution, which is the convergence between the telecommunications and broadcastworlds, an integration of mobile WiMAX with present 2G, 2.5G or 3G accesses into a homogeneous architecturegoes a long way to achieve the reality of mobile broadband networks. With the advent of mobile WiMAX, a mobilebroadband wireless access solution and based on all-IP (Internet Protocol based OFDMA (Orthogonal FrequencyDivision Multiple Access technology, an UMTS-WiMAX

  2. An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

    Directory of Open Access Journals (Sweden)

    Liu Lili

    2013-06-01

    Full Text Available Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs, protein-protein interactions (PPIs and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

  3. Reconstruction of biological networks based on life science data integration

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

    Full Text Available For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  4. Reconstruction of biological networks based on life science data integration.

    Science.gov (United States)

    Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf

    2010-10-27

    For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  5. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  6. Analysis of protein folds using protein contact networks

    Indian Academy of Sciences (India)

    is a well-recognized classification system of proteins, which is based on manual in- ... can easily correspond to the information in the 2D matrix. ..... [7] U K Muppirala and Zhijun Li, Protein Engineering, Design & Selection 19, 265 (2006).

  7. Scoring protein relationships in functional interaction networks predicted from sequence data.

    Directory of Open Access Journals (Sweden)

    Gaston K Mazandu

    Full Text Available UNLABELLED: The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. AVAILABILITY: Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.

  8. Transition Towards An Integrated Network Organisation

    DEFF Research Database (Denmark)

    Mykhaylenko, Alona; Wæhrens, Brian Vejrum

    2016-01-01

    , with particular attention to the role played by the home base (HB) organisation in this evolution. The research is focused on the intra-organisational global network and uses a longitudinal single-case study. Findings depict the transition as being enabled by the interaction between HB knowledge about......Management of internationally dispersed and networked operations has been in the focus of research attention. However, the existing studies underestimate the incrementality of changes shaping such organisations. This work investigates how organisations evolve into network structures...... the organization, and its reconfiguration decisions. Implications are also discussed regarding process drivers and the role of HB in the network organization....

  9. Visualization of protein interaction networks: problems and solutions

    Directory of Open Access Journals (Sweden)

    Agapito Giuseppe

    2013-01-01

    Full Text Available Abstract Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins and edges (interactions, the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i technology, i.e. availability/license of the software and supported OS (Operating System platforms; (ii interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the

  10. A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

    Science.gov (United States)

    Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

    2017-09-01

    Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Evolution of a protein domain interaction network

    International Nuclear Information System (INIS)

    Li-Feng, Gao; Jian-Jun, Shi; Shan, Guan

    2010-01-01

    In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows: (i) there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; (ii) few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; (iii) in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases. (general)

  12. CNNcon: improved protein contact maps prediction using cascaded neural networks.

    Directory of Open Access Journals (Sweden)

    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  13. NatalieQ: A web server for protein-protein interaction network querying

    NARCIS (Netherlands)

    El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

  14. Optimal neural networks for protein-structure prediction

    International Nuclear Information System (INIS)

    Head-Gordon, T.; Stillinger, F.H.

    1993-01-01

    The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

  15. Diffusion of Integral Membrane Proteins in Protein-Rich Membranes

    DEFF Research Database (Denmark)

    Javanainen, Matti; Martinez-Seara, Hector; Metzler, Ralf

    2017-01-01

    of being protein-poor, native cell membranes are extremely crowded with proteins. On the basis of extensive molecular simulations, we here demonstrate that protein crowding of the membrane at physiological levels leads to deviations from the SD relation and to the emergence of a stronger Stokes......-like dependence D ∝ 1/R. We propose that this 1/R law mainly arises due to geometrical factors: smaller proteins are able to avoid confinement effects much better than their larger counterparts. The results highlight that the lateral dynamics in the crowded setting found in native membranes is radically different......The lateral diffusion of embedded proteins along lipid membranes in protein-poor conditions has been successfully described in terms of the Saffman-Delbrück (SD) model, which predicts that the protein diffusion coefficient D is weakly dependent on its radius R as D ∝ ln(1/R). However, instead...

  16. Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

    Science.gov (United States)

    Maguire, Jack B; Boyken, Scott E; Baker, David; Kuhlman, Brian

    2018-05-08

    Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

  17. Network based approaches reveal clustering in protein point patterns

    Science.gov (United States)

    Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

    2014-03-01

    Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

  18. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

    2017-01-01

    is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data...

  19. Topological properties of complex networks in protein structures

    Science.gov (United States)

    Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

    2014-03-01

    We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

  20. In silico modeling of the yeast protein and protein family interaction network

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2004-03-01

    Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

  1. Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

    International Nuclear Information System (INIS)

    Pei, Guangsheng; Chen, Lei; Wang, Jiangxin; Qiao, Jianjun; Zhang, Weiwen

    2014-01-01

    Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles, which need to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein–protein interaction network. Proteins with statistically higher topological overlap in the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a weighted gene co-expression network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation, and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.

  2. Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

    Energy Technology Data Exchange (ETDEWEB)

    Pei, Guangsheng; Chen, Lei; Wang, Jiangxin; Qiao, Jianjun, E-mail: jianjunq@tju.edu.cn; Zhang, Weiwen, E-mail: jianjunq@tju.edu.cn [Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin (China); Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin (China); SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering, Tianjin (China)

    2014-11-03

    Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles, which need to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein–protein interaction network. Proteins with statistically higher topological overlap in the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a weighted gene co-expression network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation, and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.

  3. Integrated topology optimisation of multi-energy networks

    NARCIS (Netherlands)

    Mazairac, L.A.J.; Salenbien, R.; Vanhoudt, D.; Desmedt, J.; Vries, de B.

    2015-01-01

    Multi-carrier hybrid energy distribution net- works provide flexibility in case of network malfunctions, energy shortages and price fluctuations through energy conversion and storage. Therefore hybrid networks can cope with large-scale integration of distributed and intermittent renewable energy

  4. Analysis of Basic Transmission Networks for Integrated Ship Control Systems

    DEFF Research Database (Denmark)

    Hansen, T.N.; Granum-Jensen, M.

    1993-01-01

    Description of a computer network for Integrated Ship Control Systems which is going to be developed as part of an EC-project. Today equipment of different make are not able to communicate with each other because most often each supplier of ISC systems has got their own proprietary network.....

  5. FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

    Directory of Open Access Journals (Sweden)

    A. A. Doudkin

    2015-01-01

    Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

  6. Seamless integrated network system for wireless communication systems

    NARCIS (Netherlands)

    Wu, Gang; Mizuno, Mitsuhiko; Hase, Yoshihiro; Havinga, Paul J.M.

    2006-01-01

    To create a network that connects a plurality of wireless communication systems to create optimal systems for various environments, and that seamlessly integrates the resulting systems together in order to provide more efficient and advanced service in general. A network system that can seamlessly

  7. Seamless integrated network system for wireless communication systems

    NARCIS (Netherlands)

    Wu, Gang; Mizuno, Mitsuhiko; Hase, Yoshihiro; Havinga, Paul J.M.

    2002-01-01

    To create a network that connects a plurality of wireless communication systems to create optimal systems for various environments, and that seamlessly integrates the resulting systems together in order to provide more efficient and advanced service in general. A network system that can seamlessly

  8. Network analysis of epidermal growth factor signaling using integrated genomic, proteomic and phosphorylation data.

    Directory of Open Access Journals (Sweden)

    Katrina M Waters

    Full Text Available To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

  9. Network Analysis of Epidermal Growth Factor Signaling using Integrated Genomic, Proteomic and Phosphorylation Data

    Energy Technology Data Exchange (ETDEWEB)

    Waters, Katrina M.; Liu, Tao; Quesenberry, Ryan D.; Willse, Alan R.; Bandyopadhyay, Somnath; Kathmann, Loel E.; Weber, Thomas J.; Smith, Richard D.; Wiley, H. S.; Thrall, Brian D.

    2012-03-29

    To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

  10. Cytoscape: a software environment for integrated models of biomolecular interaction networks.

    Science.gov (United States)

    Shannon, Paul; Markiel, Andrew; Ozier, Owen; Baliga, Nitin S; Wang, Jonathan T; Ramage, Daniel; Amin, Nada; Schwikowski, Benno; Ideker, Trey

    2003-11-01

    Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

  11. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

    Science.gov (United States)

    CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

    2016-01-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

  12. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  13. Advanced path sampling of the kinetic network of small proteins

    NARCIS (Netherlands)

    Du, W.

    2014-01-01

    This thesis is focused on developing advanced path sampling simulation methods to study protein folding and unfolding, and to build kinetic equilibrium networks describing these processes. In Chapter 1 the basic knowledge of protein structure and folding theories were introduced and a brief overview

  14. Mining protein interactomes to improve their reliability and support the advancement of network medicine

    KAUST Repository

    Alanis Lobato, Gregorio

    2015-09-23

    High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.

  15. Mining protein interactomes to improve their reliability and support the advancement of network medicine

    Directory of Open Access Journals (Sweden)

    Gregorio eAlanis-Lobato

    2015-09-01

    Full Text Available High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease aetiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.

  16. AS Migration and Optimization of the Power Integrated Data Network

    Science.gov (United States)

    Zhou, Junjie; Ke, Yue

    2018-03-01

    In the transformation process of data integration network, the impact on the business has always been the most important reference factor to measure the quality of network transformation. With the importance of the data network carrying business, we must put forward specific design proposals during the transformation, and conduct a large number of demonstration and practice to ensure that the transformation program meets the requirements of the enterprise data network. This paper mainly demonstrates the scheme of over-migrating point-to-point access equipment in the reconstruction project of power data comprehensive network to migrate the BGP autonomous domain to the specified domain defined in the industrial standard, and to smooth the intranet OSPF protocol Migration into ISIS agreement. Through the optimization design, eventually making electric power data network performance was improved on traffic forwarding, traffic forwarding path optimized, extensibility, get larger, lower risk of potential loop, the network stability was improved, and operational cost savings, etc.

  17. Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

    Science.gov (United States)

    DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

    2015-06-01

    Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Integration of Satellite and Terrestrial Networks at JPL

    Science.gov (United States)

    Pinck, D. S.

    1995-01-01

    This presentation focuses on the activities at JPL on the integration of satellite and terrestrial networks for mobile and personal communications. Activities fall into two categories: 1)advanced systems work, and 2)laboratory and field experimentation. Results of a workshop held at JPL on PCS integration and interoperability will be presented. Experiments will be described.

  19. Integrated Circuit Chip Improves Network Efficiency

    Science.gov (United States)

    2008-01-01

    Prior to 1999 and the development of SpaceWire, a standard for high-speed links for computer networks managed by the European Space Agency (ESA), there was no high-speed communications protocol for flight electronics. Onboard computers, processing units, and other electronics had to be designed for individual projects and then redesigned for subsequent projects, which increased development periods, costs, and risks. After adopting the SpaceWire protocol in 2000, NASA implemented the standard on the Swift mission, a gamma ray burst-alert telescope launched in November 2004. Scientists and developers on the James Webb Space Telescope further developed the network version of SpaceWire. In essence, SpaceWire enables more science missions at a lower cost, because it provides a standard interface between flight electronics components; new systems need not be custom built to accommodate individual missions, so electronics can be reused. New protocols are helping to standardize higher layers of computer communication. Goddard Space Flight Center improved on the ESA-developed SpaceWire by enabling standard protocols, which included defining quality of service and supporting plug-and-play capabilities. Goddard upgraded SpaceWire to make the routers more efficient and reliable, with features including redundant cables, simultaneous discrete broadcast pulses, prevention of network blockage, and improved verification. Redundant cables simplify management because the user does not need to worry about which connection is available, and simultaneous broadcast signals allow multiple users to broadcast low-latency side-band signal pulses across the network using the same resources for data communication. Additional features have been added to the SpaceWire switch to prevent network blockage so that more robust networks can be designed. Goddard s verification environment for the link-and-switch implementation continuously randomizes and tests different parts, constantly anticipating

  20. Integrated Network Analysis and Effective Tools in Plant Systems Biology

    Directory of Open Access Journals (Sweden)

    Atsushi eFukushima

    2014-11-01

    Full Text Available One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1 network visualization tools, (2 pathway analyses, (3 genome-scale metabolic reconstruction, and (4 the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

  1. Semantic web for integrated network analysis in biomedicine.

    Science.gov (United States)

    Chen, Huajun; Ding, Li; Wu, Zhaohui; Yu, Tong; Dhanapalan, Lavanya; Chen, Jake Y

    2009-03-01

    The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

  2. Momentum integral network method for thermal-hydraulic transient analysis

    International Nuclear Information System (INIS)

    Van Tuyle, G.J.

    1983-01-01

    A new momentum integral network method has been developed, and tested in the MINET computer code. The method was developed in order to facilitate the transient analysis of complex fluid flow and heat transfer networks, such as those found in the balance of plant of power generating facilities. The method employed in the MINET code is a major extension of a momentum integral method reported by Meyer. Meyer integrated the momentum equation over several linked nodes, called a segment, and used a segment average pressure, evaluated from the pressures at both ends. Nodal mass and energy conservation determined nodal flows and enthalpies, accounting for fluid compression and thermal expansion

  3. Romanian network for structural integrity assessment of nuclear components

    International Nuclear Information System (INIS)

    Roth, Maria; Constantinescu, Dan Mihai; Brad, Sebastian; Ducu, Catalin

    2008-01-01

    Full text: Based of the Romanian option to develop and operate nuclear facilities, using as model the networks created at European level and taking into account the international importance of the structural integrity assessments for lifetime extension of the nuclear components, a national Project started since 2005 in the framework of the National Program 'Research of Excellence', Modulus I 2006-2008, managed by the Ministry of Education and Research. Entitled 'Integrated Network for Structural Integrity Monitoring of Critical Components in Nuclear Facilities', with the acronym RIMIS, the Project had two main objectives: - to elaborate a procedure applicable to the structural integrity assessment of the critical components used in Romanian nuclear facilities; - to integrate the national networking in a similar one, at European level, to enhance the scientific significance of Romanian R and D organizations as well as to increase the contribution to solving one of the major issue of the nuclear field. The paper aimed to present the activities performed in the Romanian institutes, involved in the Project, the final results obtained as part of the R and D activities, including experimental, theoretical and modeling ones regarding structural integrity assessment of nuclear components employed in CANDU type reactors. Also the activity carried out in the framework of the NULIFE network, created at European level of the FP6 Program and sustained by the RIMIS network will be described. (authors)

  4. A service integration platform for collaborative networks

    NARCIS (Netherlands)

    Osorio, A. L.; Afsarmanesh, H.; Camarinha-Matos, L.M.

    2011-01-01

    Integrated manufacturing constitutes a complex system made of heterogeneous information and control subsystems. Those subsystems are not designed to the cooperation. Typically each subsystem automates specific processes, and establishes closed application domains, therefore it is very difficult to

  5. Message Integrity Model for Wireless Sensor Networks

    Science.gov (United States)

    Qleibo, Haider W.

    2009-01-01

    WSNs are susceptible to a variety of attacks. These attacks vary in the way they are performed and executed; they include but not limited to node capture, physical tampering, denial of service, and message alteration. It is of paramount importance to protect gathered data by WSNs and defend the network against illegal access and malicious…

  6. Value Creation Through Integrated Networks and Convergence

    Energy Technology Data Exchange (ETDEWEB)

    De Martini, Paul; Taft, Jeffrey D.

    2015-04-01

    Customer adoption of distributed energy resources and public policies are driving changes in the uses of the distribution system. A system originally designed and built for one-way energy flows from central generating facilities to end-use customers is now experiencing injections of energy from customers anywhere on the grid and frequent reversals in the direction of energy flow. In response, regulators and utilities are re-thinking the design and operations of the grid to create more open and transactive electric networks. This evolution has the opportunity to unlock significant value for customers and utilities. Alternatively, failure to seize this potential may instead lead to an erosion of value if customers seek to defect and disconnect from the system. This paper will discuss how current grid modernization investments may be leveraged to create open networks that increase value through the interaction of intelligent devices on the grid and prosumerization of customers. Moreover, even greater value can be realized through the synergistic effects of convergence of multiple networks. This paper will highlight examples of the emerging nexus of non-electric networks with electricity.

  7. Integrating job scheduling and constrained network routing

    DEFF Research Database (Denmark)

    Gamst, Mette

    2010-01-01

    This paper examines the NP-hard problem of scheduling jobs on resources such that the overall profit of executed jobs is maximized. Job demand must be sent through a constrained network to the resource before execution can begin. The problem has application in grid computing, where a number...

  8. Meditation is associated with increased brain network integration.

    Science.gov (United States)

    van Lutterveld, Remko; van Dellen, Edwin; Pal, Prasanta; Yang, Hua; Stam, Cornelis Jan; Brewer, Judson

    2017-09-01

    This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4-8 Hz), alpha (8-13 Hz) and lower beta (13-20 Hz) frequency bands. Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P = 0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P = 0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P = 0.016 and P = 0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using

  9. Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

    Science.gov (United States)

    Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

    2010-07-01

    The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

  10. Emergence of modularity and disassortativity in protein-protein interaction networks.

    Science.gov (United States)

    Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

    2010-12-01

    In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

  11. Integrative analysis of many weighted co-expression networks using tensor computation.

    Directory of Open Access Journals (Sweden)

    Wenyuan Li

    2011-06-01

    Full Text Available The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks.

  12. Computational Modeling of Complex Protein Activity Networks

    NARCIS (Netherlands)

    Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude

    2017-01-01

    Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a

  13. Protein diffusion in photopolymerized poly(ethylene glycol) hydrogel networks

    International Nuclear Information System (INIS)

    Engberg, Kristin; Frank, Curtis W

    2011-01-01

    In this study, protein diffusion through swollen hydrogel networks prepared from end-linked poly(ethylene glycol)-diacrylate (PEG-DA) was investigated. Hydrogels were prepared via photopolymerization from PEG-DA macromonomer solutions of two molecular weights, 4600 Da and 8000 Da, with three initial solid contents: 20, 33 and 50 wt/wt% PEG. Diffusion coefficients for myoglobin traveling across the hydrogel membrane were determined for all PEG network compositions. The diffusion coefficient depended on PEG molecular weight and initial solid content, with the slowest diffusion occurring through lower molecular weight, high-solid-content networks (D gel = 0.16 ± 0.02 x 10 -8 cm 2 s -1 ) and the fastest diffusion occurring through higher molecular weight, low-solid-content networks (D gel = 11.05 ± 0.43 x 10 -8 cm 2 s -1 ). Myoglobin diffusion coefficients increased linearly with the increase of water content within the hydrogels. The permeability of three larger model proteins (horseradish peroxidase, bovine serum albumin and immunoglobulin G) through PEG(8000) hydrogel membranes was also examined, with the observation that globular molecules as large as 10.7 nm in hydrodynamic diameter can diffuse through the PEG network. Protein diffusion coefficients within the PEG hydrogels ranged from one to two orders of magnitude lower than the diffusion coefficients in free water. Network defects were determined to be a significant contributing factor to the observed protein diffusion.

  14. Integrated Adversarial Network Theory (iANT)

    Science.gov (United States)

    2011-07-01

    elite networks and governance changes in the 1980s. American Journal of Sociology, 103(1): 1-37. DiMaggio, P. 1986. Structural analysis of...Interorganization contagion in corporate philanthropy . Administrative Science Quarterly, 36(1): 88-105. Gargiulo, M., & Benassi, M. 1999. The dark...1): 7 1-84. Useem, M. 1979. The social organization ofthe American business elite and participation of corporation directors in the governance of

  15. Networks as integrated in research methodologies in PER

    DEFF Research Database (Denmark)

    Bruun, Jesper

    2016-01-01

    of using networks to create insightful maps of learning discussions. To conclude, I argue that conceptual blending is a powerful framework for constructing "mixed methods" methodologies that may integrate diverse theories and other methodologies with network methodologies.......In recent years a number of researchers within the PER community have started using network analysis as a new methodology to extend our understanding of teaching and learning physics by viewing these as complex systems. In this paper, I give examples of social, cognitive, and action mapping...... networks and how they can be analyzed. In so doing I show how a network can be methodologically described as a set of relations between a set of entities, and how a network can be characterized and analyzed as a mathematical object. Then, as an illustrative example, I discuss a relatively new example...

  16. A tensegrity model for hydrogen bond networks in proteins

    Directory of Open Access Journals (Sweden)

    Robert P. Bywater

    2017-05-01

    Full Text Available Hydrogen-bonding networks in proteins considered as structural tensile elements are in balance separately from any other stabilising interactions that may be in operation. The hydrogen bond arrangement in the network is reminiscent of tensegrity structures in architecture and sculpture. Tensegrity has been discussed before in cells and tissues and in proteins. In contrast to previous work only hydrogen bonds are studied here. The other interactions within proteins are either much stronger − covalent bonds connecting the atoms in the molecular skeleton or weaker forces like the so-called hydrophobic interactions. It has been demonstrated that the latter operate independently from hydrogen bonds. Each category of interaction must, if the protein is to have a stable structure, balance out. The hypothesis here is that the entire hydrogen bond network is in balance without any compensating contributions from other types of interaction. For sidechain-sidechain, sidechain-backbone and backbone-backbone hydrogen bonds in proteins, tensegrity balance (“closure” is required over the entire length of the polypeptide chain that defines individually folding units in globular proteins (“domains” as well as within the repeating elements in fibrous proteins that consist of extended chain structures. There is no closure to be found in extended structures that do not have repeating elements. This suggests an explanation as to why globular domains, as well as the repeat units in fibrous proteins, have to have a defined number of residues. Apart from networks of sidechain-sidechain hydrogen bonds there are certain key points at which this closure is achieved in the sidechain-backbone hydrogen bonds and these are associated with demarcation points at the start or end of stretches of secondary structure. Together, these three categories of hydrogen bond achieve the closure that is necessary for the stability of globular protein domains as well as repeating

  17. A tensegrity model for hydrogen bond networks in proteins.

    Science.gov (United States)

    Bywater, Robert P

    2017-05-01

    Hydrogen-bonding networks in proteins considered as structural tensile elements are in balance separately from any other stabilising interactions that may be in operation. The hydrogen bond arrangement in the network is reminiscent of tensegrity structures in architecture and sculpture. Tensegrity has been discussed before in cells and tissues and in proteins. In contrast to previous work only hydrogen bonds are studied here. The other interactions within proteins are either much stronger - covalent bonds connecting the atoms in the molecular skeleton or weaker forces like the so-called hydrophobic interactions. It has been demonstrated that the latter operate independently from hydrogen bonds. Each category of interaction must, if the protein is to have a stable structure, balance out. The hypothesis here is that the entire hydrogen bond network is in balance without any compensating contributions from other types of interaction. For sidechain-sidechain, sidechain-backbone and backbone-backbone hydrogen bonds in proteins, tensegrity balance ("closure") is required over the entire length of the polypeptide chain that defines individually folding units in globular proteins ("domains") as well as within the repeating elements in fibrous proteins that consist of extended chain structures. There is no closure to be found in extended structures that do not have repeating elements. This suggests an explanation as to why globular domains, as well as the repeat units in fibrous proteins, have to have a defined number of residues. Apart from networks of sidechain-sidechain hydrogen bonds there are certain key points at which this closure is achieved in the sidechain-backbone hydrogen bonds and these are associated with demarcation points at the start or end of stretches of secondary structure. Together, these three categories of hydrogen bond achieve the closure that is necessary for the stability of globular protein domains as well as repeating elements in fibrous proteins.

  18. Supervised maximum-likelihood weighting of composite protein networks for complex prediction

    Directory of Open Access Journals (Sweden)

    Yong Chern Han

    2012-12-01

    Full Text Available Abstract Background Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. Results We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. Conclusions Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to

  19. Specificity and evolvability in eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2007-02-01

    Full Text Available Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

  20. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Sandip Chakraborty

    2016-01-01

    Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

  1. Differential neural network configuration during human path integration

    Science.gov (United States)

    Arnold, Aiden E. G. F; Burles, Ford; Bray, Signe; Levy, Richard M.; Iaria, Giuseppe

    2014-01-01

    Path integration is a fundamental skill for navigation in both humans and animals. Despite recent advances in unraveling the neural basis of path integration in animal models, relatively little is known about how path integration operates at a neural level in humans. Previous attempts to characterize the neural mechanisms used by humans to visually path integrate have suggested a central role of the hippocampus in allowing accurate performance, broadly resembling results from animal data. However, in recent years both the central role of the hippocampus and the perspective that animals and humans share similar neural mechanisms for path integration has come into question. The present study uses a data driven analysis to investigate the neural systems engaged during visual path integration in humans, allowing for an unbiased estimate of neural activity across the entire brain. Our results suggest that humans employ common task control, attention and spatial working memory systems across a frontoparietal network during path integration. However, individuals differed in how these systems are configured into functional networks. High performing individuals were found to more broadly express spatial working memory systems in prefrontal cortex, while low performing individuals engaged an allocentric memory system based primarily in the medial occipito-temporal region. These findings suggest that visual path integration in humans over short distances can operate through a spatial working memory system engaging primarily the prefrontal cortex and that the differential configuration of memory systems recruited by task control networks may help explain individual biases in spatial learning strategies. PMID:24808849

  2. Integration of SPS with utility system networks

    Energy Technology Data Exchange (ETDEWEB)

    Kaupang, B.M.

    1980-06-01

    This paper will discuss the integration of SPS power in electric utility power systems. Specifically treated will be the nature of the power output variations from the spacecraft to the rectenna, the operational characteristics of the rectenna power and the impacts on the electric utility system from utilizing SPS power to serve part of the system load.

  3. Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches

    Directory of Open Access Journals (Sweden)

    Jay Krishna Thakur

    2015-08-01

    Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.

  4. Functional brain networks underlying detection and integration of disconfirmatory evidence.

    Science.gov (United States)

    Lavigne, Katie M; Metzak, Paul D; Woodward, Todd S

    2015-05-15

    Processing evidence that disconfirms a prior interpretation is a fundamental aspect of belief revision, and has clear social and clinical relevance. This complex cognitive process requires (at minimum) an alerting stage and an integration stage, and in the current functional magnetic resonance imaging (fMRI) study, we used multivariate analysis methodology on two datasets in an attempt to separate these sequentially-activated cognitive stages and link them to distinct functional brain networks. Thirty-nine healthy participants completed one of two versions of an evidence integration experiment involving rating two consecutive animal images, both of which consisted of two intact images of animal faces morphed together at different ratios (e.g., 70/30 bird/dolphin followed by 10/90 bird/dolphin). The two versions of the experiment differed primarily in terms of stimulus presentation and timing, which facilitated functional interpretation of brain networks based on differences in the hemodynamic response shapes between versions. The data were analyzed using constrained principal component analysis for fMRI (fMRI-CPCA), which allows distinct, simultaneously active task-based networks to be separated, and these were interpreted using both temporal (task-based hemodynamic response shapes) and spatial (dominant brain regions) information. Three networks showed increased activity during integration of disconfirmatory relative to confirmatory evidence: (1) a network involved in alerting to the requirement to revise an interpretation, identified as the salience network (dorsal anterior cingulate cortex and bilateral insula); (2) a sensorimotor response-related network (pre- and post-central gyri, supplementary motor area, and thalamus); and (3) an integration network involving rostral prefrontal, orbitofrontal and posterior parietal cortex. These three networks were staggered in their peak activity (alerting, responding, then integrating), but at certain time points (e

  5. The membrane stress response buffers lethal effects of lipid disequilibrium by reprogramming the protein homeostasis network.

    Science.gov (United States)

    Thibault, Guillaume; Shui, Guanghou; Kim, Woong; McAlister, Graeme C; Ismail, Nurzian; Gygi, Steven P; Wenk, Markus R; Ng, Davis T W

    2012-10-12

    Lipid composition can differ widely among organelles and even between leaflets of a membrane. Lipid homeostasis is critical because disequilibrium can have disease outcomes. Despite their importance, mechanisms maintaining lipid homeostasis remain poorly understood. Here, we establish a model system to study the global effects of lipid imbalance. Quantitative lipid profiling was integral to monitor changes to lipid composition and for system validation. Applying global transcriptional and proteomic analyses, a dramatically altered biochemical landscape was revealed from adaptive cells. The resulting composite regulation we term the "membrane stress response" (MSR) confers compensation, not through restoration of lipid composition, but by remodeling the protein homeostasis network. To validate its physiological significance, we analyzed the unfolded protein response (UPR), one facet of the MSR and a key regulator of protein homeostasis. We demonstrate that the UPR maintains protein biogenesis, quality control, and membrane integrity-functions otherwise lethally compromised in lipid dysregulated cells. Copyright © 2012 Elsevier Inc. All rights reserved.

  6. Droplet networks with incorporated protein diodes show collective properties

    Science.gov (United States)

    Maglia, Giovanni; Heron, Andrew J.; Hwang, William L.; Holden, Matthew A.; Mikhailova, Ellina; Li, Qiuhong; Cheley, Stephen; Bayley, Hagan

    2009-07-01

    Recently, we demonstrated that submicrolitre aqueous droplets submerged in an apolar liquid containing lipid can be tightly connected by means of lipid bilayers to form networks. Droplet interface bilayers have been used for rapid screening of membrane proteins and to form asymmetric bilayers with which to examine the fundamental properties of channels and pores. Networks, meanwhile, have been used to form microscale batteries and to detect light. Here, we develop an engineered protein pore with diode-like properties that can be incorporated into droplet interface bilayers in droplet networks to form devices with electrical properties including those of a current limiter, a half-wave rectifier and a full-wave rectifier. The droplet approach, which uses unsophisticated components (oil, lipid, salt water and a simple pore), can therefore be used to create multidroplet networks with collective properties that cannot be produced by droplet pairs.

  7. Constructing an integrated gene similarity network for the identification of disease genes.

    Science.gov (United States)

    Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

    2017-09-20

    Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

  8. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  9. Integrating Subjective Trust into Networked Infrastructures

    Science.gov (United States)

    2009-04-22

    architectural framework for hexperimenting wit trust. • Use of semantic technologies incorporated into h b id b d t t t ta y r - ase rus managemen ...Language for Operation PI Persistent Identifier PILOW P i t t Id tifi T blers s en en er a es PINL Persistent Identifier Networking Layer SBIR Small...Investigate and propose an architecture to determine/measure and convey th t t l l f th i l t ie rus eve o e var ous e emen s n a distributed or

  10. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  11. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A; Kellis, Manolis

    2012-07-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

  12. Integration of SPS with utility system networks

    Science.gov (United States)

    Kaupang, B. M.

    1980-01-01

    The integration of Satellite Power System (SPS) power in electric utility power systems is discussed. Specifically, the nature of the power output variations from the spacecraft to the rectenna, the operational characteristics of the rectenna power, and the impacts on the electric utility system from utilizing SPS power to serve part of the system load are treated. It is concluded that if RF beam control is an acceptable method for power control, and that the site distribution of SPS rectennas do not cause a very high local penetration (40 to 50%), SPS may be integrated into electric utility system with a few negative impacts. Increased regulating duty on the conventional generation, and a potential impact on system reliability for SPS penetration in excess of about 25% appear to be two areas of concern.

  13. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    Directory of Open Access Journals (Sweden)

    Natalie Berestovsky

    Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them

  14. Integrated computer network high-speed parallel interface

    International Nuclear Information System (INIS)

    Frank, R.B.

    1979-03-01

    As the number and variety of computers within Los Alamos Scientific Laboratory's Central Computer Facility grows, the need for a standard, high-speed intercomputer interface has become more apparent. This report details the development of a High-Speed Parallel Interface from conceptual through implementation stages to meet current and future needs for large-scle network computing within the Integrated Computer Network. 4 figures

  15. Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

    KAUST Repository

    Chowdhary, Rajesh

    2012-04-06

    Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

  16. Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

    KAUST Repository

    Chowdhary, Rajesh; Tan, Sin Lam; Zhang, Jinfeng; Karnik, Shreyas; Bajic, Vladimir B.; Liu, Jun S.

    2012-01-01

    Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

  17. Integrated Environment for Ubiquitous Healthcare and Mobile IPv6 Networks

    Science.gov (United States)

    Cagalaban, Giovanni; Kim, Seoksoo

    The development of Internet technologies based on the IPv6 protocol will allow real-time monitoring of people with health deficiencies and improve the independence of elderly people. This paper proposed a ubiquitous healthcare system for the personalized healthcare services with the support of mobile IPv6 networks. Specifically, this paper discusses the integration of ubiquitous healthcare and wireless networks and its functional requirements. This allow an integrated environment where heterogeneous devices such a mobile devices and body sensors can continuously monitor patient status and communicate remotely with healthcare servers, physicians, and family members to effectively deliver healthcare services.

  18. Integrated resource management for Hybrid Optical Wireless (HOW) networks

    DEFF Research Database (Denmark)

    Yan, Ying; Yu, Hao; Wessing, Henrik

    2009-01-01

    Efficient utilization of available bandwidth over hybrid optical wireless networks is a critical issue, especially for multimedia applications with high data rates and stringent Quality of Service (QoS) requirements. In this paper, we propose an integrated resource management including an enhanced...... resource sharing scheme and an integrated admission control scheme for the hybrid optical wireless networks. It provides QoS guarantees for connections through both optical and wireless domain. Simulation results show that our proposed scheme improves QoS performances in terms of high throughput and low...

  19. Network integration of distributed power generation

    Science.gov (United States)

    Dondi, Peter; Bayoumi, Deia; Haederli, Christoph; Julian, Danny; Suter, Marco

    The world-wide move to deregulation of the electricity and other energy markets, concerns about the environment, and advances in renewable and high efficiency technologies has led to major emphasis being placed on the use of small power generation units in a variety of forms. The paper reviews the position of distributed generation (DG, as these small units are called in comparison with central power plants) with respect to the installation and interconnection of such units with the classical grid infrastructure. In particular, the status of technical standards both in Europe and USA, possible ways to improve the interconnection situation, and also the need for decisions that provide a satisfactory position for the network operator (who remains responsible for the grid, its operation, maintenance and investment plans) are addressed.

  20. Integrated Telecom And Data Ring Network

    Science.gov (United States)

    Maaloe, Jens

    1985-08-01

    The NKT Tele-Ringnet is a combined digital telephone and data network based on a fibre optical ring topology. The purpose of NKT Tele-Ringnet is to carry telephone and data communication as a digital PABX does. However, the optical system is not based on individual lines to a central mother exchange. It is a ringnet with an 8 Mbit/s transmission rate interconnecting simple telephone concentrators. The advantage is that all the telephone and data traffic can be multidropped over a large campus. In addition the signals are carried by an optical dual fibre cable which has many advantages compared with conventional cobber cables, i.e. low attenuation, large noise immunity, no electromagnetic radiation, small size and low weight.

  1. Integrating Information Networks for Collective Planetary Stewardship

    Science.gov (United States)

    Tiwari, A.

    2016-12-01

    Responsible behaviour resulting from climate literacy in global environmental movement is limited to policy and planning institutions in the Global South, while remaining absent for ends-user. Thus, planetary stewardship exists only at earth system boundaries where pressures sink to the local scale while ethics remains afloat. Existing citizen participation is restricted within policy spheres, appearing synonymous to enforcements in social psychology. Much, accounted reason is that existing information mechanisms operate mostly through linear exchanges between institutions and users, therefore reinforcing only hierarchical relationships. This study discloses such relationships that contribute to broad networking gaps through information demand assessment of stakeholders in a dozen development projects based in South Asia. Two parameters widely used for this purpose are: a. Feedback: Ends-user feedback to improve consumption literacy of climate sensitive resources (through consumption displays, billing, advisory services ecolabelling, sensors) and, b. Institutional Policy: Rewarding punishing to enforce desired behaviour (subsidies, taxation). Research answered: 1. Who gets the information (Equity in Information Distribution)? As existing information publishing mechanisms are designed by and for analysts, 2. How information translates to climate action Transparency of Execution)? Findings suggested that climate goals manifested in economic policy, than environmental policy, have potential clear short-term benefits and costs, and coincide with people's economic goals Also grassroots roles for responsible behaviour are empowered with presence of end user information. Barier free climate communication process and decision making is ensured among multiplicity of stakeholders with often conflicting perspectives. Research finds significance where collaboration among information networks can better translate regional policies into local action for climate adaptation and

  2. Integration Of An MR Image Network Into A Clinical PACS

    Science.gov (United States)

    Ratib, Osman M.; Mankovich, Nicholas J.; Taira, Ricky K.; Cho, Paul S.; Huang, H. K.

    1988-06-01

    A direct link between a clinical pediatric PACS module and a FONAR MRI image network was implemented. The original MR network combines together the MR scanner, a remote viewing station and a central archiving station. The pediatric PACS directly connects to the archiving unit through an Ethernet TCP-IP network adhering to FONAR's protocol. The PACS communication software developed supports the transfer of patient studies and the patient information directly from the MR archive database to the pediatric PACS. In the first phase of our project we developed a package to transfer data between a VAX-111750 and the IBM PC I AT-based MR archive database through the Ethernet network. This system served as a model for PACS-to-modality network communication. Once testing was complete on this research network, the software and network hardware was moved to the clinical pediatric VAX for full PACS integration. In parallel to the direct transmission of digital images to the Pediatric PACS, a broadband communication system in video format was developed for real-time broadcasting of images originating from the MR console to 8 remote viewing stations distributed in the radiology department. These analog viewing stations allow the radiologists to directly monitor patient positioning and to select the scan levels during a patient examination from remote locations in the radiology department. This paper reports (1) the technical details of this implementation, (2) the merits of this network development scheme, and (3) the performance statistics of the network-to-PACS interface.

  3. Towards integrated crisis support of regional emergency networks.

    Science.gov (United States)

    Caro, D H

    1999-01-01

    Emergency and crisis management pose multidimensional information systems challenges for communities across North America. In the quest to reduce mortality and morbidity risks and to increase the level of crisis preparedness, regional emergency management networks have evolved. Integrated Crisis Support Systems (ICSS) are enabling information technologies that assist emergency managers by enhancing the ability to strategically manage and control these regional emergency networks efficiently and effectively. This article underscores the ICCS development, control and leadership issues and their promising implications for regional emergency management networks.

  4. NETWORK CULTURE - INTEGRAL PART OF NEW VALUES OF CIVIL SOCIETY

    OpenAIRE

    Vyacheslav Vladimirovich Sukhanov

    2014-01-01

    New technologies not only improve working conditions or communication, they are also bringing new values to  the society. This article discusses the concept of «network culture», which is now perceived by society as an integral part of values that can only exist in a civil society. We can research ( find)   this kind of society in modern time period in Russia. The article analyzes the meaning of communication, how to use it, development processes in network media.  Nowadays network culture an...

  5. Explicit integration with GPU acceleration for large kinetic networks

    International Nuclear Information System (INIS)

    Brock, Benjamin; Belt, Andrew; Billings, Jay Jay; Guidry, Mike

    2015-01-01

    We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. This orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems in various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.

  6. Explicit integration of extremely stiff reaction networks: partial equilibrium methods

    International Nuclear Information System (INIS)

    Guidry, M W; Hix, W R; Billings, J J

    2013-01-01

    In two preceding papers (Guidry et al 2013 Comput. Sci. Disc. 6 015001 and Guidry and Harris 2013 Comput. Sci. Disc. 6 015002), we have shown that when reaction networks are well removed from equilibrium, explicit asymptotic and quasi-steady-state approximations can give algebraically stabilized integration schemes that rival standard implicit methods in accuracy and speed for extremely stiff systems. However, we also showed that these explicit methods remain accurate but are no longer competitive in speed as the network approaches equilibrium. In this paper, we analyze this failure and show that it is associated with the presence of fast equilibration timescales that neither asymptotic nor quasi-steady-state approximations are able to remove efficiently from the numerical integration. Based on this understanding, we develop a partial equilibrium method to deal effectively with the approach to equilibrium and show that explicit asymptotic methods, combined with the new partial equilibrium methods, give an integration scheme that can plausibly deal with the stiffest networks, even in the approach to equilibrium, with accuracy and speed competitive with that of implicit methods. Thus we demonstrate that such explicit methods may offer alternatives to implicit integration of even extremely stiff systems and that these methods may permit integration of much larger networks than have been possible before in a number of fields. (paper)

  7. High throughput platforms for structural genomics of integral membrane proteins.

    Science.gov (United States)

    Mancia, Filippo; Love, James

    2011-08-01

    Structural genomics approaches on integral membrane proteins have been postulated for over a decade, yet specific efforts are lagging years behind their soluble counterparts. Indeed, high throughput methodologies for production and characterization of prokaryotic integral membrane proteins are only now emerging, while large-scale efforts for eukaryotic ones are still in their infancy. Presented here is a review of recent literature on actively ongoing structural genomics of membrane protein initiatives, with a focus on those aimed at implementing interesting techniques aimed at increasing our rate of success for this class of macromolecules. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Construction of functional linkage gene networks by data integration.

    Science.gov (United States)

    Linghu, Bolan; Franzosa, Eric A; Xia, Yu

    2013-01-01

    Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

  9. Protein interaction networks by proteome peptide scanning.

    Directory of Open Access Journals (Sweden)

    Christiane Landgraf

    2004-01-01

    Full Text Available A substantial proportion of protein interactions relies on small domains binding to short peptides in the partner proteins. Many of these interactions are relatively low affinity and transient, and they impact on signal transduction. However, neither the number of potential interactions mediated by each domain nor the degree of promiscuity at a whole proteome level has been investigated. We have used a combination of phage display and SPOT synthesis to discover all the peptides in the yeast proteome that have the potential to bind to eight SH3 domains. We first identified the peptides that match a relaxed consensus, as deduced from peptides selected by phage display experiments. Next, we synthesized all the matching peptides at high density on a cellulose membrane, and we probed them directly with the SH3 domains. The domains that we have studied were grouped by this approach into five classes with partially overlapping specificity. Within the classes, however, the domains display a high promiscuity and bind to a large number of common targets with comparable affinity. We estimate that the yeast proteome contains as few as six peptides that bind to the Abp1 SH3 domain with a dissociation constant lower than 100 microM, while it contains as many as 50-80 peptides with corresponding affinity for the SH3 domain of Yfr024c. All the targets of the Abp1 SH3 domain, identified by this approach, bind to the native protein in vivo, as shown by coimmunoprecipitation experiments. Finally, we demonstrate that this strategy can be extended to the analysis of the entire human proteome. We have developed an approach, named WISE (whole interactome scanning experiment, that permits rapid and reliable identification of the partners of any peptide recognition module by peptide scanning of a proteome. Since the SPOT synthesis approach is semiquantitative and provides an approximation of the dissociation constants of the several thousands of interactions that are

  10. A network analysis of leadership theory : the infancy of integration.

    OpenAIRE

    Meuser, J. D.; Gardner, W. L.; Dinh, J. E.; Hu, J.; Liden, R. C.; Lord, R. G.

    2016-01-01

    We investigated the status of leadership theory integration by reviewing 14 years of published research (2000 through 2013) in 10 top journals (864 articles). The authors of these articles examined 49 leadership approaches/theories, and in 293 articles, 3 or more of these leadership approaches were included in their investigations. Focusing on these articles that reflected relatively extensive integration, we applied an inductive approach and used graphic network analysis as a guide for drawi...

  11. A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks

    NARCIS (Netherlands)

    Blankendaal, Romy; Parinussa, Sarah; Treur, Jan

    2016-01-01

    This paper introduces an integrated temporal-causal model for dynamics in social networks addressing the contagion principle by which states are affected mutually, and both the homophily principle and the more-becomes-more principle by which connections are adapted over time. The integrated model

  12. P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

    Science.gov (United States)

    Young-Rae Cho; Yanan Xin; Speegle, Greg

    2015-01-01

    Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

  13. BiologicalNetworks 2.0 - an integrative view of genome biology data

    Directory of Open Access Journals (Sweden)

    Ponomarenko Julia

    2010-12-01

    Full Text Available Abstract Background A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems. Results Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other and their relations (interactions, co-expression, co-citations, and other. The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations. Conclusions The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org.

  14. Host Proteins Determine MRSA Biofilm Structure and Integrity

    DEFF Research Database (Denmark)

    Dreier, Cindy; Nielsen, Astrid; Jørgensen, Nis Pedersen

    Human extracellular matrix (hECM) proteins aids the initial attachment and initiation of an infection, by specific binding to bacterial cell surface proteins. However, the importance of hECM proteins in structure, integrity and antibiotic resilience of a biofilm is unknown. This study aims...... to determine how specific hECM proteins affect S. aureus USA300 JE2 biofilms. Biofilms were grown in the presence of synovial fluid from rheumatoid arteritis patients to mimic in vivo conditions, where bacteria incorporate hECM proteins into the biofilm matrix. Difference in biofilm structure, with and without...... addition of hECM to growth media, was visualized by confocal laser scanning microscopy. Two enzymatic degradation experiments were used to study biofilm matrix composition and importance of hECM proteins: enzymatic removal of specific hECM proteins from growth media, before biofilm formation, and enzymatic...

  15. Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome

    Science.gov (United States)

    Poirot, Olivier; Timsit, Youri

    2016-05-01

    From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing.

  16. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  17. Dynamic state estimation for distribution networks with renewable energy integration

    NARCIS (Netherlands)

    Nguyen, P.H.; Venayagamoorthy, G.K.; Kling, W.L.; Ribeiro, P.F.

    2013-01-01

    The massive integration of variable and unpredictable Renewable Energy Sources (RES) and new types of load consumptions increases the dynamic and uncertain nature of the electricity grid. Emerging interests have focused on improving the monitoring capabilities of network operators so that they can

  18. Integral inventory control in spare parts networks with capacity restrictions

    NARCIS (Netherlands)

    Sleptchenko, Andrei

    2002-01-01

    Integral inventory control of repairable items in service networks can result in a significant gain compared to traditional local control mechanisms, in terms of both efficiency and customer service. Research on quantitative decision support models has yielded various useful results. However, in

  19. Network constrained wind integration on Vancouver Island

    International Nuclear Information System (INIS)

    Maddaloni, Jesse D.; Rowe, Andrew M.; Kooten, G. Cornelis van

    2008-01-01

    The aim of this study is to determine the costs and carbon emissions associated with operating a hydro-dominated electricity generation system (Vancouver Island, Canada) with varying degrees of wind penetration. The focus is to match the wind resource, system demand and abilities of extant generating facilities on a temporal basis, resulting in an operating schedule that minimizes system cost over a given period. This is performed by taking the perspective of a social planner who desires to find the lowest-cost mix of new and existing generation facilities. Unlike other studies, this analysis considers variable efficiency for thermal and hydro-generators, resulting in a fuel cost that varies with respect to generator part load. Since this study and others have shown that wind power may induce a large variance on existing dispatchable generators, forcing more frequent operation at reduced part load, inclusion of increased fuel cost at part load is important when investigating wind integration as it can significantly reduce the economic benefits of utilizing low-cost wind. Results indicate that the introduction of wind power may reduce system operating costs, but this depends heavily on whether the capital cost of the wind farm is considered. For the Vancouver Island mix with its large hydro-component, operating cost was reduced by a maximum of 15% at a wind penetration of 50%, with a negligible reduction in operating cost when the wind farm capital cost was included

  20. Vertical integration and organizational networks in health care.

    Science.gov (United States)

    Robinson, J C; Casalino, L P

    1996-01-01

    This paper documents the growing linkages between primary care-centered medical groups and specialists and between physicians and hospitals under managed care. We evaluate the two alternative forms of organizational coordination: "vertical integration," based on unified ownership, and "virtual integration," based on contractual networks. Excess capacity and the need for investment capital are major short-term determinants of these vertical versus virtual integration decisions in health care. In the longer term, the principal determinants are economies of scale, risk-bearing ability, transaction costs, and the capacity for innovation in methods of managing care.

  1. POINeT: protein interactome with sub-network analysis and hub prioritization

    Directory of Open Access Journals (Sweden)

    Lai Jin-Mei

    2009-04-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. Results We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3 to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. Conclusion The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to

  2. A Global Protein Kinase and Phosphatase Interaction Network in Yeast

    Science.gov (United States)

    Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

    2011-01-01

    The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

  3. Designing optimal bioethanol networks with purification for integrated biorefineries

    International Nuclear Information System (INIS)

    Shenoy, Akshay U.; Shenoy, Uday V.

    2014-01-01

    Highlights: • An analytical method is devised for bioethanol network integration with purification. • Minimum fresh bioethanol flow and pinch are found by the Unified Targeting Algorithm. • Optimal bioethanol networks are then synthesized by the Nearest Neighbors Algorithm. • Continuous targets and networks are developed over the purifier inlet flowrate range. • Case study of a biorefinery producing bioethanol from wheat shows large savings. - Abstract: Bioethanol networks with purification for processing pathways in integrated biorefineries are targeted and designed in this work by an analytical approach not requiring graphical constructions. The approach is based on six fundamental equations involving eight variables: two balance equations for the stream flowrate and the bioethanol load over the total network system; one equation for the above-pinch bioethanol load being picked up by the minimum fresh resource and the purified stream; and three equations for the purification unit. A solution strategy is devised by specifying the two variables associated with the purifier inlet stream. Importantly, continuous targeting is then possible over the entire purifier inlet flowrate range on deriving elegant formulae for the remaining six variables. The Unified Targeting Algorithm (UTA) is utilized to establish the minimum fresh bioethanol resource flowrate and identify the pinch purity. The fresh bioethanol resource flowrate target is shown to decrease linearly with purifier inlet flowrate provided the pinch is held by the same point. The Nearest Neighbors Algorithm (NNA) is used to methodically synthesize optimal networks matching bioethanol demands and sources. A case study of a biorefinery producing bioethanol from wheat with arabinoxylan (AX) coproduction is presented. It illustrates the versatility of the approach in generating superior practical designs with up to nearly 94% savings for integrated bioethanol networks, both with and without process

  4. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

    Science.gov (United States)

    Li, Hui; Liu, Chunmei

    2014-06-14

    3DProIN is a computational tool to visualize protein-protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.

  5. Control strategies for power distribution networks with electric vehicles integration

    DEFF Research Database (Denmark)

    Hu, Junjie

    of electrical energy. A smart grid can also be dened as an electricity network that can intelligently integrate the actions of all users connected to it - generators, consumers and those that do both - in order to eciently deliver sustainable, economic and secure electricity supplies. This thesis focuses...... of the ii market. To build a complete solution for integration of EVs into the distribution network, a price coordinated hierarchical scheduling system is proposed which can well characterize the involved actors in the smart grid. With this system, we demonstrate that it is possible to schedule the charging......Demand side resources, like electric vehicles (EVs), can become integral parts of a smart grids because instead of just consuming power they are capable of providing valuable services to power systems. EVs can be used to balance the intermittent renewable energy resources such as wind and solar...

  6. A Physical Interaction Network of Dengue Virus and Human Proteins*

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

    2011-01-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

  7. A physical interaction network of dengue virus and human proteins.

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

    2011-12-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

  8. System-level Modeling of Wireless Integrated Sensor Networks

    DEFF Research Database (Denmark)

    Virk, Kashif M.; Hansen, Knud; Madsen, Jan

    2005-01-01

    Wireless integrated sensor networks have emerged as a promising infrastructure for a new generation of monitoring and tracking applications. In order to efficiently utilize the extremely limited resources of wireless sensor nodes, accurate modeling of the key aspects of wireless sensor networks...... is necessary so that system-level design decisions can be made about the hardware and the software (applications and real-time operating system) architecture of sensor nodes. In this paper, we present a SystemC-based abstract modeling framework that enables system-level modeling of sensor network behavior...... by modeling the applications, real-time operating system, sensors, processor, and radio transceiver at the sensor node level and environmental phenomena, including radio signal propagation, at the sensor network level. We demonstrate the potential of our modeling framework by simulating and analyzing a small...

  9. Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks.

    Science.gov (United States)

    Benzekry, Sebastian; Tuszynski, Jack A; Rietman, Edward A; Lakka Klement, Giannoula

    2015-05-28

    The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity. We observed a linear correlation of R = -0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target. We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger

  10. Functional network integrity presages cognitive decline in preclinical Alzheimer disease.

    Science.gov (United States)

    Buckley, Rachel F; Schultz, Aaron P; Hedden, Trey; Papp, Kathryn V; Hanseeuw, Bernard J; Marshall, Gad; Sepulcre, Jorge; Smith, Emily E; Rentz, Dorene M; Johnson, Keith A; Sperling, Reisa A; Chhatwal, Jasmeer P

    2017-07-04

    To examine the utility of resting-state functional connectivity MRI (rs-fcMRI) measurements of network integrity as a predictor of future cognitive decline in preclinical Alzheimer disease (AD). A total of 237 clinically normal older adults (aged 63-90 years, Clinical Dementia Rating 0) underwent baseline β-amyloid (Aβ) imaging with Pittsburgh compound B PET and structural and rs-fcMRI. We identified 7 networks for analysis, including 4 cognitive networks (default, salience, dorsal attention, and frontoparietal control) and 3 noncognitive networks (primary visual, extrastriate visual, motor). Using linear and curvilinear mixed models, we used baseline connectivity in these networks to predict longitudinal changes in preclinical Alzheimer cognitive composite (PACC) performance, both alone and interacting with Aβ burden. Median neuropsychological follow-up was 3 years. Baseline connectivity in the default, salience, and control networks predicted longitudinal PACC decline, unlike connectivity in the dorsal attention and all noncognitive networks. Default, salience, and control network connectivity was also synergistic with Aβ burden in predicting decline, with combined higher Aβ and lower connectivity predicting the steepest curvilinear decline in PACC performance. In clinically normal older adults, lower functional connectivity predicted more rapid decline in PACC scores over time, particularly when coupled with increased Aβ burden. Among examined networks, default, salience, and control networks were the strongest predictors of rate of change in PACC scores, with the inflection point of greatest decline beyond the fourth year of follow-up. These results suggest that rs-fcMRI may be a useful predictor of early, AD-related cognitive decline in clinical research settings. © 2017 American Academy of Neurology.

  11. Structural analysis of site-directed mutants of cellular retinoic acid-binding protein II addresses the relationship between structural integrity and ligand binding

    International Nuclear Information System (INIS)

    Vaezeslami, Soheila; Jia, Xiaofei; Vasileiou, Chrysoula; Borhan, Babak; Geiger, James H.

    2008-01-01

    A water network stabilizes the structure of cellular retionic acid binding protein II. The structural integrity of cellular retinoic acid-binding protein II (CRABPII) has been investigated using the crystal structures of CRABPII mutants. The overall fold was well maintained by these CRABPII mutants, each of which carried multiple different mutations. A water-mediated network is found to be present across the large binding cavity, extending from Arg111 deep inside the cavity to the α2 helix at its entrance. This chain of interactions acts as a ‘pillar’ that maintains the integrity of the protein. The disruption of the water network upon loss of Arg111 leads to decreased structural integrity of the protein. A water-mediated network can be re-established by introducing the hydrophilic Glu121 inside the cavity, which results in a rigid protein with the α2 helix adopting an altered conformation compared with wild-type CRABPII

  12. Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

    Science.gov (United States)

    Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

    2012-01-01

    Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

  13. LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni

    Directory of Open Access Journals (Sweden)

    Priyanka Patel

    2016-03-01

    Full Text Available A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level. Keywords: L.major, S.mansoni, Regulatory networks, Transcription factors, Database

  14. Integrating wireless sensor network for monitoring subsidence phenomena

    Science.gov (United States)

    Marturià, Jordi; Lopez, Ferran; Gigli, Giovanni; Intrieri, Emanuele; Mucchi, Lorenzo; Fornaciai, Alessandro

    2016-04-01

    An innovative wireless sensor network (WSN) for the 3D superficial monitoring of deformations (such as landslides and subsidence) is being developed in the frame of the Wi-GIM project (Wireless sensor network for Ground Instability Monitoring - LIFE12 ENV/IT/001033). The surface movement is detected acquiring the position (x, y and z) by integrating large bandwidth technology able to detect the 3D coordinates of the sensor with a sub-meter error, with continuous wave radar, which allows decreasing the error down to sub-cm. The Estació neighborhood in Sallent is located over the old potassium mine Enrique. This zone has been affected by a subsidence process over more than twenty years. The implementation of a wide network for ground auscultation has allowed monitoring the process of subsidence since 1997. This network consists of: i) a high-precision topographic leveling network to control the subsidence in surface; ii) a rod extensometers network to monitor subsurface deformation; iii) an automatic Leica TCA Total Station to monitor building movements; iv) an inclinometers network to measure the horizontal displacements on subsurface and v) a piezometer to measure the water level. Those networks were implemented within an alert system for an organized an efficient response of the civil protection authorities in case of an emergency. On 23rd December 2008, an acceleration of subsoil movements (of approx. 12-18 cm/year) provoked the activation of the emergency plan by the Catalan Civil Protection. This implied the preventive and scheduled evacuation of the neighbours (January 2009) located in the area with a higher risk of collapse: around 120 residents of 43 homes. As a consequence, the administration implemented a compensation plan for the evacuation of the whole neighbourhood residents and the demolition of 405 properties. In this work, the adaptation and integration process of Wi-GIM system with those conventional monitoring network are presented for its testing

  15. General theory for integrated analysis of growth, gene, and protein expression in biofilms.

    Science.gov (United States)

    Zhang, Tianyu; Pabst, Breana; Klapper, Isaac; Stewart, Philip S

    2013-01-01

    A theory for analysis and prediction of spatial and temporal patterns of gene and protein expression within microbial biofilms is derived. The theory integrates phenomena of solute reaction and diffusion, microbial growth, mRNA or protein synthesis, biomass advection, and gene transcript or protein turnover. Case studies illustrate the capacity of the theory to simulate heterogeneous spatial patterns and predict microbial activities in biofilms that are qualitatively different from those of planktonic cells. Specific scenarios analyzed include an inducible GFP or fluorescent protein reporter, a denitrification gene repressed by oxygen, an acid stress response gene, and a quorum sensing circuit. It is shown that the patterns of activity revealed by inducible stable fluorescent proteins or reporter unstable proteins overestimate the region of activity. This is due to advective spreading and finite protein turnover rates. In the cases of a gene induced by either limitation for a metabolic substrate or accumulation of a metabolic product, maximal expression is predicted in an internal stratum of the biofilm. A quorum sensing system that includes an oxygen-responsive negative regulator exhibits behavior that is distinct from any stage of a batch planktonic culture. Though here the analyses have been limited to simultaneous interactions of up to two substrates and two genes, the framework applies to arbitrarily large networks of genes and metabolites. Extension of reaction-diffusion modeling in biofilms to the analysis of individual genes and gene networks is an important advance that dovetails with the growing toolkit of molecular and genetic experimental techniques.

  16. Improving protein function prediction methods with integrated literature data

    Directory of Open Access Journals (Sweden)

    Gabow Aaron P

    2008-04-01

    Full Text Available Abstract Background Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. Results We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder

  17. Perspectives and limitations of QKD integration in metropolitan area networks.

    Science.gov (United States)

    Aleksic, Slavisa; Hipp, Florian; Winkler, Dominic; Poppe, Andreas; Schrenk, Bernhard; Franzl, Gerald

    2015-04-20

    Quantum key distribution (QKD) systems have already reached a reasonable level of maturity. However, a smooth integration and a wide adoption of commercial QKD systems in metropolitan area networks has still remained challenging because of technical and economical obstacles. Mainly the need for dedicated fibers and the strong dependence of the secret key rate on both loss budget and background noise in the quantum channel hinder a practical, flexible and robust implementation of QKD in current and next-generation optical metro networks. In this paper, we discuss these obstacles and present approaches to share existing fiber infrastructures among quantum and classical channels. Particularly, a proposal for a smooth integration of QKD in optical metro networks, which implies removing spurious background photons caused by optical transmitters, amplifiers and nonlinear effects in fibers, is presented and discussed. We determine and characterize impairments on quantum channels caused by many classical telecom channels at practically used power levels coexisting within the same fiber. Extensive experimental results are presented and indicate that a practical integration of QKD in conventional optical metro networks is possible.

  18. An integrative -omics approach to identify functional sub-networks in human colorectal cancer.

    Directory of Open Access Journals (Sweden)

    Rod K Nibbe

    2010-01-01

    Full Text Available Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC

  19. Integrated healthcare networks' performance: a growth curve modeling approach.

    Science.gov (United States)

    Wan, Thomas T H; Wang, Bill B L

    2003-05-01

    This study examines the effects of integration on the performance ratings of the top 100 integrated healthcare networks (IHNs) in the United States. A strategic-contingency theory is used to identify the relationship of IHNs' performance to their structural and operational characteristics and integration strategies. To create a database for the panel study, the top 100 IHNs selected by the SMG Marketing Group in 1998 were followed up in 1999 and 2000. The data were merged with the Dorenfest data on information system integration. A growth curve model was developed and validated by the Mplus statistical program. Factors influencing the top 100 IHNs' performance in 1998 and their subsequent rankings in the consecutive years were analyzed. IHNs' initial performance scores were positively influenced by network size, number of affiliated physicians and profit margin, and were negatively associated with average length of stay and technical efficiency. The continuing high performance, judged by maintaining higher performance scores, tended to be enhanced by the use of more managerial or executive decision-support systems. Future studies should include time-varying operational indicators to serve as predictors of network performance.

  20. Fast mapping rapidly integrates information into existing memory networks.

    Science.gov (United States)

    Coutanche, Marc N; Thompson-Schill, Sharon L

    2014-12-01

    Successful learning involves integrating new material into existing memory networks. A learning procedure known as fast mapping (FM), thought to simulate the word-learning environment of children, has recently been linked to distinct neuroanatomical substrates in adults. This idea has suggested the (never-before tested) hypothesis that FM may promote rapid incorporation into cortical memory networks. We test this hypothesis here in 2 experiments. In our 1st experiment, we introduced 50 participants to 16 unfamiliar animals and names through FM or explicit encoding (EE) and tested participants on the training day, and again after sleep. Learning through EE produced strong declarative memories, without immediate lexical competition, as expected from slow-consolidation models. Learning through FM, however, led to almost immediate lexical competition, which continued to the next day. Additionally, the learned words began to prime related concepts on the day following FM (but not EE) training. In a 2nd experiment, we replicated the lexical integration results and determined that presenting an already-known item during learning was crucial for rapid integration through FM. The findings presented here indicate that learned items can be integrated into cortical memory networks at an accelerated rate through fast mapping. The retrieval of a related known concept, in order to infer the target of the FM question, is critical for this effect. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  1. A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Santra, Tapesh, E-mail: tapesh.santra@ucd.ie [Systems Biology Ireland, University College Dublin, Dublin (Ireland)

    2014-05-20

    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

  2. A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

    International Nuclear Information System (INIS)

    Santra, Tapesh

    2014-01-01

    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

  3. Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

    Science.gov (United States)

    LAVALLÉE-ADAM, MATHIEU; COULOMBE, BENOIT; BLANCHETTE, MATHIEU

    2015-01-01

    High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com. PMID:20377456

  4. Social networking in nursing education: integrative literature review.

    Science.gov (United States)

    Kakushi, Luciana Emi; Évora, Yolanda Dora Martinez

    2016-01-01

    to identify the use of social networking in nursing education. integrative literature review in the databases: LILACS, IBECS, Cochrane, BDENF, SciELO, CINAHL, Scopus, PubMed, CAPES Periodicals Portal and Web of Science, using the descriptors: social networking and nursing education and the keywords: social networking sites and nursing education, carried out in April 2015. of the 489 articles found, only 14 met the inclusion and exclusion criteria. Most studies were published after 2013 (57%), originating from the United States and United Kingdom (77.8%). It was observed the use of social networking among nursing students, postgraduate students, mentors and nurses, in undergraduate programmes, hybrid education (blended-learning) and in interprofessional education. The social networking sites used in the teaching and learning process were Facebook (42.8%), Ning (28.5%), Twitter (21.4%) and MySpace (7.1%), by means of audios, videos, quizzes, animations, forums, guidance, support, discussions and research group. few experiences of the use of social networking in nursing education were found and their contributions show the numerous benefits and difficulties faced, providing resourses for the improvement and revaluation of their use in the teaching and learning process.

  5. Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach.

    Science.gov (United States)

    Senior, Alistair M; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J

    2016-01-01

    Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.

  6. Contingent approach to Internet-based supply network integration

    Science.gov (United States)

    Ho, Jessica; Boughton, Nick; Kehoe, Dennis; Michaelides, Zenon

    2001-10-01

    The Internet is playing an increasingly important role in enhancing the operations of supply networks as many organizations begin to recognize the benefits of Internet- enabled supply arrangements. However, the developments and applications to-date do not extend significantly beyond the dyadic model, whereas the real advantages are to be made with the external and network models to support a coordinated and collaborative based approach. The DOMAIN research group at the University of Liverpool is currently defining new Internet- enabled approaches to enable greater collaboration across supply chains. Different e-business models and tools are focusing on different applications. Using inappropriate e- business models, tools or techniques will bring negative results instead of benefits to all the tiers in the supply network. Thus there are a number of issues to be considered before addressing Internet based supply network integration, in particular an understanding of supply chain management, the emergent business models and evaluating the effects of deploying e-business to the supply network or a particular tier. It is important to utilize a contingent approach to selecting the right e-business model to meet the specific supply chain requirements. This paper addresses the issues and provides a case study on the indirect materials supply networks.

  7. Social networking in nursing education: integrative literature review

    Directory of Open Access Journals (Sweden)

    Luciana Emi Kakushi

    Full Text Available Abstract Objective: to identify the use of social networking in nursing education. Method: integrative literature review in the databases: LILACS, IBECS, Cochrane, BDENF, SciELO, CINAHL, Scopus, PubMed, CAPES Periodicals Portal and Web of Science, using the descriptors: social networking and nursing education and the keywords: social networking sites and nursing education, carried out in April 2015. Results: of the 489 articles found, only 14 met the inclusion and exclusion criteria. Most studies were published after 2013 (57%, originating from the United States and United Kingdom (77.8%. It was observed the use of social networking among nursing students, postgraduate students, mentors and nurses, in undergraduate programmes, hybrid education (blended-learning and in interprofessional education. The social networking sites used in the teaching and learning process were Facebook (42.8%, Ning (28.5%, Twitter (21.4% and MySpace (7.1%, by means of audios, videos, quizzes, animations, forums, guidance, support, discussions and research group. Conclusion: few experiences of the use of social networking in nursing education were found and their contributions show the numerous benefits and difficulties faced, providing resourses for the improvement and revaluation of their use in the teaching and learning process.

  8. Social networks in nursing work processes: an integrative literature review

    Directory of Open Access Journals (Sweden)

    Ana Cláudia Mesquita

    Full Text Available Abstract OBJECTIVE To identify and analyze the available evidence in the literature on the use of social networks in nursing work processes. METHOD An integrative review of the literature conducted in PubMed, CINAHL, EMBASE and LILACS databases in January 2016, using the descriptors social media, social networking, nursing, enfermagem, redes sociais, mídias sociais, and the keyword nursing practice, without year restriction. RESULTS The sample consisted of 27 international articles which were published between 2011 and 2016. The social networks used were Facebook (66.5%, Twitter (30% and WhatsApp (3.5%. In 70.5% of the studies, social networks were used for research purposes, in 18.5% they were used as a tool aimed to assist students in academic activities, and in 11% for executing interventions via the internet. CONCLUSION Nurses have used social networks in their work processes such as Facebook, Twitter and WhatsApp to research, teach and watch. The articles show several benefits in using such tools in the nursing profession; however, ethical considerations regarding the use of social networks deserve further discussion.

  9. Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma Metastasis

    Energy Technology Data Exchange (ETDEWEB)

    Goudarzi, Atta, E-mail: atta.goudarzi@utoronto.ca [Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8 (Canada); Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Gokgoz, Nalan; Gill, Mona; Pinnaduwage, Dushanthi [Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Merico, Daniele [The Centre for Applied Genomics, The Hospital for Sick Children, MaRS Centre-East Tower, 101 College Street Rm.14-701, Toronto, ON M5G 1L7 (Canada); Wunder, Jay S. [Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Andrulis, Irene L. [Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8 (Canada); Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada)

    2013-04-08

    Osteosarcoma (OS) is the most common primary malignant tumor of the bone, and pulmonary metastasis is the most frequent cause of OS mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Expression profiling of OS tumors, and subsequent supervised network analysis, was performed to discover genetic networks differentially activated or organized in metastatic OS compared to localized OS. Broad trends among the profiles of metastatic tumors include aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was also found to be differentially expressed among OS cell lines with differing metastatic capacity in xenograft models. PRKCε transcript was more abundant in some metastatic OS tumors; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation.

  10. Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma Metastasis

    International Nuclear Information System (INIS)

    Goudarzi, Atta; Gokgoz, Nalan; Gill, Mona; Pinnaduwage, Dushanthi; Merico, Daniele; Wunder, Jay S.; Andrulis, Irene L.

    2013-01-01

    Osteosarcoma (OS) is the most common primary malignant tumor of the bone, and pulmonary metastasis is the most frequent cause of OS mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Expression profiling of OS tumors, and subsequent supervised network analysis, was performed to discover genetic networks differentially activated or organized in metastatic OS compared to localized OS. Broad trends among the profiles of metastatic tumors include aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was also found to be differentially expressed among OS cell lines with differing metastatic capacity in xenograft models. PRKCε transcript was more abundant in some metastatic OS tumors; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation

  11. Securing Digital Images Integrity using Artificial Neural Networks

    Science.gov (United States)

    Hajji, Tarik; Itahriouan, Zakaria; Ouazzani Jamil, Mohammed

    2018-05-01

    Digital image signature is a technique used to protect the image integrity. The application of this technique can serve several areas of imaging applied to smart cities. The objective of this work is to propose two methods to protect digital image integrity. We present a description of two approaches using artificial neural networks (ANN) to digitally sign an image. The first one is “Direct Signature without learning” and the second is “Direct Signature with learning”. This paper presents the theory of proposed approaches and an experimental study to test their effectiveness.

  12. Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Eric A Yen

    2014-05-01

    Full Text Available Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and

  13. Improving Protein Fold Recognition by Deep Learning Networks

    Science.gov (United States)

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-12-01

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

  14. Improving Protein Fold Recognition by Deep Learning Networks.

    Science.gov (United States)

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-12-04

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

  15. Integrating protein structures and precomputed genealogies in the Magnum database: Examples with cellular retinoid binding proteins

    Directory of Open Access Journals (Sweden)

    Bradley Michael E

    2006-02-01

    Full Text Available Abstract Background When accurate models for the divergent evolution of protein sequences are integrated with complementary biological information, such as folded protein structures, analyses of the combined data often lead to new hypotheses about molecular physiology. This represents an excellent example of how bioinformatics can be used to guide experimental research. However, progress in this direction has been slowed by the lack of a publicly available resource suitable for general use. Results The precomputed Magnum database offers a solution to this problem for ca. 1,800 full-length protein families with at least one crystal structure. The Magnum deliverables include 1 multiple sequence alignments, 2 mapping of alignment sites to crystal structure sites, 3 phylogenetic trees, 4 inferred ancestral sequences at internal tree nodes, and 5 amino acid replacements along tree branches. Comprehensive evaluations revealed that the automated procedures used to construct Magnum produced accurate models of how proteins divergently evolve, or genealogies, and correctly integrated these with the structural data. To demonstrate Magnum's capabilities, we asked for amino acid replacements requiring three nucleotide substitutions, located at internal protein structure sites, and occurring on short phylogenetic tree branches. In the cellular retinoid binding protein family a site that potentially modulates ligand binding affinity was discovered. Recruitment of cellular retinol binding protein to function as a lens crystallin in the diurnal gecko afforded another opportunity to showcase the predictive value of a browsable database containing branch replacement patterns integrated with protein structures. Conclusion We integrated two areas of protein science, evolution and structure, on a large scale and created a precomputed database, known as Magnum, which is the first freely available resource of its kind. Magnum provides evolutionary and structural

  16. The construction of an amino acid network for understanding protein structure and function.

    Science.gov (United States)

    Yan, Wenying; Zhou, Jianhong; Sun, Maomin; Chen, Jiajia; Hu, Guang; Shen, Bairong

    2014-06-01

    Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

  17. Integrating Space Communication Network Capabilities via Web Portal Technologies

    Science.gov (United States)

    Johnston, Mark D.; Lee, Carlyn-Ann; Lau, Chi-Wung; Cheung, Kar-Ming; Levesque, Michael; Carruth, Butch; Coffman, Adam; Wallace, Mike

    2014-01-01

    We have developed a service portal prototype as part of an investigation into the feasibility of using Java portlet technology as a means of providing integrated access to NASA communications network services. Portal servers provide an attractive platform for this role due to the various built-in collaboration applications they can provide, combined with the possibility to develop custom inter-operating portlets to extent their functionality while preserving common presentation and behavior. This paper describes various options for integration of network services related to planning and scheduling, and results based on use of a popular open-source portal framework. Plans are underway to develop an operational SCaN Service Portal, building on the experiences reported here.

  18. STITCH 2: an interaction network database for small molecules and proteins

    DEFF Research Database (Denmark)

    Kuhn, Michael; Szklarczyk, Damian; Franceschini, Andrea

    2010-01-01

    Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug......-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other...... chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/....

  19. Social network approaches to leadership: an integrative conceptual review.

    Science.gov (United States)

    Carter, Dorothy R; DeChurch, Leslie A; Braun, Michael T; Contractor, Noshir S

    2015-05-01

    Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness. (c) 2015 APA, all rights reserved.

  20. Peptide microarrays to probe for competition for binding sites in a protein interaction network

    NARCIS (Netherlands)

    Sinzinger, M.D.S.; Ruttekolk, I.R.R.; Gloerich, J.; Wessels, H.; Chung, Y.D.; Adjobo-Hermans, M.J.W.; Brock, R.E.

    2013-01-01

    Cellular protein interaction networks are a result of the binding preferences of a particular protein and the entirety of interactors that mutually compete for binding sites. Therefore, the reconstruction of interaction networks by the accumulation of interaction networks for individual proteins

  1. Integrated evolutionary computation neural network quality controller for automated systems

    Energy Technology Data Exchange (ETDEWEB)

    Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

  2. Integrated System for Performance Monitoring of ATLAS TDAQ Network

    CERN Document Server

    Savu, D; The ATLAS collaboration; Martin, B; Sjoen, R; Batraneanu, S; Stancu, S

    2010-01-01

    The ATLAS TDAQ Network consists of three separate networks spanning four levels of the experimental building. Over 200 edge switches and 5 multi-blade chassis routers are used to interconnect 2000 processors, adding up to more than 7000 high speed interfaces. In order to substantially speed-up ad-hoc and post mortem analysis, a scalable, yet flexible, integrated system for monitoring both network statistics and environmental conditions, processor parameters and data taking characteristics was required. For successful up-to-the-minute monitoring, information from many SNMP compliant devices, independent databases and custom APIs was gathered, stored and displayed in an optimal way. Easy navigation and compact aggregation of multiple data sources were the main requirements; characteristics not found in any of the tested products, either open-source or commercial. This paper describes how performance, scalability and display issues were addressed and what challenges the project faced during development and deplo...

  3. Integration of Bacterial Small RNAs in Regulatory Networks.

    Science.gov (United States)

    Nitzan, Mor; Rehani, Rotem; Margalit, Hanah

    2017-05-22

    Small RNAs (sRNAs) are central regulators of gene expression in bacteria, controlling target genes posttranscriptionally by base pairing with their mRNAs. sRNAs are involved in many cellular processes and have unique regulatory characteristics. In this review, we discuss the properties of regulation by sRNAs and how it differs from and combines with transcriptional regulation. We describe the global characteristics of the sRNA-target networks in bacteria using graph-theoretic approaches and review the local integration of sRNAs in mixed regulatory circuits, including feed-forward loops and their combinations, feedback loops, and circuits made of an sRNA and another regulator, both derived from the same transcript. Finally, we discuss the competition effects in posttranscriptional regulatory networks that may arise over shared targets, shared regulators, and shared resources and how they may lead to signal propagation across the network.

  4. Integration of metabolome data with metabolic networks reveals reporter reactions

    DEFF Research Database (Denmark)

    Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen

    2006-01-01

    Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...... network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...

  5. Nanosensors-Cellphone Integration for Extended Chemical Sensing Network

    Science.gov (United States)

    Li, Jing

    2011-01-01

    This poster is to present the development of a cellphone sensor network for extended chemical sensing. The nanosensors using carbon nanotubes and other nanostructures are used with low power and high sensitivity for chemical detection. The sensing module has been miniaturized to a small size that can plug in or clip on to a smartphone. The chemical information detected by the nanosensors are acquired by a smartphone and transmitted via cellphone 3g or WiFi network to an internet server. The whole integrated sensing system from sensor to cellphone to a cloud will provide an extended chemical sensing network that can cover nation wide and even cover global wide for early warning of a hazardous event.

  6. Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

    Science.gov (United States)

    Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

    1989-03-01

    A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

  7. Networked Social Reproduction: Crises in the Integrated Circuit

    Directory of Open Access Journals (Sweden)

    Elise Danielle Thorburn

    2016-07-01

    Full Text Available This paper argues that the means of communication are sites for, and aspects of, social reproduction. In contemporary capitalism, motivated as it is by new, networked digital technologies, social reproduction is increasingly virtualised through the means of communication. Although recent political struggles have demonstrated how networked technologies can liberate social reproduction from the profit motive and from commodifying impulses, the tendency is to invoke and accelerate socially reproductive crises—crises in the capacity to reproduce ourselves both daily and intergenerationally. These crises have psychic and corporeal impacts, and intensify Tronti’s “social factory” thesis of capital’s technical composition. In order to develop modes and means of liberatory communication in the integrated circuit it is necessary to untangle and chart both the pathways and outcomes of the crises networked social reproduction invokes.

  8. Increasing cellular coverage within integrated terrestrial/satellite mobile networks

    Science.gov (United States)

    Castro, Jonathan P.

    1995-01-01

    When applying the hierarchical cellular concept, the satellite acts as giant umbrella cell covering a region with some terrestrial cells. If a mobile terminal traversing the region arrives to the border-line or limits of a regular cellular ground service, network transition occurs and the satellite system continues the mobile coverage. To adequately assess the boundaries of service of a mobile satellite system an a cellular network within an integrated environment, this paper provides an optimized scheme to predict when a network transition may be necessary. Under the assumption of a classified propagation phenomenon and Lognormal shadowing, the study applies an analytical approach to estimate the location of a mobile terminal based on a reception of the signal strength emitted by a base station.

  9. Energy in Southeast Asia: from Networks to Markets Integration

    International Nuclear Information System (INIS)

    Cornot-Gandolphe, Sylvie

    2017-01-01

    Southeast Asia is one of the world's most dynamic regions and experiences strong economic and energy demand growth rates. In this context, the Association of Southeast Asian Nations (ASEAN) is seeking to interconnect the electric grids and gas networks of the countries through two initiatives, the Asean Power Grid and the Trans-Asean Gas Pipeline, in order to pool resources and optimize energy markets integration in the region

  10. Local Health Integration Networks: Build on their purpose.

    Science.gov (United States)

    MacLeod, Hugh

    2015-11-01

    This article provides a high-level overview on the creation of Local Health Integration Networks (LHINs) and illustrates the complexities involved in their implementation. To understand regional structures such as LHINs, one must understand the context in which design and execution takes place. The article ends with a commentary on how Ontario is performing post-LHINs and discusses next steps. © 2015 The Canadian College of Health Leaders.

  11. Silicon-based optical integrated circuits for terabit communication networks

    International Nuclear Information System (INIS)

    Svidzinsky, K K

    2003-01-01

    A brief review is presented of the development of silicon-based optical integrated circuits used as components in modern all-optical communication networks with the terabit-per-second transmission capacity. The designs and technologies for manufacturing these circuits are described and the problems related to their development and application in WDM communication systems are considered. (special issue devoted to the memory of academician a m prokhorov)

  12. Parallel protein secondary structure prediction based on neural networks.

    Science.gov (United States)

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  13. Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis

    NARCIS (Netherlands)

    Dam, van J.C.J.; Schaap, P.J.; Martins dos Santos, V.A.P.; Suarez Diez, M.

    2014-01-01

    Background: Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each

  14. Integrated network for structural integrity monitoring of critical components in nuclear facilities, RIMIS

    International Nuclear Information System (INIS)

    Roth, Maria; Constantinescu, Dan Mihai; Brad, Sebastian; Ducu, Catalin; Malinovschi, Viorel

    2008-01-01

    The round table aims to join specialists working in the research area of the Romanian R and D Institutes and Universities involved in structural integrity assessment of materials, especially those working in the nuclear field, together with the representatives of the end user, the Cernavoda NPP. This scientific event will offer the opportunity to disseminate the theoretical, experimental and modelling activities, carried out to date, in the framework of the National Program 'Research of Excellence', Module I 2006-2008, managed by the National Authority for Scientific Research. Entitled 'Integrated Network for Structural Integrity Monitoring of Critical Components in Nuclear Facilities, RIMIS, the project has two main objectives: 1. - to elaborate a procedure applicable to the structural integrity assessment of critical components used in Romanian nuclear facilities (CANDU type Reactor, Hydrogen Isotopes Separation installations); 2. - to integrate the national networking into a similar one of European level, and to enhance the scientific significance of Romanian R and D organisations as well as to increase the contribution in solving major issues of the nuclear field. The topics of the round table will be focused on: 1. Development of a Structural Integrity Assessment Methodology applicable to the nuclear facilities components; 2. Experimental investigation methods and procedures; 3. Numeric simulation of nuclear components behaviour; 4. Further activities to finalize the assessment procedure. Also participations and contributions to sustain the activity in the European Network NULIFE, FP6 will be discussed. (authors)

  15. Integrated multimedia information system on interactive CATV network

    Science.gov (United States)

    Lee, Meng-Huang; Chang, Shin-Hung

    1998-10-01

    In the current CATV system architectures, they provide one- way delivery of a common menu of entertainment to all the homes through the cable network. Through the technologies evolution, the interactive services (or two-way services) can be provided in the cable TV systems. They can supply customers with individualized programming and support real- time two-way communications. With a view to the service type changed from the one-way delivery systems to the two-way interactive systems, `on demand services' is a distinct feature of multimedia systems. In this paper, we present our work of building up an integrated multimedia system on interactive CATV network in Shih Chien University. Besides providing the traditional analog TV programming from the cable operator, we filter some channels to reserve them as our campus information channels. In addition to the analog broadcasting channel, the system also provides the interactive digital multimedia services, e.g. Video-On- Demand (VOD), Virtual Reality, BBS, World-Wide-Web, and Internet Radio Station. These two kinds of services are integrated in a CATV network by the separation of frequency allocation for the analog broadcasting service and the digital interactive services. Our ongoing work is to port our previous work of building up a VOD system conformed to DAVIC standard (for inter-operability concern) on Ethernet network into the current system.

  16. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    Science.gov (United States)

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  17. Annotating gene sets by mining large literature collections with protein networks.

    Science.gov (United States)

    Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey

    2018-01-01

    Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

  18. Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data.

    Science.gov (United States)

    Tomescu, Oana A; Mattanovich, Diethard; Thallinger, Gerhard G

    2014-01-01

    Technological improvements have shifted the focus from data generation to data analysis. The availability of large amounts of data from transcriptomics, protemics and metabolomics experiments raise new questions concerning suitable integrative analysis methods. We compare three integrative analysis techniques (co-inertia analysis, generalized singular value decomposition and integrative biclustering) by applying them to gene and protein abundance data from the six life cycle stages of Plasmodium falciparum. Co-inertia analysis is an analysis method used to visualize and explore gene and protein data. The generalized singular value decomposition has shown its potential in the analysis of two transcriptome data sets. Integrative Biclustering applies biclustering to gene and protein data. Using CIA, we visualize the six life cycle stages of Plasmodium falciparum, as well as GO terms in a 2D plane and interpret the spatial configuration. With GSVD, we decompose the transcriptomic and proteomic data sets into matrices with biologically meaningful interpretations and explore the processes captured by the data sets. IBC identifies groups of genes, proteins, GO Terms and life cycle stages of Plasmodium falciparum. We show method-specific results as well as a network view of the life cycle stages based on the results common to all three methods. Additionally, by combining the results of the three methods, we create a three-fold validated network of life cycle stage specific GO terms: Sporozoites are associated with transcription and transport; merozoites with entry into host cell as well as biosynthetic and metabolic processes; rings with oxidation-reduction processes; trophozoites with glycolysis and energy production; schizonts with antigenic variation and immune response; gametocyctes with DNA packaging and mitochondrial transport. Furthermore, the network connectivity underlines the separation of the intraerythrocytic cycle from the gametocyte and sporozoite stages

  19. Integral membrane protein structure determination using pseudocontact shifts

    Energy Technology Data Exchange (ETDEWEB)

    Crick, Duncan J.; Wang, Jue X. [University of Cambridge, Department of Biochemistry (United Kingdom); Graham, Bim; Swarbrick, James D. [Monash University, Monash Institute of Pharmaceutical Sciences (Australia); Mott, Helen R.; Nietlispach, Daniel, E-mail: dn206@cam.ac.uk [University of Cambridge, Department of Biochemistry (United Kingdom)

    2015-04-15

    Obtaining enough experimental restraints can be a limiting factor in the NMR structure determination of larger proteins. This is particularly the case for large assemblies such as membrane proteins that have been solubilized in a membrane-mimicking environment. Whilst in such cases extensive deuteration strategies are regularly utilised with the aim to improve the spectral quality, these schemes often limit the number of NOEs obtainable, making complementary strategies highly beneficial for successful structure elucidation. Recently, lanthanide-induced pseudocontact shifts (PCSs) have been established as a structural tool for globular proteins. Here, we demonstrate that a PCS-based approach can be successfully applied for the structure determination of integral membrane proteins. Using the 7TM α-helical microbial receptor pSRII, we show that PCS-derived restraints from lanthanide binding tags attached to four different positions of the protein facilitate the backbone structure determination when combined with a limited set of NOEs. In contrast, the same set of NOEs fails to determine the correct 3D fold. The latter situation is frequently encountered in polytopical α-helical membrane proteins and a PCS approach is thus suitable even for this particularly challenging class of membrane proteins. The ease of measuring PCSs makes this an attractive route for structure determination of large membrane proteins in general.

  20. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  1. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

    Science.gov (United States)

    Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

    2012-01-01

    Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

  2. Discovery of Cellular Proteins Required for the Early Steps of HCV Infection Using Integrative Genomics

    Science.gov (United States)

    Yang, Jae-Seong; Kwon, Oh Sung; Kim, Sanguk; Jang, Sung Key

    2013-01-01

    Successful viral infection requires intimate communication between virus and host cell, a process that absolutely requires various host proteins. However, current efforts to discover novel host proteins as therapeutic targets for viral infection are difficult. Here, we developed an integrative-genomics approach to predict human genes involved in the early steps of hepatitis C virus (HCV) infection. By integrating HCV and human protein associations, co-expression data, and tight junction-tetraspanin web specific networks, we identified host proteins required for the early steps in HCV infection. Moreover, we validated the roles of newly identified proteins in HCV infection by knocking down their expression using small interfering RNAs. Specifically, a novel host factor CD63 was shown to directly interact with HCV E2 protein. We further demonstrated that an antibody against CD63 blocked HCV infection, indicating that CD63 may serve as a new therapeutic target for HCV-related diseases. The candidate gene list provides a source for identification of new therapeutic targets. PMID:23593195

  3. Validation of protein models by a neural network approach

    Directory of Open Access Journals (Sweden)

    Fantucci Piercarlo

    2008-01-01

    Full Text Available Abstract Background The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods. Conclusion In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the quality of protein structures. The use of AIDE in combination with other evaluation tools is expected to further enhance protein refinement efforts.

  4. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Alina Sîrbu

    2015-05-01

    Full Text Available Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions. Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

  5. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.

    Science.gov (United States)

    Sîrbu, Alina; Crane, Martin; Ruskin, Heather J

    2015-05-14

    Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

  6. T-SDN architecture for space and ground integrated optical transport network

    Science.gov (United States)

    Nie, Kunkun; Hu, Wenjing; Gao, Shenghua; Chang, Chengwu

    2015-11-01

    Integrated optical transport network is the development trend of the future space information backbone network. The space and ground integrated optical transport network(SGIOTN) may contain a variety of equipment and systems. Changing the network or meeting some innovation missions in the network will be an expensive implement. Software Defined Network(SDN) provides a good solution to flexibly adding process logic, timely control states and resources of the whole network, as well as shielding the differences of heterogeneous equipment and so on. According to the characteristics of SGIOTN, we propose an transport SDN architecture for it, with hierarchical control plane and data plane composed of packet networks and optical transport networks.

  7. An automated approach to network features of protein structure ensembles

    Science.gov (United States)

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-01-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896

  8. Integral and peripheral association of proteins and protein complexes with Yersinia pestis inner and outer membranes

    Directory of Open Access Journals (Sweden)

    Bunai Christine L

    2009-02-01

    Full Text Available Abstract Yersinia pestis proteins were sequentially extracted from crude membranes with a high salt buffer (2.5 M NaBr, an alkaline solution (180 mM Na2CO3, pH 11.3 and membrane denaturants (8 M urea, 2 M thiourea and 1% amidosulfobetaine-14. Separation of proteins by 2D gel electrophoresis was followed by identification of more than 600 gene products by MS. Data from differential 2D gel display experiments, comparing protein abundances in cytoplasmic, periplasmic and all three membrane fractions, were used to assign proteins found in the membrane fractions to three protein categories: (i integral membrane proteins and peripheral membrane proteins with low solubility in aqueous solutions (220 entries; (ii peripheral membrane proteins with moderate to high solubility in aqueous solutions (127 entries; (iii cytoplasmic or ribosomal membrane-contaminating proteins (80 entries. Thirty-one proteins were experimentally associated with the outer membrane (OM. Circa 50 proteins thought to be part of membrane-localized, multi-subunit complexes were identified in high Mr fractions of membrane extracts via size exclusion chromatography. This data supported biologically meaningful assignments of many proteins to the membrane periphery. Since only 32 inner membrane (IM proteins with two or more predicted transmembrane domains (TMDs were profiled in 2D gels, we resorted to a proteomic analysis by 2D-LC-MS/MS. Ninety-four additional IM proteins with two or more TMDs were identified. The total number of proteins associated with Y. pestis membranes increased to 456 and included representatives of all six β-barrel OM protein families and 25 distinct IM transporter families.

  9. Integration and the performance of healthcare networks: do integration strategies enhance efficiency, profitability, and image?

    Directory of Open Access Journals (Sweden)

    Thomas T.H. Wan

    2001-06-01

    Full Text Available Purpose: This study examines the integration effects on efficiency and financial viability of the top 100 integrated healthcare networks (IHNs in the United States. Theory: A contingency- strategic theory is used to identify the relationship of IHNs' performance to their structural and operational characteristics and integration strategies. Methods: The lists of the top 100 IHNs ranked in two years, 1998 and 1999, by the SMG Marketing Group were merged to create a database for the study. Multiple indicators were used to examine the relationship between IHNs' characteristics and their performance in efficiency and financial viability. A path analytical model was developed and validated by the Mplus statistical program. Factors influencing the top 100 IHNs' images, represented by attaining ranking among the top 100 in two consecutive years, were analysed. Results and conclusion: No positive associations were found between integration and network performance in efficiency or profits. Longitudinal data are needed to investigate the effect of integration on healthcare networks' financial performance.

  10. An integrated artificial neural networks approach for predicting global radiation

    International Nuclear Information System (INIS)

    Azadeh, A.; Maghsoudi, A.; Sohrabkhani, S.

    2009-01-01

    This article presents an integrated artificial neural network (ANN) approach for predicting solar global radiation by climatological variables. The integrated ANN trains and tests data with multi layer perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where no available measurement equipment. Also, it considers all related climatological and meteorological parameters as input variables. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 6 years (1995-2000) in six nominal cities in Iran. Separate model for each city is considered and the quantity of solar global radiation in each city is calculated. Furthermore an integrated ANN model has been introduced for prediction of solar global radiation. The acquired results of the integrated model have shown high accuracy of about 94%. The results of the integrated model have been compared with traditional angstrom's model to show its considerable accuracy. Therefore, the proposed approach can be used as an efficient tool for prediction of solar radiation in the remote and rural locations with no direct measurement equipment.

  11. TNF-α inhibits trophoblast integration into endothelial cellular networks.

    Science.gov (United States)

    Xu, B; Nakhla, S; Makris, A; Hennessy, A

    2011-03-01

    Preeclampsia has been linked to shallow trophoblast invasion and failure of uterine spiral artery transformation. Interaction between trophoblast cells and maternal uterine endothelium is critically important for this remodelling. The aim of our study was to investigate the effect of TNF-α on the interactions of trophoblast-derived JEG-3 cells into capillary-like cellular networks. We have employed an in vitro trophoblast-endothelial cell co-culture model to quantify trophoblast integration into endothelial cellular networks and to investigate the effects of TNF-α. Controlled co-cultures were also treated with anti-TNF-α antibody (5 μg/ml) to specifically block the effect of TNF-α. The invasion was evaluated by performing quantitative PCR (Q-PCR) to analyse gene expression of matrix metalloproteinases-2 (MMP-2), MMP-9, tissue inhibitor of matrix metalloproteinase (TIMP)-1, integrins (α(1)β(1) and α(6)β(4)), plasminogen activator inhibitor (PAI)-1, E-cadherin and VE-cadherin. JEG-3 cell integration into endothelial networks was significantly inhibited by exogenous TNF-α. The inhibition was observed in the range of 0.2-5 ng/ml, to a maximum 56% inhibition at the highest concentration. This inhibition was reversed by anti-TNF-α antibody. Q-PCR analysis showed that mRNA expression of integrins α(1)β(1) and MMP-2 was significantly decreased. VE-cadherin mRNA expression was significantly up-regulated (32-80%, p integration into maternal endothelial cellular networks, and this process involves the inhibition of MMP-2 and a failure of integrins switch from α(6)β(4) to α(1)β(1.) These molecular correlations reflect the changes identified in human preeclampsia. Copyright © 2011 Elsevier Ltd. All rights reserved.

  12. The Integration of Bacteriorhodopsin Proteins with Semiconductor Heterostructure Devices

    Science.gov (United States)

    Xu, Jian

    2008-03-01

    Bioelectronics has emerged as one of the most rapidly developing fields among the active frontiers of interdisciplinary research. A major thrust in this field is aimed at the coupling of the technologically-unmatched performance of biological systems, such as neural and sensing functions, with the well developed technology of microelectronics and optoelectronics. To this end we have studied the integration of a suitably engineered protein, bacteriorhodopsin (BR), with semiconductor optoelectronic devices and circuits. Successful integration will potentially lead to ultrasensitive sensors with polarization selectivity and built-in preprocessing capabilities that will be useful for high speed tracking, motion and edge detection, biological detection, and artificial vision systems. In this presentation we will summarize our progresses in this area, which include fundamental studies on the transient dynamics of photo-induced charge shift in BR and the coupling mechanism at protein-semiconductor interface for effective immobilizing and selectively integrating light sensitive proteins with microelectronic devices and circuits, and the device engineering of BR-transistor-integrated optical sensors as well as their applications in phototransceiver circuits. Work done in collaboration with Pallab Bhattacharya, Jonghyun Shin, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI; Robert R. Birge, Department of Chemistry, University of Connecticut, Storrs, CT 06269; and György V'ar'o, Institute of Biophysics, Biological Research Center of the Hungarian Academy of Science, H-6701 Szeged, Hungary.

  13. Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

    Directory of Open Access Journals (Sweden)

    Stavros I. Dimitriadis

    2017-12-01

    Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node

  14. PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine.

    Directory of Open Access Journals (Sweden)

    Ji-Hyun Lee

    Full Text Available Despite the growing attention given to Traditional Medicine (TM worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound, disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software and BioMart (a data federation framework for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org.

  15. Network Based Integrated Analysis of Phenotype-Genotype Data for Prioritization of Candidate Symptom Genes

    Directory of Open Access Journals (Sweden)

    Xing Li

    2014-01-01

    Full Text Available Background. Symptoms and signs (symptoms in brief are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM. To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC (area under curve 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.

  16. Modeling and Simulation of Handover Scheme in Integrated EPON-WiMAX Networks

    DEFF Research Database (Denmark)

    Yan, Ying; Dittmann, Lars

    2011-01-01

    In this paper, we tackle the seamless handover problem in integrated optical wireless networks. Our model applies for the convergence network of EPON and WiMAX and a mobilityaware signaling protocol is proposed. The proposed handover scheme, Integrated Mobility Management Scheme (IMMS), is assisted...... by enhancing the traditional MPCP signaling protocol, which cooperatively collects mobility information from the front-end wireless network and makes centralized bandwidth allocation decisions in the backhaul optical network. The integrated network architecture and the joint handover scheme are simulated using...... OPNET modeler. Results show validation of the protocol, i.e., integrated handover scheme gains better network performances....

  17. Development of a Deterministic Optimization Model for Design of an Integrated Utility and Hydrogen Supply Network

    International Nuclear Information System (INIS)

    Hwangbo, Soonho; Lee, In-Beum; Han, Jeehoon

    2014-01-01

    Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network

  18. Unravelling Protein-Protein Interaction Networks Linked to Aliphatic and Indole Glucosinolate Biosynthetic Pathways in Arabidopsis

    Directory of Open Access Journals (Sweden)

    Sebastian J. Nintemann

    2017-11-01

    Full Text Available Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches—split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens—to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link between the glucosinolate defense compounds and defense against biotrophic pathogens, mediated by protein-protein interactions.

  19. Students' network integration vs. persistence in introductory physics courses

    Science.gov (United States)

    Zwolak, Justyna; Brewe, Eric

    2017-01-01

    Society is constantly in flux, which demands the continuous development of our educational system to meet new challenges and impart the appropriate knowledge/skills to students. In order to improve student learning, among other things, the way we are teaching has significantly changed over the past few decades. We are moving away from traditional, lecture-based teaching towards more interactive, engagement-based strategies. A current, major challenge for universities is to increase student retention. While students' academic and social integration into an institution seems to be vital for student retention, research on the effect of interpersonal interactions is rare. I use of network analysis to investigate academic and social experiences of students in and beyond the classroom. In particular, there is a compelling case that transformed physics classes, such as Modeling Instruction (MI), promote persistence by the creation of learning communities that support the integration of students into the university. I will discuss recent results on pattern development in networks of MI students' interactions throughout the semester, as well as the effect of students' position within the network on their persistence in physics.

  20. Probabilistic Inference of Biological Networks via Data Integration

    Directory of Open Access Journals (Sweden)

    Mark F. Rogers

    2015-01-01

    Full Text Available There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel appears to work best, different kernels may contribute complimentary information about interactions: experiments in S. cerevisiae (yeast reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.

  1. Development of optical packet and circuit integrated ring network testbed.

    Science.gov (United States)

    Furukawa, Hideaki; Harai, Hiroaki; Miyazawa, Takaya; Shinada, Satoshi; Kawasaki, Wataru; Wada, Naoya

    2011-12-12

    We developed novel integrated optical packet and circuit switch-node equipment. Compared with our previous equipment, a polarization-independent 4 × 4 semiconductor optical amplifier switch subsystem, gain-controlled optical amplifiers, and one 100 Gbps optical packet transponder and seven 10 Gbps optical path transponders with 10 Gigabit Ethernet (10GbE) client-interfaces were newly installed in the present system. The switch and amplifiers can provide more stable operation without equipment adjustments for the frequent polarization-rotations and dynamic packet-rate changes of optical packets. We constructed an optical packet and circuit integrated ring network testbed consisting of two switch nodes for accelerating network development, and we demonstrated 66 km fiber transmission and switching operation of multiplexed 14-wavelength 10 Gbps optical paths and 100 Gbps optical packets encapsulating 10GbE frames. Error-free (frame error rate optical packets of various packet lengths and packet rates, and stable operation of the network testbed was confirmed. In addition, 4K uncompressed video streaming over OPS links was successfully demonstrated. © 2011 Optical Society of America

  2. Production of animal and vegetable proteins: an integrated thermal approach

    Energy Technology Data Exchange (ETDEWEB)

    Kesari, J P; Bonvehi, F; De Saint-Salvy, A; Miquel, J F

    1984-01-01

    For the optimization of our integrated farm, theoretical models using a microcomputer and experimental tests to verify these models were carried out on two research units. A test cell integrated with a greenhouse and a rock bed and a standard rock bed coupled with solar air collectors. A complete wooden house has been constructed and experimented in a remote village 200 km north of Toulouse as part of a demonstration unit. The geese and the Lemna minor (duckweed) have been selected as an animal and as a vegetable for the protein production. Some of the experimental results are reported.

  3. A network model to correlate conformational change and the impedance spectrum of single proteins

    Energy Technology Data Exchange (ETDEWEB)

    Alfinito, Eleonora; Pennetta, Cecilia; Reggiani, Lino [Dipartimento di Ingegneria dell' Innovazione, Universita del Salento, Via Arnesano, Lecce (Italy); Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia (CNISM) (Italy)

    2008-02-13

    Integrated nanodevices based on proteins or biomolecules are attracting increasing interest in today's research. In fact, it has been shown that proteins such as azurin and bacteriorhodopsin manifest some electrical properties that are promising for the development of active components of molecular electronic devices. Here we focus on two relevant kinds of protein: bovine rhodopsin, prototype of G-protein-coupled-receptor (GPCR) proteins, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer's disease. Both these proteins exert their function starting with a conformational change of their native structure. Our guess is that such a change should be accompanied with a detectable variation of their electrical properties. To investigate this conjecture, we present an impedance network model of proteins, able to estimate the different impedance spectra associated with the different configurations. The distinct types of conformational change of rhodopsin and AChE agree with their dissimilar electrical responses. In particular, for rhodopsin the model predicts variations of the impedance spectra up to about 30%, while for AChE the same variations are limited to about 10%, which supports the existence of a dynamical equilibrium between its native and complexed states.

  4. A local network integrated into a balloon-borne apparatus

    Science.gov (United States)

    Imori, Masatosi; Ueda, Ikuo; Shimamura, Kotaro; Maeno, Tadashi; Murata, Takahiro; Sasaki, Makoto; Matsunaga, Hiroyuki; Matsumoto, Hiroshi; Shikaze, Yoshiaki; Anraku, Kazuaki; Matsui, Nagataka; Yamagami, Takamasa

    A local network is incorporated into an apparatus for a balloon-borne experiment. A balloon-borne system implemented in the apparatus is composed of subsystems interconnected through a local network, which introduces modular architecture into the system. The network decomposes the balloon-borne system into subsystems, which are similarly structured from the point of view that the systems is kept under the control of a ground station. The subsystem is functionally self-contained and electrically independent. A computer is integrated into a subsystem, keeping the subsystem under the control. An independent group of batteries, being dedicated to a subsystem, supplies the whole electricity of the subsystem. The subsystem could be turned on and off independently of the other subsystems. So communication among the subsystems needs to be based on such a protocol that could guarantee the independence of the individual subsystems. An Omninet protocol is employed to network the subsystems. A ground station sends commands to the balloon-borne system. The command is received and executed at the system, then results of the execution are returned to the ground station. Various commands are available so that the system borne on a balloon could be controlled and monitored remotely from the ground station. A subsystem responds to a specific group of commands. A command is received by a transceiver subsystem and then transferred through the network to the subsystem to which the command is addressed. Then the subsystem executes the command and returns results to the transceiver subsystem, where the results are telemetered to the ground station. The network enhances independence of the individual subsystems, which enables programs of the individual subsystems to be coded independently. Independence facilitates development and debugging of programs, improving the quality of the system borne on a balloon.

  5. Duplicate retention in signalling proteins and constraints from network dynamics.

    Science.gov (United States)

    Soyer, O S; Creevey, C J

    2010-11-01

    Duplications are a major driving force behind evolution. Most duplicates are believed to fix through genetic drift, but it is not clear whether this process affects all duplications equally or whether there are certain gene families that are expected to show neutral expansions under certain circumstances. Here, we analyse the neutrality of duplications in different functional classes of signalling proteins based on their effects on response dynamics. We find that duplications involving intermediary proteins in a signalling network are neutral more often than those involving receptors. Although the fraction of neutral duplications in all functional classes increase with decreasing population size and selective pressure on dynamics, this effect is most pronounced for receptors, indicating a possible expansion of receptors in species with small population size. In line with such an expectation, we found a statistically significant increase in the number of receptors as a fraction of genome size in eukaryotes compared with prokaryotes. Although not confirmative, these results indicate that neutral processes can be a significant factor in shaping signalling networks and affect proteins from different functional classes differently. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.

  6. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  7. [Comprehensive system integration and networking in operating rooms].

    Science.gov (United States)

    Feußner, H; Ostler, D; Kohn, N; Vogel, T; Wilhelm, D; Koller, S; Kranzfelder, M

    2016-12-01

    A comprehensive surveillance and control system integrating all devices and functions is a precondition for realization of the operating room of the future. Multiple proprietary integrated operation room systems are currently available with a central user interface; however, they only cover a relatively small part of all functionalities. Internationally, there are at least three different initiatives to promote a comprehensive systems integration and networking in the operating room: the Japanese smart cyber operating theater (SCOT), the American medical device plug-and-play interoperability program (MDPnP) and the German secure and dynamic networking in operating room and hospital (OR.NET) project supported by the Federal Ministry of Education and Research. Within the framework of the internationally advanced OR.NET project, prototype solution approaches were realized, which make short-term and mid-term comprehensive data retrieval systems probable. An active and even autonomous control of the medical devices by the surveillance and control system (closed loop) is expected only in the long run due to strict regulatory barriers.

  8. Survivable integrated grooming in multi-granularity optical networks

    Science.gov (United States)

    Wu, Jingjing; Guo, Lei; Wei, Xuetao; Liu, Yejun

    2012-05-01

    Survivability is an important issue to ensure the service continuity in optical network. At the same time, with the granularity of traffic demands ranging from sub-wavelength-level to wavelength-level, traffic demands need to be aggregated and carried over the network in order to utilize resources effectively. Therefore, multi-granularity grooming is proposed to save the cost and reduce the number of switching ports in Optical-Cross Connects (OXCs). However, current works mostly addressed the survivable wavelength or waveband grooming. Therefore, in this paper, we propose three heuristic algorithms called Multi-granularity Dedicated Protection Grooming (MDPG), Multi-granularity Shared Protection Grooming (MSPG) and Multi-granularity Mixed Protection Grooming (MMPG), respectively. All of them are performed based on the Survivable Multi-granularity Integrated Auxiliary Graph (SMIAG) that includes one Wavelength Integrated Auxiliary Graph (WIAG) for wavelength protection and one waveBand Integrated Auxiliary Graph (BIAG) for waveband protection. Numerical results show that MMPG has the lowest average port-cost, the best resource utilization ratio and the lowest blocking probability among these three algorithms. Compared with MDPG, MSPG has lower average port-cost, better resource utilization ratio and lower blocking probability.

  9. Convolutional neural network architectures for predicting DNA–protein binding

    Science.gov (United States)

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  10. Analysis of network motifs in cellular regulation: Structural similarities, input-output relations and signal integration.

    Science.gov (United States)

    Straube, Ronny

    2017-12-01

    Much of the complexity of regulatory networks derives from the necessity to integrate multiple signals and to avoid malfunction due to cross-talk or harmful perturbations. Hence, one may expect that the input-output behavior of larger networks is not necessarily more complex than that of smaller network motifs which suggests that both can, under certain conditions, be described by similar equations. In this review, we illustrate this approach by discussing the similarities that exist in the steady state descriptions of a simple bimolecular reaction, covalent modification cycles and bacterial two-component systems. Interestingly, in all three systems fundamental input-output characteristics such as thresholds, ultrasensitivity or concentration robustness are described by structurally similar equations. Depending on the system the meaning of the parameters can differ ranging from protein concentrations and affinity constants to complex parameter combinations which allows for a quantitative understanding of signal integration in these systems. We argue that this approach may also be extended to larger regulatory networks. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

    Science.gov (United States)

    Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

    2010-12-01

    The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  12. Designing a Fuzzy Strategic Integrated Multiechelon Agile Supply Chain Network

    Directory of Open Access Journals (Sweden)

    Morteza Abbasi

    2013-01-01

    Full Text Available This paper integrates production, distribution and logistics activities at the strategic decision making level, where the objective is to design a multiechelon supply chain network considering agility as a key design criterion. A network with five echelons of supply chains including suppliers, plants, distribution centers, cross-docks, and customer zones is addressed in this paper. The problem has been mathematically formulated as a biobjective optimization model that aims to minimize the cost (fixed and variable and maximize the plant flexibility and volume flexibility. A novel multiobjective parallel simulating annealing algorithm (MOPSA is proposed to obtain the Pareto-optimal solutions of the problem. The performance of the proposed solution algorithm is compared with two well-known metaheuristics, namely, nondominated sorting genetic algorithm (NSGA-II and Pareto archive evolution strategy (PAES. Computational results show that MOPSA outperforms the other metaheuristics.

  13. Complexity and network dynamics in physiological adaptation: an integrated view.

    Science.gov (United States)

    Baffy, György; Loscalzo, Joseph

    2014-05-28

    Living organisms constantly interact with their surroundings and sustain internal stability against perturbations. This dynamic process follows three fundamental strategies (restore, explore, and abandon) articulated in historical concepts of physiological adaptation such as homeostasis, allostasis, and the general adaptation syndrome. These strategies correspond to elementary forms of behavior (ordered, chaotic, and static) in complex adaptive systems and invite a network-based analysis of the operational characteristics, allowing us to propose an integrated framework of physiological adaptation from a complex network perspective. Applicability of this concept is illustrated by analyzing molecular and cellular mechanisms of adaptation in response to the pervasive challenge of obesity, a chronic condition resulting from sustained nutrient excess that prompts chaotic exploration for system stability associated with tradeoffs and a risk of adverse outcomes such as diabetes, cardiovascular disease, and cancer. Deconstruction of this complexity holds the promise of gaining novel insights into physiological adaptation in health and disease. Published by Elsevier Inc.

  14. Category theoretic analysis of hierarchical protein materials and social networks.

    Directory of Open Access Journals (Sweden)

    David I Spivak

    Full Text Available Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a "concept web" or "semantic network" except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.

  15. Neuronal oscillations form parietal/frontal networks during contour integration.

    Science.gov (United States)

    Castellano, Marta; Plöchl, Michael; Vicente, Raul; Pipa, Gordon

    2014-01-01

    The ability to integrate visual features into a global coherent percept that can be further categorized and manipulated are fundamental abilities of the neural system. While the processing of visual information involves activation of early visual cortices, the recruitment of parietal and frontal cortices has been shown to be crucial for perceptual processes. Yet is it not clear how both cortical and long-range oscillatory activity leads to the integration of visual features into a coherent percept. Here, we will investigate perceptual grouping through the analysis of a contour categorization task, where the local elements that form contour must be linked into a coherent structure, which is then further processed and manipulated to perform the categorization task. The contour formation in our visual stimulus is a dynamic process where, for the first time, visual perception of contours is disentangled from the onset of visual stimulation or from motor preparation, cognitive processes that until now have been behaviorally attached to perceptual processes. Our main finding is that, while local and long-range synchronization at several frequencies seem to be an ongoing phenomena, categorization of a contour could only be predicted through local oscillatory activity within parietal/frontal sources, which in turn, would synchronize at gamma (>30 Hz) frequency. Simultaneously, fronto-parietal beta (13-30 Hz) phase locking forms a network spanning across neural sources that are not category specific. Both long range networks, i.e., the gamma network that is category specific, and the beta network that is not category specific, are functionally distinct but spatially overlapping. Altogether, we show that a critical mechanism underlying contour categorization involves oscillatory activity within parietal/frontal cortices, as well as its synchronization across distal cortical sites.

  16. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    Science.gov (United States)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  17. A membrane protein / signaling protein interaction network for Arabidopsis version AMPv2

    Directory of Open Access Journals (Sweden)

    Sylvie Lalonde

    2010-09-01

    Full Text Available Interactions between membrane proteins and the soluble fraction are essential for signal transduction and for regulating nutrient transport. To gain insights into the membrane-based interactome, 3,852 open reading frames (ORFs out of a target list of 8,383 representing membrane and signaling proteins from Arabidopsis thaliana were cloned into a Gateway compatible vector. The mating-based split-ubiquitin system was used to screen for potential protein-protein interactions (pPPIs among 490 Arabidopsis ORFs. A binary robotic screen between 142 receptor-like kinases, 72 transporters, 57 soluble protein kinases and phosphatases, 40 glycosyltransferases, 95 proteins of various functions and 89 proteins with unknown function detected 387 out of 90,370 possible PPIs. A secondary screen confirmed 343 (of 387 pPPIs between 179 proteins, yielding a scale-free network (r2=0.863. Eighty of 142 transmembrane receptor-like kinases (RLK tested positive, identifying three homomers, 63 heteromers and 80 pPPIs with other proteins. Thirty-one out of 142 RLK interactors (including RLKs had previously been found to be phosphorylated; thus interactors may be substrates for respective RLKs. None of the pPPIs described here had been reported in the major interactome databases, including potential interactors of G protein-coupled receptors, phospholipase C, and AMT ammonium transporters. Two RLKs found as putative interactors of AMT1;1 were independently confirmed using a split luciferase assay in Arabidopsis protoplasts. These RLKs may be involved in ammonium-dependent phosphorylation of the C-terminus and regulation of ammonium uptake activity. The robotic screening method established here will enable a systematic analysis of membrane protein interactions in fungi, plants and metazoa.

  18. Deep recurrent conditional random field network for protein secondary prediction

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Sønderby, Søren Kaae; Sønderby, Casper Kaae

    2017-01-01

    Deep learning has become the state-of-the-art method for predicting protein secondary structure from only its amino acid residues and sequence profile. Building upon these results, we propose to combine a bi-directional recurrent neural network (biRNN) with a conditional random field (CRF), which...... of the labels for all time-steps. We condition the CRF on the output of biRNN, which learns a distributed representation based on the entire sequence. The biRNN-CRF is therefore close to ideally suited for the secondary structure task because a high degree of cross-talk between neighboring elements can...

  19. Integration of social networks in the teaching and learning process

    Directory of Open Access Journals (Sweden)

    Cynthia Dedós Reyes

    2015-09-01

    Full Text Available In this research we explored the integration of social media in the process of learning and teaching, in a private higher education institution, in Puerto Rico. Attention was given to the perspectives of teachers and students. The participants —9 part-time teachers and 118 students— were selected based on availability. The results showed that teachers and students alike use social the network You Tube for academic purposes; and use Facebook, Twitter, and blogs for social purposes and entertainment. Results also revealed that there is no significant contrast between the perspectives of teachers and students digital immigrants.

  20. Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

    Science.gov (United States)

    Chen, Jinmiao; Chaudhari, Narendra

    2007-01-01

    Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

  1. The role of exon shuffling in shaping protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    França Gustavo S

    2010-12-01

    Full Text Available Abstract Background Physical protein-protein interaction (PPI is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains, self-interacting (able to interact with another copy of themselves and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

  2. Protein networks in induced sputum from smokers and COPD patients

    Directory of Open Access Journals (Sweden)

    Baraniuk JN

    2015-09-01

    Full Text Available James N Baraniuk,1 Begona Casado,1 Lewis K Pannell,2 Peter B McGarvey,3 Piera Boschetto,4 Maurizio Luisetti,5,† Paolo Iadarola6 1Division of Rheumatology, Immunology and Allergy, Georgetown University, Washington, DC, 2Proteomics and Mass Spectrometry Laboratory, Mitchell Cancer Center, University of South Alabama, Mobile, AL, 3Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA; 4Department of Medical Sciences, University of Ferrara, Ferrara, 5SC Pneumologia, Dipartimento Medicina Molecolare, Fondazione IRCCS Policlinico San Matteo, 6Lazzaro Spallanzani Department of Biology and Biotechnology, University of Pavia, Pavia, Italy †Maurizio Luisetti passed away on October 20, 2014 Rationale: Subtypes of cigarette smoke-induced disease affect different lung structures and may have distinct pathophysiological mechanisms. Objective: To determine if proteomic classification of the cellular and vascular origins of sputum proteins can characterize these mechanisms and phenotypes. Subjects and methods: Individual sputum specimens from lifelong nonsmokers (n=7 and smokers with normal lung function (n=13, mucous hypersecretion with normal lung function (n=11, obstructed airflow without emphysema (n=15, and obstruction plus emphysema (n=10 were assessed with mass spectrometry. Data reduction, logarithmic transformation of spectral counts, and Cytoscape network-interaction analysis were performed. The original 203 proteins were reduced to the most informative 50. Sources were secretory dimeric IgA, submucosal gland serous and mucous cells, goblet and other epithelial cells, and vascular permeability. Results: Epithelial proteins discriminated nonsmokers from smokers. Mucin 5AC was elevated in healthy smokers and chronic bronchitis, suggesting a continuum with the severity of hypersecretion determined by mechanisms of goblet-cell hyperplasia. Obstructed airflow was correlated with glandular proteins and lower levels of

  3. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Ubiquitous Integrity via Network Integration and Parallelism—Sustaining Pedestrian/Bike Urbanism

    Directory of Open Access Journals (Sweden)

    Li-Yen Hsu

    2013-08-01

    Full Text Available Nowadays, due to the concern regarding environmental issues, establishing pedestrian/bike friendly urbanism is widely encouraged. To promote safety-assured, mobile communication environments, efficient, reliable maintenance, and information integrity need to be designed, especially in highly possibly interfered places. For busy traffic areas, regular degree-3 dedicated short range communication (DSRC networks are safety and information featured with availability, reliability, and maintainability in paths of multi-lanes. For sparsely populated areas, probes of wireless sensors are rational, especially if sensor nodes can be organized to enhance security, reliability, and flexibility. Applying alternative network topologies, such as spider-webs, generalized honeycomb tori, and cube-connected cycles, for comparing and analyzing is proposed in DSRC and cellular communications to enhance integrity in communications.

  5. Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

    Science.gov (United States)

    Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

    2013-01-01

    DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

  6. Social networks in nursing work processes: an integrative literature review.

    Science.gov (United States)

    Mesquita, Ana Cláudia; Zamarioli, Cristina Mara; Fulquini, Francine Lima; Carvalho, Emilia Campos de; Angerami, Emilia Luigia Saporiti

    2017-03-20

    To identify and analyze the available evidence in the literature on the use of social networks in nursing work processes. An integrative review of the literature conducted in PubMed, CINAHL, EMBASE and LILACS databases in January 2016, using the descriptors social media, social networking, nursing, enfermagem, redes sociais, mídias sociais, and the keyword nursing practice, without year restriction. The sample consisted of 27 international articles which were published between 2011 and 2016. The social networks used were Facebook (66.5%), Twitter (30%) and WhatsApp (3.5%). In 70.5% of the studies, social networks were used for research purposes, in 18.5% they were used as a tool aimed to assist students in academic activities, and in 11% for executing interventions via the internet. Nurses have used social networks in their work processes such as Facebook, Twitter and WhatsApp to research, teach and watch. The articles show several benefits in using such tools in the nursing profession; however, ethical considerations regarding the use of social networks deserve further discussion. Identificar e analisar as evidências disponíveis na literatura sobre a utilização de redes sociais nos processos de trabalho em enfermagem. Revisão integrativa da literatura realizada em janeiro de 2016, nas bases de dados PubMed, CINAHL, EMBASE e LILACS, com os descritores social media, social networking, nursing, enfermagem, redes sociais, mídias sociais e a palavra-chave nursing practice, sem restrição de ano. A amostra foi composta por 27 artigos, os quais foram publicados entre 2011 e 2016, todos internacionais. As redes sociais utilizadas foram o Facebook (66,5%), o Twitter (30%) e o WhatsApp (3,5%). Em 70,5% dos estudos as redes sociais foram utilizadas para fins de pesquisa, em 18,5% como ferramenta para auxiliar estudantes nas atividades acadêmicas, e em 11% para a realização de intervenções via internet. Em seus processos de trabalho, os enfermeiros têm utilizado

  7. Graph theoretic analysis of protein interaction networks of eukaryotes

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2005-11-01

    Owing to the recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high-throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are conserved across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interologs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interolog additions, for which we present a possible scenario through an in silico modeling.

  8. Towards a map of the Populus biomass protein-protein interaction network

    Energy Technology Data Exchange (ETDEWEB)

    Beers, Eric [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Brunner, Amy [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Helm, Richard [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Dickerman, Allan [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

    2015-07-31

    -depth characterizations. Characterizations involved both in vivo and in vitro independent methods to confirm protein-protein interactions and the evaluation of novel phenotypes resulting from creation of transgenic poplar and Arabidopsis plants engineered for increased or decreased expression of the selected genes. Transgenic poplar trees were studied in growth chamber, greenhouse, and two separate replicated field trials involving over 25 distinct wood-associated proteins. In-depth characterizations yielding positive results include the following. First, a NAC domain transcription factor (NAC154) that is a promoter of stress response and dormancy in trees was discovered. Increasing expression of NAC154 caused stunted growth and premature senescence, while decreasing expression led to both delayed bud and leaf expansion in spring and delayed leaf drop (i.e., prolonged leaf retention) in fall. Second, we discovered and characterized a new connection between a negative regulator of wood formation, the NAC domain transcription factor XND1, and an important regulator of cell division and cell differentiation, RBR. Third, we identified a new network of interacting wood-associated transcription factors belonging to the MYB and HD families. One of the HD family proteins, WOX13, was used to prepare transgenic poplar for high-level expression, resulting in significantly increased lateral branch growth. Finally, we modeled and performed in vitro analyses of the insect protein rubber resilin and we prepared transgenic Arabidopsis plants for expression of resilin to test the feasibility of using resilin to modify lignin cross-linking in wood and reduce recalcitrance and improve yield of fermentable sugars for biofuels production. Analysis of these and additional transgenics created with this support is continuing.

  9. Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

    Directory of Open Access Journals (Sweden)

    Guo Hao

    2011-05-01

    Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

  10. A human protein interaction network shows conservation of aging processes between human and invertebrate species.

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    Russell Bell

    2009-03-01

    Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

  11. Australian national networked tele-test facility for integrated systems

    Science.gov (United States)

    Eshraghian, Kamran; Lachowicz, Stefan W.; Eshraghian, Sholeh

    2001-11-01

    The Australian Commonwealth government recently announced a grant of 4.75 million as part of a 13.5 million program to establish a world class networked IC tele-test facility in Australia. The facility will be based on a state-of-the-art semiconductor tester located at Edith Cowan University in Perth that will operate as a virtual centre spanning Australia. Satellite nodes will be located at the University of Western Australia, Griffith University, Macquarie University, Victoria University and the University of Adelaide. The facility will provide vital equipment to take Australia to the frontier of critically important and expanding fields in microelectronics research and development. The tele-test network will provide state of the art environment for the electronics and microelectronics research and the industry community around Australia to test and prototype Very Large Scale Integrated (VLSI) circuits and other System On a Chip (SOC) devices, prior to moving to the manufacturing stage. Such testing is absolutely essential to ensure that the device performs to specification. This paper presents the current context in which the testing facility is being established, the methodologies behind the integration of design and test strategies and the target shape of the tele-testing Facility.

  12. NETWORK CULTURE - INTEGRAL PART OF NEW VALUES OF CIVIL SOCIETY

    Directory of Open Access Journals (Sweden)

    Vyacheslav Vladimirovich Sukhanov

    2014-06-01

    Full Text Available New technologies not only improve working conditions or communication, they are also bringing new values to  the society. This article discusses the concept of «network culture», which is now perceived by society as an integral part of values that can only exist in a civil society. We can research ( find   this kind of society in modern time period in Russia. The article analyzes the meaning of communication, how to use it, development processes in network media.  Nowadays network culture and its influence on society are almost in all spheres of state and society, as well as changes in perception of information and content generation process. Development of the Internet and Internet technologies largely set the tone for  the development of popular culture and allows you to store or to influence the national culture. Internet press today is and example which shows us, how this kind of tool can be used to influence on citizens.DOI: http://dx.doi.org/10.12731/2218-7405-2014-3-6

  13. Clustering and visualizing similarity networks of membrane proteins.

    Science.gov (United States)

    Hu, Geng-Ming; Mai, Te-Lun; Chen, Chi-Ming

    2015-08-01

    We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. © 2015 Wiley Periodicals, Inc.

  14. Integrative network analysis unveils convergent molecular pathways in Parkinson's disease and diabetes.

    Science.gov (United States)

    Santiago, Jose A; Potashkin, Judith A

    2013-01-01

    Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level. Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients. These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that

  15. Integrative network analysis unveils convergent molecular pathways in Parkinson's disease and diabetes.

    Directory of Open Access Journals (Sweden)

    Jose A Santiago

    Full Text Available Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level.Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP, previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS and the Prognostic Biomarker Study (PROBE, revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients.These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first

  16. MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

    Directory of Open Access Journals (Sweden)

    Wasserman Wyeth W

    2011-03-01

    Full Text Available Abstract Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs, microRNAs (miRNAs and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs. Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL. In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT, an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http

  17. Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2016-10-06

    Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

  18. Weighted Protein Interaction Network Analysis of Frontotemporal Dementia.

    Science.gov (United States)

    Ferrari, Raffaele; Lovering, Ruth C; Hardy, John; Lewis, Patrick A; Manzoni, Claudia

    2017-02-03

    The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein-protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery.

  19. Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

    Science.gov (United States)

    Cao, HuanHuan; Zhang, YuHang; Zhao, Jia; Zhu, Liucun; Wang, Yi; Li, JiaRui; Feng, Yuan-Ming; Zhang, Ning

    2017-01-01

    Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Developing integrated crop knowledge networks to advance candidate gene discovery.

    Science.gov (United States)

    Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

    2016-12-01

    The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

  1. Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    He Weiming

    2010-07-01

    Full Text Available Abstract Background Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions. Results Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways. Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods. Conclusions Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

  2. Design and implementation of interface units for high speed fiber optics local area networks and broadband integrated services digital networks

    Science.gov (United States)

    Tobagi, Fouad A.; Dalgic, Ismail; Pang, Joseph

    1990-01-01

    The design and implementation of interface units for high speed Fiber Optic Local Area Networks and Broadband Integrated Services Digital Networks are discussed. During the last years, a number of network adapters that are designed to support high speed communications have emerged. This approach to the design of a high speed network interface unit was to implement package processing functions in hardware, using VLSI technology. The VLSI hardware implementation of a buffer management unit, which is required in such architectures, is described.

  3. Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

    Science.gov (United States)

    Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

    2017-10-01

    The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

  4. ProNormz--an integrated approach for human proteins and protein kinases normalization.

    Science.gov (United States)

    Subramani, Suresh; Raja, Kalpana; Natarajan, Jeyakumar

    2014-02-01

    The task of recognizing and normalizing protein name mentions in biomedical literature is a challenging task and important for text mining applications such as protein-protein interactions, pathway reconstruction and many more. In this paper, we present ProNormz, an integrated approach for human proteins (HPs) tagging and normalization. In Homo sapiens, a greater number of biological processes are regulated by a large human gene family called protein kinases by post translational phosphorylation. Recognition and normalization of human protein kinases (HPKs) is considered to be important for the extraction of the underlying information on its regulatory mechanism from biomedical literature. ProNormz distinguishes HPKs from other HPs besides tagging and normalization. To our knowledge, ProNormz is the first normalization system available to distinguish HPKs from other HPs in addition to gene normalization task. ProNormz incorporates a specialized synonyms dictionary for human proteins and protein kinases, a set of 15 string matching rules and a disambiguation module to achieve the normalization. Experimental results on benchmark BioCreative II training and test datasets show that our integrated approach achieve a fairly good performance and outperforms more sophisticated semantic similarity and disambiguation systems presented in BioCreative II GN task. As a freely available web tool, ProNormz is useful to developers as extensible gene normalization implementation, to researchers as a standard for comparing their innovative techniques, and to biologists for normalization and categorization of HPs and HPKs mentions in biomedical literature. URL: http://www.biominingbu.org/pronormz. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. Integrating protein engineering with process design for biocatalysis

    DEFF Research Database (Denmark)

    Woodley, John M.

    2017-01-01

    Biocatalysis uses enzymes for chemical synthesis and production, offering selective, safe and sustainable catalysis. While today the majority of applications are in the pharmaceutical sector, new opportunities are arising every day in other industry sectors, where production costs become a more...... important driver. In the early applications of the technology, it was necessary to design processes to match the properties of the biocatalyst. With the advent of protein engineering, organic chemists started to develop and improve enzymes to suit their needs. Likewise in industry, although not widespread......, a new paradigm was already implemented several years ago to engineer enzymes to suit process needs. Today, a new era is entered, where the effectiveness with which such integrated protein and process engineering is achieved becomes critical to implementation. In this paper, the development of a tool...

  6. Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks

    Science.gov (United States)

    2012-09-21

    Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks Paulo Shakarian1*, J. Kenneth Wickiser2 1 Paulo Shakarian...significantly attacked. Citation: Shakarian P, Wickiser JK (2012) Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks...to 00-00-2012 4. TITLE AND SUBTITLE Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks 5a. CONTRACT NUMBER 5b

  7. SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets.

    Science.gov (United States)

    Gosline, Sara J C; Spencer, Sarah J; Ursu, Oana; Fraenkel, Ernest

    2012-11-01

    The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein-protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT.

  8. Network architectures and protocols for the integration of ACTS and ISDN

    Science.gov (United States)

    Chitre, D. M.; Lowry, P. A.

    1992-01-01

    A close integration of satellite networks and the integrated services digital network (ISDN) is essential for satellite networks to carry ISDN traffic effectively. This also shows how a given (pre-ISDN) satellite network architecture can be enhanced to handle ISDN signaling and provide ISDN services. It also describes the functional architecture and high-level protocols that could be implemented in the NASA Advanced Communications Technology Satellite (ACTS) low burst rate communications system to provide ISDN services.

  9. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

    Cannistraci, Carlo; Alanis Lobato, Gregorio; Ravasi, Timothy

    2013-01-01

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness

  10. Integration of Decentralized Thermal Storages Within District Heating (DH Networks

    Directory of Open Access Journals (Sweden)

    Schuchardt Georg K.

    2016-12-01

    Full Text Available Thermal Storages and Thermal Accumulators are an important component within District Heating (DH systems, adding flexibility and offering additional business opportunities for these systems. Furthermore, these components have a major impact on the energy and exergy efficiency as well as the heat losses of the heat distribution system. Especially the integration of Thermal Storages within ill-conditioned parts of the overall DH system enhances the efficiency of the heat distribution. Regarding an illustrative and simplified example for a DH system, the interactions of different heat storage concepts (centralized and decentralized and the heat losses, energy and exergy efficiencies will be examined by considering the thermal state of the heat distribution network.

  11. Integrative Analysis of the Physical Transport Network into Australia.

    Directory of Open Access Journals (Sweden)

    Robert C Cope

    Full Text Available Effective biosecurity is necessary to protect nations and their citizens from a variety of threats, including emerging infectious diseases, agricultural or environmental pests and pathogens, and illegal wildlife trade. The physical pathways by which these threats are transported internationally, predominantly shipping and air traffic, have undergone significant growth and changes in spatial distributions in recent decades. An understanding of the specific pathways and donor-traffic hotspots created by this integrated physical transport network is vital for the development of effective biosecurity strategies into the future. In this study, we analysed the physical transport network into Australia over the period 1999-2012. Seaborne and air traffic were weighted to calculate a "weighted cumulative impact" score for each source region worldwide, each year. High risk source regions, and those source regions that underwent substantial changes in risk over the study period, were determined. An overall risk ranking was calculated by integrating across all possible weighting combinations. The source regions having greatest overall physical connectedness with Australia were Singapore, which is a global transport hub, and the North Island of New Zealand, a close regional trading partner with Australia. Both those regions with large amounts of traffic across multiple vectors (e.g., Hong Kong, and those with high levels of traffic of only one type (e.g., Bali, Indonesia with respect to passenger flights, were represented among high risk source regions. These data provide a baseline model for the transport of individuals and commodities against which the effectiveness of biosecurity controls may be assessed, and are a valuable tool in the development of future biosecurity policy.

  12. A framework for integration of heterogeneous medical imaging networks.

    Science.gov (United States)

    Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

    2014-01-01

    Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS.

  13. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2014-06-01

    Full Text Available In this paper, we will propose the neural networks integrated circuit (NNIC which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generates the driving waveform using synchronization phenomena such as biological neural networks. The driving waveform can operate the actuators of the MEMS microrobot directly. Therefore, the NNIC bare chip realizes the robot control without using any software programs or A/D converters. The microrobot performed forward and backward locomotion, and also changes direction by inputting an external single trigger pulse. The locomotion speed of the microrobot was 26.4 mm/min when the step width was 0.88 mm. The power consumption of the system was 250 mWh when the room temperature was 298 K.

  14. Integrating generation and transmission networks reliability for unit commitment solution

    International Nuclear Information System (INIS)

    Jalilzadeh, S.; Shayeghi, H.; Hadadian, H.

    2009-01-01

    This paper presents a new method with integration of generation and transmission networks reliability for the solution of unit commitment (UC) problem. In fact, in order to have a more accurate assessment of system reserve requirement, in addition to unavailability of generation units, unavailability of transmission lines are also taken into account. In this way, evaluation of the required spinning reserve (SR) capacity is performed by applying reliability constraints based on loss of load probability and expected energy not supplied (EENS) indices. Calculation of the above parameters is accomplished by employing a novel procedure based on the linear programming which it also minimizes them to achieve optimum level of the SR capacity and consequently a cost-benefit reliability constrained UC schedule. In addition, a powerful solution technique called 'integer-coded genetic algorithm (ICGA)' is being used for the solution of the proposed method. Numerical results on the IEEE reliability test system show that the consideration of transmission network unavailability has an important influence on reliability indices of the UC schedules

  15. Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

    Science.gov (United States)

    Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

    2011-11-01

    A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

  16. A novel Usher protein network at the periciliary reloading point between molecular transport machineries in vertebrate photoreceptor cells.

    Science.gov (United States)

    Maerker, Tina; van Wijk, Erwin; Overlack, Nora; Kersten, Ferry F J; McGee, Joann; Goldmann, Tobias; Sehn, Elisabeth; Roepman, Ronald; Walsh, Edward J; Kremer, Hannie; Wolfrum, Uwe

    2008-01-01

    The human Usher syndrome (USH) is the most frequent cause of combined deaf-blindness. USH is genetically heterogeneous with at least 12 chromosomal loci assigned to three clinical types, USH1-3. Although these USH types exhibit similar phenotypes in human, the corresponding gene products belong to very different protein classes and families. The scaffold protein harmonin (USH1C) was shown to integrate all identified USH1 and USH2 molecules into protein networks. Here, we analyzed a protein network organized in the absence of harmonin by the scaffold proteins SANS (USH1G) and whirlin (USH2D). Immunoelectron microscopic analyses disclosed the colocalization of all network components in the apical inner segment collar and the ciliary apparatus of mammalian photoreceptor cells. In this complex, whirlin and SANS directly interact. Furthermore, SANS provides a linkage to the microtubule transport machinery, whereas whirlin may anchor USH2A isoform b and VLGR1b (very large G-protein coupled receptor 1b) via binding to their cytodomains at specific membrane domains. The long ectodomains of both transmembrane proteins extend into the gap between the adjacent membranes of the connecting cilium and the apical inner segment. Analyses of Vlgr1/del7TM mice revealed the ectodomain of VLGR1b as a component of fibrous links present in this gap. Comparative analyses of mouse and Xenopus photoreceptors demonstrated that this USH protein network is also part of the periciliary ridge complex in Xenopus. Since this structural specialization in amphibian photoreceptor cells defines a specialized membrane domain for docking and fusion of transport vesicles, we suggest a prominent role of the USH proteins in cargo shipment.

  17. S100A4 and its role in metastasis – computational integration of data on biological networks.

    Science.gov (United States)

    Buetti-Dinh, Antoine; Pivkin, Igor V; Friedman, Ran

    2015-08-01

    Characterising signal transduction networks is fundamental to our understanding of biology. However, redundancy and different types of feedback mechanisms make it difficult to understand how variations of the network components contribute to a biological process. In silico modelling of signalling interactions therefore becomes increasingly useful for the development of successful therapeutic approaches. Unfortunately, quantitative information cannot be obtained for all of the proteins or complexes that comprise the network, which limits the usability of computational models. We developed a flexible computational framework for the analysis of biological signalling networks. We demonstrate our approach by studying the mechanism of metastasis promotion by the S100A4 protein, and suggest therapeutic strategies. The advantage of the proposed method is that only limited information (interaction type between species) is required to set up a steady-state network model. This permits a straightforward integration of experimental information where the lack of details are compensated by efficient sampling of the parameter space. We investigated regulatory properties of the S100A4 network and the role of different key components. The results show that S100A4 enhances the activity of matrix metalloproteinases (MMPs), causing higher cell dissociation. Moreover, it leads to an increased stability of the pathological state. Thus, avoiding metastasis in S100A4-expressing tumours requires multiple target inhibition. Moreover, the analysis could explain the previous failure of MMP inhibitors in clinical trials. Finally, our method is applicable to a wide range of biological questions that can be represented as directional networks.

  18. Monterey Bay National Marine Sanctuary: Sanctuary Integrated Monitoring Network (SIMoN)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Sanctuary Integrated Monitoring Network (SIMoN) is an integrated, long-term program that takes an ecosystem approach to identify and understand changes to the...

  19. SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803

    OpenAIRE

    Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

    2008-01-01

    Background Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date. Description We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactio...

  20. Integrated Approach to Reconstruction of Microbial Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Rodionov, Dmitry A [Sanford-Burnham Medical Research Institute; Novichkov, Pavel S [Lawrence Berkeley National Laboratory

    2013-11-04

    This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated in RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.

  1. A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network

    Directory of Open Access Journals (Sweden)

    De Graaf David

    2007-07-01

    Full Text Available Abstract Background The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. Results Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. Conclusion We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.

  2. Building alternate protein structures using the elastic network model.

    Science.gov (United States)

    Yang, Qingyi; Sharp, Kim A

    2009-02-15

    We describe a method for efficiently generating ensembles of alternate, all-atom protein structures that (a) differ significantly from the starting structure, (b) have good stereochemistry (bonded geometry), and (c) have good steric properties (absence of atomic overlap). The method uses reconstruction from a series of backbone framework structures that are obtained from a modified elastic network model (ENM) by perturbation along low-frequency normal modes. To ensure good quality backbone frameworks, the single force parameter ENM is modified by introducing two more force parameters to characterize the interaction between the consecutive carbon alphas and those within the same secondary structure domain. The relative stiffness of the three parameters is parameterized to reproduce B-factors, while maintaining good bonded geometry. After parameterization, violations of experimental Calpha-Calpha distances and Calpha-Calpha-Calpha pseudo angles along the backbone are reduced to less than 1%. Simultaneously, the average B-factor correlation coefficient improves to R = 0.77. Two applications illustrate the potential of the approach. (1) 102,051 protein backbones spanning a conformational space of 15 A root mean square deviation were generated from 148 nonredundant proteins in the PDB database, and all-atom models with minimal bonded and nonbonded violations were produced from this ensemble of backbone structures using the SCWRL side chain building program. (2) Improved backbone templates for homology modeling. Fifteen query sequences were each modeled on two targets. For each of the 30 target frameworks, dozens of improved templates could be produced In all cases, improved full atom homology models resulted, of which 50% could be identified blind using the D-Fire statistical potential. (c) 2008 Wiley-Liss, Inc.

  3. Arabidopsis protein phosphatase DBP1 nucleates a protein network with a role in regulating plant defense.

    Directory of Open Access Journals (Sweden)

    José Luis Carrasco

    Full Text Available Arabidopsis thaliana DBP1 belongs to the plant-specific family of DNA-binding protein phosphatases. Although recently identified as a novel host factor mediating susceptibility to potyvirus, little is known about DBP1 targets and partners and the molecular mechanisms underlying its function. Analyzing changes in the phosphoproteome of a loss-of-function dbp1 mutant enabled the identification of 14-3-3λ isoform (GRF6, a previously reported DBP1 interactor, and MAP kinase (MAPK MPK11 as components of a small protein network nucleated by DBP1, in which GRF6 stability is modulated by MPK11 through phosphorylation, while DBP1 in turn negatively regulates MPK11 activity. Interestingly, grf6 and mpk11 loss-of-function mutants showed altered response to infection by the potyvirus Plum pox virus (PPV, and the described molecular mechanism controlling GRF6 stability was recapitulated upon PPV infection. These results not only contribute to a better knowledge of the biology of DBP factors, but also of MAPK signalling in plants, with the identification of GRF6 as a likely MPK11 substrate and of DBP1 as a protein phosphatase regulating MPK11 activity, and unveils the implication of this protein module in the response to PPV infection in Arabidopsis.

  4. The Tobacco mosaic virus Movement Protein Associates with but Does Not Integrate into Biological Membranes

    Science.gov (United States)

    Peiró, Ana; Martínez-Gil, Luis; Tamborero, Silvia; Pallás, Vicente

    2014-01-01

    ABSTRACT Plant positive-strand RNA viruses require association with plant cell endomembranes for viral translation and replication, as well as for intra- and intercellular movement of the viral progeny. The membrane association and RNA binding of the Tobacco mosaic virus (TMV) movement protein (MP) are vital for orchestrating the macromolecular network required for virus movement. A previously proposed topological model suggests that TMV MP is an integral membrane protein with two putative α-helical transmembrane (TM) segments. Here we tested this model using an experimental system that measured the efficiency with which natural polypeptide segments were inserted into the ER membrane under conditions approximating the in vivo situation, as well as in planta. Our results demonstrated that the two hydrophobic regions (HRs) of TMV MP do not span biological membranes. We further found that mutations to alter the hydrophobicity of the first HR modified membrane association and precluded virus movement. We propose a topological model in which the TMV MP HRs intimately associate with the cellular membranes, allowing maximum exposure of the hydrophilic domains of the MP to the cytoplasmic cellular components. IMPORTANCE To facilitate plant viral infection and spread, viruses encode one or more movement proteins (MPs) that interact with ER membranes. The present work investigated the membrane association of the 30K MP of Tobacco mosaic virus (TMV), and the results challenge the previous topological model, which predicted that the TMV MP behaves as an integral membrane protein. The current data provide greatly needed clarification of the topological model and provide substantial evidence that TMV MP is membrane associated only at the cytoplasmic face of the membrane and that neither of its domains is integrated into the membrane or translocated into the lumen. Understanding the topology of MPs in the ER is vital for understanding the role of the ER in plant virus transport

  5. Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

    Science.gov (United States)

    Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

    2015-03-01

    Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing

  6. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

    Directory of Open Access Journals (Sweden)

    Xingli Guo

    Full Text Available The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.

  7. Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Furuya Toshio

    2011-02-01

    Full Text Available Abstract Background The amount of data on protein-protein interactions (PPIs available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine. Results To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS. Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM. Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV proteins identified to date. Conclusions The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.

  8. Alpha-crystallin-type heat shock proteins: socializing minichaperones in the context of a multichaperone network.

    Science.gov (United States)

    Narberhaus, Franz

    2002-03-01

    Alpha-crystallins were originally recognized as proteins contributing to the transparency of the mammalian eye lens. Subsequently, they have been found in many, but not all, members of the Archaea, Bacteria, and Eucarya. Most members of the diverse alpha-crystallin family have four common structural and functional features: (i) a small monomeric molecular mass between 12 and 43 kDa; (ii) the formation of large oligomeric complexes; (iii) the presence of a moderately conserved central region, the so-called alpha-crystallin domain; and (iv) molecular chaperone activity. Since alpha-crystallins are induced by a temperature upshift in many organisms, they are often referred to as small heat shock proteins (sHsps) or, more accurately, alpha-Hsps. Alpha-crystallins are integrated into a highly flexible and synergistic multichaperone network evolved to secure protein quality control in the cell. Their chaperone activity is limited to the binding of unfolding intermediates in order to protect them from irreversible aggregation. Productive release and refolding of captured proteins into the native state requires close cooperation with other cellular chaperones. In addition, alpha-Hsps seem to play an important role in membrane stabilization. The review compiles information on the abundance, sequence conservation, regulation, structure, and function of alpha-Hsps with an emphasis on the microbial members of this chaperone family.

  9. Discovering functional interdependence relationship in PPI networks for protein complex identification.

    Science.gov (United States)

    Lam, Winnie W M; Chan, Keith C C

    2012-04-01

    Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation

  10. Classification of protein-protein interaction full-text documents using text and citation network features.

    Science.gov (United States)

    Kolchinsky, Artemy; Abi-Haidar, Alaa; Kaur, Jasleen; Hamed, Ahmed Abdeen; Rocha, Luis M

    2010-01-01

    We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: 1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification of abstracts and 2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top-performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics.

  11. Radioresistance related genes screened by protein-protein interaction network analysis in nasopharyngeal carcinoma

    International Nuclear Information System (INIS)

    Zhu Xiaodong; Guo Ya; Qu Song; Li Ling; Huang Shiting; Li Danrong; Zhang Wei

    2012-01-01

    Objective: To discover radioresistance associated molecular biomarkers and its mechanism in nasopharyngeal carcinoma by protein-protein interaction network analysis. Methods: Whole genome expression microarray was applied to screen out differentially expressed genes in two cell lines CNE-2R and CNE-2 with different radiosensitivity. Four differentially expressed genes were randomly selected for further verification by the semi-quantitative RT-PCR analysis with self-designed primers. The common differentially expressed genes from two experiments were analyzed with the SNOW online database in order to find out the central node related to the biomarkers of nasopharyngeal carcinoma radioresistance. The expression of STAT1 in CNE-2R and CNE-2 cells was measured by Western blot. Results: Compared with CNE-2 cells, 374 genes in CNE-2R cells were differentially expressed while 197 genes showed significant differences. Four randomly selected differentially expressed genes were verified by RT-PCR and had same change trend in consistent with the results of chip assay. Analysis with the SNOW database demonstrated that those 197 genes could form a complicated interaction network where STAT1 and JUN might be two key nodes. Indeed, the STAT1-α expression in CNE-2R was higher than that in CNE-2 (t=4.96, P<0.05). Conclusions: The key nodes of STAT1 and JUN may be the molecular biomarkers leading to radioresistance in nasopharyngeal carcinoma, and STAT1-α might have close relationship with radioresistance. (authors)

  12. Self-organizing path integration using a linked continuous attractor and competitive network: path integration of head direction.

    Science.gov (United States)

    Stringer, Simon M; Rolls, Edmund T

    2006-12-01

    A key issue is how networks in the brain learn to perform path integration, that is update a represented position using a velocity signal. Using head direction cells as an example, we show that a competitive network could self-organize to learn to respond to combinations of head direction and angular head rotation velocity. These combination cells can then be used to drive a continuous attractor network to the next head direction based on the incoming rotation signal. An associative synaptic modification rule with a short term memory trace enables preceding combination cell activity during training to be associated with the next position in the continuous attractor network. The network accounts for the presence of neurons found in the brain that respond to combinations of head direction and angular head rotation velocity. Analogous networks in the hippocampal system could self-organize to perform path integration of place and spatial view representations.

  13. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database.

    Science.gov (United States)

    Barneh, Farnaz; Jafari, Mohieddin; Mirzaie, Mehdi

    2016-11-01

    Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks.

    Science.gov (United States)

    Zhang, P; Tao, L; Zeng, X; Qin, C; Chen, S Y; Zhu, F; Yang, S Y; Li, Z R; Chen, W P; Chen, Y Z

    2017-02-03

    The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. On the area spectral efficiency improvement of heterogeneous network by exploiting the integration of macro-femto cellular networks

    KAUST Repository

    Shakir, Muhammad; Alouini, Mohamed-Slim

    2012-01-01

    . In this paper, we consider a Heterogeneous network where we complement the macrocell network with low-power low-cost user deployed nodes, such as femtocell base stations to increase the mean achievable capacity of the system. In this context, we integrate macro

  16. CPLA 1.0: an integrated database of protein lysine acetylation.

    Science.gov (United States)

    Liu, Zexian; Cao, Jun; Gao, Xinjiao; Zhou, Yanhong; Wen, Longping; Yang, Xiangjiao; Yao, Xuebiao; Ren, Jian; Xue, Yu

    2011-01-01

    As a reversible post-translational modification (PTM) discovered decades ago, protein lysine acetylation was known for its regulation of transcription through the modification of histones. Recent studies discovered that lysine acetylation targets broad substrates and especially plays an essential role in cellular metabolic regulation. Although acetylation is comparable with other major PTMs such as phosphorylation, an integrated resource still remains to be developed. In this work, we presented the compendium of protein lysine acetylation (CPLA) database for lysine acetylated substrates with their sites. From the scientific literature, we manually collected 7151 experimentally identified acetylation sites in 3311 targets. We statistically studied the regulatory roles of lysine acetylation by analyzing the Gene Ontology (GO) and InterPro annotations. Combined with protein-protein interaction information, we systematically discovered a potential human lysine acetylation network (HLAN) among histone acetyltransferases (HATs), substrates and histone deacetylases (HDACs). In particular, there are 1862 triplet relationships of HAT-substrate-HDAC retrieved from the HLAN, at least 13 of which were previously experimentally verified. The online services of CPLA database was implemented in PHP + MySQL + JavaScript, while the local packages were developed in JAVA 1.5 (J2SE 5.0). The CPLA database is freely available for all users at: http://cpla.biocuckoo.org.

  17. cisPath: an R/Bioconductor package for cloud users for visualization and management of functional protein interaction networks.

    Science.gov (United States)

    Wang, Likun; Yang, Luhe; Peng, Zuohan; Lu, Dan; Jin, Yan; McNutt, Michael; Yin, Yuxin

    2015-01-01

    With the burgeoning development of cloud technology and services, there are an increasing number of users who prefer cloud to run their applications. All software and associated data are hosted on the cloud, allowing users to access them via a web browser from any computer, anywhere. This paper presents cisPath, an R/Bioconductor package deployed on cloud servers for client users to visualize, manage, and share functional protein interaction networks. With this R package, users can easily integrate downloaded protein-protein interaction information from different online databases with private data to construct new and personalized interaction networks. Additional functions allow users to generate specific networks based on private databases. Since the results produced with the use of this package are in the form of web pages, cloud users can easily view and edit the network graphs via the browser, using a mouse or touch screen, without the need to download them to a local computer. This package can also be installed and run on a local desktop computer. Depending on user preference, results can be publicized or shared by uploading to a web server or cloud driver, allowing other users to directly access results via a web browser. This package can be installed and run on a variety of platforms. Since all network views are shown in web pages, such package is particularly useful for cloud users. The easy installation and operation is an attractive quality for R beginners and users with no previous experience with cloud services.

  18. Integrated network analysis reveals potentially novel molecular mechanisms and therapeutic targets of refractory epilepsies.

    Directory of Open Access Journals (Sweden)

    Hongwei Chu

    Full Text Available Epilepsy is a complex neurological disorder and a significant health problem. The pathogenesis of epilepsy remains obscure in a significant number of patients and the current treatment options are not adequate in about a third of individuals which were known as refractory epilepsies (RE. Network medicine provides an effective approach for studying the molecular mechanisms underlying complex diseases. Here we integrated 1876 disease-gene associations of RE and located those genes to human protein-protein interaction (PPI network to obtain 42 significant RE-associated disease modules. The functional analysis of these disease modules showed novel molecular pathological mechanisms of RE, such as the novel enriched pathways (e.g., "presynaptic nicotinic acetylcholine receptors", "signaling by insulin receptor". Further analysis on the relationships between current drug targets and the RE-related disease genes showed the rational mechanisms of most antiepileptic drugs. In addition, we detected ten potential novel drug targets (e.g., KCNA1, KCNA4-6, KCNC3, KCND2, KCNMA1, CAMK2G, CACNB4 and GRM1 located in three RE related disease modules, which might provide novel insights into the new drug discovery for RE therapy.

  19. State-related functional integration and functional segregation brain networks in schizophrenia.

    Science.gov (United States)

    Yu, Qingbao; Sui, Jing; Kiehl, Kent A; Pearlson, Godfrey; Calhoun, Vince D

    2013-11-01

    Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder. © 2013.

  20. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  1. A Critical Agency Network Model for Building an Integrated Outreach Program

    Science.gov (United States)

    Kiyama, Judy Marquez; Lee, Jenny J.; Rhoades, Gary

    2012-01-01

    This study considers a distinct case of a college outreach program that integrates student affairs staff, academic administrators, and faculty across campus. The authors find that social networks and critical agency help to understand the integration of these various professionals and offer a critical agency network model of enacting change.…

  2. Academic self-efficacy, growth mindsets, and university students' integration in academic and social support networks

    NARCIS (Netherlands)

    Zander, Lysann; Brouwer, Jasperina; Jansen, Ellen; Crayen, Claudia; Hannover, Bettina

    Combining complete social networks and structural equation modeling, we investigate how two learning-related cognitions, academic self-efficacy and growth mindsets, relate to integration in support networks of 580 university students in 30 seminar groups. We assessed integration as popularity in

  3. Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

    Science.gov (United States)

    Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf

    2018-04-01

    Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

  4. A Network of Multi-Tasking Proteins at the DNA Replication Fork Preserves Genome Stability.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available To elucidate the network that maintains high fidelity genome replication, we have introduced two conditional mutant alleles of DNA2, an essential DNA replication gene, into each of the approximately 4,700 viable yeast deletion mutants and determined the fitness of the double mutants. Fifty-six DNA2-interacting genes were identified. Clustering analysis of genomic synthetic lethality profiles of each of 43 of the DNA2-interacting genes defines a network (consisting of 322 genes and 876 interactions whose topology provides clues as to how replication proteins coordinate regulation and repair to protect genome integrity. The results also shed new light on the functions of the query gene DNA2, which, despite many years of study, remain controversial, especially its proposed role in Okazaki fragment processing and the nature of its in vivo substrates. Because of the multifunctional nature of virtually all proteins at the replication fork, the meaning of any single genetic interaction is inherently ambiguous. The multiplexing nature of the current studies, however, combined with follow-up supporting experiments, reveals most if not all of the unique pathways requiring Dna2p. These include not only Okazaki fragment processing and DNA repair but also chromatin dynamics.

  5. Too Many Friends: Social Integration, Network Cohesion and Adolescent Depressive Symptoms

    Science.gov (United States)

    Falci, Christina; McNeely, Clea

    2009-01-01

    Using a nationally representative sample of adolescents, we examine associations among social integration (network size), network cohesion (alter-density), perceptions of social relationships (e.g., social support) and adolescent depressive symptoms. We find that adolescents with either too large or too small a network have higher levels of…

  6. Finding influential nodes for integration in brain networks using optimal percolation theory.

    Science.gov (United States)

    Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A

    2018-06-11

    Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

  7. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  8. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  9. MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

    Science.gov (United States)

    Zhang, Chengxin; Zheng, Wei; Freddolino, Peter L; Zhang, Yang

    2018-03-10

    Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein-protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/. Copyright © 2018. Published by Elsevier Ltd.

  10. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology.

    Science.gov (United States)

    Bahramali, Golnaz; Goliaei, Bahram; Minuchehr, Zarrin; Marashi, Sayed-Amir

    2017-02-01

    Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

  11. The Contribution of Network Organization and Integration to the Development of Cognitive Control.

    Science.gov (United States)

    Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N; Luna, Beatriz

    2015-12-01

    Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10-26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.

  12. The Contribution of Network Organization and Integration to the Development of Cognitive Control

    Science.gov (United States)

    Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N.; Luna, Beatriz

    2015-01-01

    Abstract Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10–26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control. PMID:26713863

  13. The Contribution of Network Organization and Integration to the Development of Cognitive Control.

    Directory of Open Access Journals (Sweden)

    Scott Marek

    2015-12-01

    Full Text Available Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI, graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10-26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.

  14. Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

    Science.gov (United States)

    Holland, David O; Johnson, Margaret E

    2018-03-01

    Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module

  15. Design mobile satellite system architecture as an integral part of the cellular access digital network

    Science.gov (United States)

    Chien, E. S. K.; Marinho, J. A.; Russell, J. E., Sr.

    1988-01-01

    The Cellular Access Digital Network (CADN) is the access vehicle through which cellular technology is brought into the mainstream of the evolving integrated telecommunications network. Beyond the integrated end-to-end digital access and per call network services provisioning of the Integrated Services Digital Network (ISDN), the CADN engenders the added capability of mobility freedom via wireless access. One key element of the CADN network architecture is the standard user to network interface that is independent of RF transmission technology. Since the Mobile Satellite System (MSS) is envisioned to not only complement but also enhance the capabilities of the terrestrial cellular telecommunications network, compatibility and interoperability between terrestrial cellular and mobile satellite systems are vitally important to provide an integrated moving telecommunications network of the future. From a network standpoint, there exist very strong commonalities between the terrestrial cellular system and the mobile satellite system. Therefore, the MSS architecture should be designed as an integral part of the CADN. This paper describes the concept of the CADN, the functional architecture of the MSS, and the user-network interface signaling protocols.

  16. Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks.

    Science.gov (United States)

    Shen, Yiwen; Hattink, Maarten H N; Samadi, Payman; Cheng, Qixiang; Hu, Ziyiz; Gazman, Alexander; Bergman, Keren

    2018-04-16

    Silicon photonics based switches offer an effective option for the delivery of dynamic bandwidth for future large-scale Datacom systems while maintaining scalable energy efficiency. The integration of a silicon photonics-based optical switching fabric within electronic Datacom architectures requires novel network topologies and arbitration strategies to effectively manage the active elements in the network. We present a scalable software-defined networking control plane to integrate silicon photonic based switches with conventional Ethernet or InfiniBand networks. Our software-defined control plane manages both electronic packet switches and multiple silicon photonic switches for simultaneous packet and circuit switching. We built an experimental Dragonfly network testbed with 16 electronic packet switches and 2 silicon photonic switches to evaluate our control plane. Observed latencies occupied by each step of the switching procedure demonstrate a total of 344 µs control plane latency for data-center and high performance computing platforms.

  17. Software-Programmed Optical Networking with Integrated NFV Service Provisioning

    DEFF Research Database (Denmark)

    Mehmeri, Victor; Wang, Xi; Basu, Shrutarshi

    2017-01-01

    We showcase demonstrations of “program & compile” styled optical networking as well as open platforms & standards based NFV service provisioning using a proof-of-concept implementation of the Software-Programmed Networking Operating System (SPN OS).......We showcase demonstrations of “program & compile” styled optical networking as well as open platforms & standards based NFV service provisioning using a proof-of-concept implementation of the Software-Programmed Networking Operating System (SPN OS)....

  18. NET European Network on Neutron Techniques Standardization for Structural Integrity

    International Nuclear Information System (INIS)

    Youtsos, A.

    2004-01-01

    Improved performance and safety of European energy production systems is essential for providing safe, clean and inexpensive electricity to the citizens of the enlarged EU. The state of the art in assessing internal stresses, micro-structure and defects in welded nuclear components -as well as their evolution due to complex thermo-mechanical loads and irradiation exposure -needs to be improved before relevant structural integrity assessment code requirements can safely become less conservative. This is valid for both experimental characterization techniques and predictive numerical algorithms. In the course of the last two decades neutron methods have proven to be excellent means for providing valuable information required in structural integrity assessment of advanced engineering applications. However, the European industry is hampered from broadly using neutron research due to lack of harmonised and standardized testing methods. 35 European major industrial and research/academic organizations have joined forces, under JRC coordination, to launch the NET European Network on Neutron Techniques Standardization for Structural Integrity in May 2002. The NET collaborative research initiative aims at further development and harmonisation of neutron scattering methods, in support of structural integrity assessment. This is pursued through a number of testing round robin campaigns on neutron diffraction and small angle neutron scattering - SANS and supported by data provided by other more conventional destructive and non-destructive methods, such as X-ray diffraction and deep and surface hole drilling. NET also strives to develop more reliable and harmonized simulation procedures for the prediction of residual stress and damage in steel welded power plant components. This is pursued through a number of computational round robin campaigns based on advanced FEM techniques, and on reliable data obtained by such novel and harmonized experimental methods. The final goal of

  19. An integrated control for overvoltage mitigation in the distribution network

    NARCIS (Netherlands)

    Viyathukattuva Mohamed Ali, M.M.; Nguyen, P.H.; Kling, W.L.

    2014-01-01

    Increasing share of distributed renewable energy sources (DRES) in the distribution network raises new operational and power quality challenges like overvoltage in the network feeders. Such power quality challenge limits the penetration of DRES in the distribution network. This paper addresses a

  20. Integrative analyses reveal a long noncoding RNA-mediated sponge regulatory network in prostate cancer.

    Science.gov (United States)

    Du, Zhou; Sun, Tong; Hacisuleyman, Ezgi; Fei, Teng; Wang, Xiaodong; Brown, Myles; Rinn, John L; Lee, Mary Gwo-Shu; Chen, Yiwen; Kantoff, Philip W; Liu, X Shirley

    2016-03-15

    Mounting evidence suggests that long noncoding RNAs (lncRNAs) can function as microRNA sponges and compete for microRNA binding to protein-coding transcripts. However, the prevalence, functional significance and targets of lncRNA-mediated sponge regulation of cancer are mostly unknown. Here we identify a lncRNA-mediated sponge regulatory network that affects the expression of many protein-coding prostate cancer driver genes, by integrating analysis of sequence features and gene expression profiles of both lncRNAs and protein-coding genes in tumours. We confirm the tumour-suppressive function of two lncRNAs (TUG1 and CTB-89H12.4) and their regulation of PTEN expression in prostate cancer. Surprisingly, one of the two lncRNAs, TUG1, was previously known for its function in polycomb repressive complex 2 (PRC2)-mediated transcriptional regulation, suggesting its sub-cellular localization-dependent function. Our findings not only suggest an important role of lncRNA-mediated sponge regulation in cancer, but also underscore the critical influence of cytoplasmic localization on the efficacy of a sponge lncRNA.

  1. Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks

    Directory of Open Access Journals (Sweden)

    Boucher Charles AB

    2010-07-01

    Full Text Available Abstract Background The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics. Results Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems. Conclusions HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another.

  2. Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

    Directory of Open Access Journals (Sweden)

    Matthew Ruffalo

    2015-12-01

    Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

  3. Similar pathogen targets in Arabidopsis thaliana and homo sapiens protein networks.

    Directory of Open Access Journals (Sweden)

    Paulo Shakarian

    Full Text Available We study the behavior of pathogens on host protein networks for humans and Arabidopsis - noting striking similarities. Specifically, we preform [Formula: see text]-shell decomposition analysis on these networks - which groups the proteins into various "shells" based on network structure. We observe that shells with a higher average degree are more highly targeted (with a power-law relationship and that highly targeted nodes lie in shells closer to the inner-core of the network. Additionally, we also note that the inner core of the network is significantly under-targeted. We show that these core proteins may have a role in intra-cellular communication and hypothesize that they are less attacked to ensure survival of the host. This may explain why certain high-degree proteins are not significantly attacked.

  4. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety

    Directory of Open Access Journals (Sweden)

    Angelica Reyes-Muñoz

    2016-01-01

    Full Text Available The emergence of Body Sensor Networks (BSNs constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1 an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving that may cause traffic accidents is presented; (2 A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3 as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.

  5. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety.

    Science.gov (United States)

    Reyes-Muñoz, Angelica; Domingo, Mari Carmen; López-Trinidad, Marco Antonio; Delgado, José Luis

    2016-01-15

    The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.

  6. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety

    Science.gov (United States)

    Reyes-Muñoz, Angelica; Domingo, Mari Carmen; López-Trinidad, Marco Antonio; Delgado, José Luis

    2016-01-01

    The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels. PMID:26784204

  7. Exploring the Unfolding Pathway of Maltose Binding Proteins: An Integrated Computational Approach

    KAUST Repository

    Guardiani, Carlo; Marino, Daniele Di; Tramontano, Anna; Chinappi, Mauro; Cecconi, Fabio

    2014-01-01

    © 2014 American Chemical Society. Recent single-molecule force spectroscopy experiments on the Maltose Binding Proteins (MBPs) identified four stable structural units, termed unfoldons, that resist mechanical stress and determine the intermediates of the unfolding pathway. In this work, we analyze the topological origin and the dynamical role of the unfoldons using an integrated approach which combines a graph-theoretical analysis of the interaction network of the MBP native-state with steered molecular dynamics simulations. The topological analysis of the native state, while revealing the structural nature of the unfoldons, provides a framework to interpret the MBP mechanical unfolding pathway. Indeed, the experimental pathway can be effectively predicted by means of molecular dynamics simulations with a simple topology-based and low-resolution model of the MBP. The results obtained from the coarse-grained approach are confirmed and further refined by all-atom molecular dynamics.

  8. Exploring the Unfolding Pathway of Maltose Binding Proteins: An Integrated Computational Approach

    KAUST Repository

    Guardiani, Carlo

    2014-09-09

    © 2014 American Chemical Society. Recent single-molecule force spectroscopy experiments on the Maltose Binding Proteins (MBPs) identified four stable structural units, termed unfoldons, that resist mechanical stress and determine the intermediates of the unfolding pathway. In this work, we analyze the topological origin and the dynamical role of the unfoldons using an integrated approach which combines a graph-theoretical analysis of the interaction network of the MBP native-state with steered molecular dynamics simulations. The topological analysis of the native state, while revealing the structural nature of the unfoldons, provides a framework to interpret the MBP mechanical unfolding pathway. Indeed, the experimental pathway can be effectively predicted by means of molecular dynamics simulations with a simple topology-based and low-resolution model of the MBP. The results obtained from the coarse-grained approach are confirmed and further refined by all-atom molecular dynamics.

  9. The Relationship Between Low-Frequency Motions and Community Structure of Residue Network in Protein Molecules.

    Science.gov (United States)

    Sun, Weitao

    2018-01-01

    The global shape of a protein molecule is believed to be dominant in determining low-frequency deformational motions. However, how structure dynamics relies on residue interactions remains largely unknown. The global residue community structure and the local residue interactions are two important coexisting factors imposing significant effects on low-frequency normal modes. In this work, an algorithm for community structure partition is proposed by integrating Miyazawa-Jernigan empirical potential energy as edge weight. A sensitivity parameter is defined to measure the effect of local residue interaction on low-frequency movement. We show that community structure is a more fundamental feature of residue contact networks. Moreover, we surprisingly find that low-frequency normal mode eigenvectors are sensitive to some local critical residue interaction pairs (CRIPs). A fair amount of CRIPs act as bridges and hold distributed structure components into a unified tertiary structure by bonding nearby communities. Community structure analysis and CRIP detection of 116 catalytic proteins reveal that breaking up of a CRIP can cause low-frequency allosteric movement of a residue at the far side of protein structure. The results imply that community structure and CRIP may be the structural basis for low-frequency motions.

  10. Ames and other European networks in integrity of ageing structures

    International Nuclear Information System (INIS)

    Davies, L.M.; Von Estorff, U.; Crutzen, S.

    1996-01-01

    Several European institutions and organisations and the Joint Research Centre have developed co-operative programmes now organised into Networks for mutual benefit. They include utilities, engineering companies, Research and Development laboratories and regulatory bodies. Networks are organised and managed like the successful Programme for the Inspection of Steel Components (PISC). The JRC's Institute for Advanced Materials of the European Commission plays the role of Operating Agent and manager of these Networks: ENIQ. AMES, NESC, each of them dealing with specific aspect of fitness for purpose of materials in structural components. This paper describes the structure and the objectives of these networks. Particular emphasis is given to the network AMES

  11. Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

    Directory of Open Access Journals (Sweden)

    Zhao Yi

    2010-12-01

    Full Text Available Abstract Background Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC for clustering vertices of a protein interaction network using a novel subgraph density measurement. Results By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. Conclusions Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

  12. The Development of PIPA: An Integrated and Automated Pipeline for Genome-Wide Protein Function Annotation

    National Research Council Canada - National Science Library

    Yu, Chenggang; Zavaljevski, Nela; Desai, Valmik; Johnson, Seth; Stevens, Fred J; Reifman, Jaques

    2008-01-01

    .... With the existence of many programs and databases for inferring different protein functions, a pipeline that properly integrates these resources will benefit from the advantages of each method...

  13. OzFlux data: network integration from collection to curation

    Directory of Open Access Journals (Sweden)

    P. Isaac

    2017-06-01

    Full Text Available Measurement of the exchange of energy and mass between the surface and the atmospheric boundary-layer by the eddy covariance technique has undergone great change in the last 2 decades. Early studies of these exchanges were confined to brief field campaigns in carefully controlled conditions followed by months of data analysis. Current practice is to run tower-based eddy covariance systems continuously over several years due to the need for continuous monitoring as part of a global effort to develop local-, regional-, continental- and global-scale budgets of carbon, water and energy. Efficient methods of processing the increased quantities of data are needed to maximise the time available for analysis and interpretation. Standardised methods are needed to remove differences in data processing as possible contributors to observed spatial variability. Furthermore, public availability of these data sets assists with undertaking global research efforts. The OzFlux data path has been developed (i to provide a standard set of quality control and post-processing tools across the network, thereby facilitating inter-site integration and spatial comparisons; (ii to increase the time available to researchers for analysis and interpretation by reducing the time spent collecting and processing data; (iii to propagate both data and metadata to the final product; and (iv to facilitate the use of the OzFlux data by adopting a standard file format and making the data available from web-based portals. Discovery of the OzFlux data set is facilitated through incorporation in FLUXNET data syntheses and the publication of collection metadata via the RIF-CS format. This paper serves two purposes. The first is to describe the data sets, along with their quality control and post-processing, for the other papers of this Special Issue. The second is to provide an example of one solution to the data collection and curation challenges that are encountered by similar flux

  14. Topology design and performance analysis of an integrated communication network

    Science.gov (United States)

    Li, V. O. K.; Lam, Y. F.; Hou, T. C.; Yuen, J. H.

    1985-01-01

    A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.

  15. Synthesis and Design of Integrated Process and Water Networks

    DEFF Research Database (Denmark)

    Handani, Zainatul B.; Quaglia, Alberto; Gani, Rafiqul

    2015-01-01

    This work presents the development of a systematic framework for a simultaneous synthesis and design of process and water networks using the superstructure-based optimization approach. In this framework, a new superstructure combining both networks is developed by attempting to consider all...... possible options with respect to the topology of the process and water networks, leading to Mixed Integer Non Linear Programming (MINLP) problem. A solution strategy to solve the multi-network problem accounts explicitly the interactions between the networks by selecting suitable technologies in order...... to transform raw materials into products and produce clean water to be reused in the process at the early stage of design. Since the connection between the process network and the wastewater treatment network is not a straight forward connection, a new converter interval is introduced in order to convert...

  16. Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks

    Directory of Open Access Journals (Sweden)

    Mazo Ilya

    2007-07-01

    Full Text Available Abstract Background Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets. Results We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology

  17. Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

    Science.gov (United States)

    White, Forest M.; Wolf-Yadlin, Alejandro

    2016-06-01

    Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

  18. The organisational structure of protein networks: revisiting the centrality-lethality hypothesis.

    Science.gov (United States)

    Raman, Karthik; Damaraju, Nandita; Joshi, Govind Krishna

    2014-03-01

    Protein networks, describing physical interactions as well as functional associations between proteins, have been unravelled for many organisms in the recent past. Databases such as the STRING provide excellent resources for the analysis of such networks. In this contribution, we revisit the organisation of protein networks, particularly the centrality-lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of 20 organisms, with essentiality information available in the Database of Essential Genes and assess the relationship between centrality measures and lethality. For each of these organisms, we obtained networks of high-confidence interactions from the STRING database, and computed network parameters such as degree, betweenness centrality, closeness centrality and pairwise disconnectivity indices. We observe that the networks considered here are predominantly disassortative. Further, we observe that essential nodes in a network have a significantly higher average degree and betweenness centrality, compared to the network average. Most previous studies have evaluated the centrality-lethality hypothesis for Saccharomyces cerevisiae and Escherichia coli; we here observe that the centrality-lethality hypothesis hold goods for a large number of organisms, with certain limitations. Betweenness centrality may also be a useful measure to identify essential nodes, but measures like closeness centrality and pairwise disconnectivity are not significantly higher for essential nodes.

  19. Integration of 100% Micro-Distributed Energy Resources in the Low Voltage Distribution Network

    DEFF Research Database (Denmark)

    You, Shi; Segerberg, Helena

    2014-01-01

    of heat pumps (HPs) and plug-in electric vehicles (PEVs) at 100% penetration level on a representative urban residential low voltage (LV) distribution network of Denmark are investigated by performing a steady-state load flow analysis through an integrated simulation setup. Three DERs integration...... oriented integration strategies, having 100% integration of DER in the provided LV network is feasible.......The existing electricity infrastructure may to a great extent limit a high penetration of the micro-sized Distributed Energy Resources (DERs), due to the physical bottlenecks, e.g. thermal capacitates of cables, transformers and the voltage limitations. In this study, the integration impacts...

  20. The integration of weighted gene association networks based on information entropy.

    Science.gov (United States)

    Yang, Fan; Wu, Duzhi; Lin, Limei; Yang, Jian; Yang, Tinghong; Zhao, Jing

    2017-01-01

    Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.

  1. End-to-End Traffic Flow Modeling of the Integrated SCaN Network

    Science.gov (United States)

    Cheung, K.-M.; Abraham, D. S.

    2012-05-01

    In this article, we describe the analysis and simulation effort of the end-to-end traffic flow for the Integrated Space Communications and Navigation (SCaN) Network. Using the network traffic derived for the 30-day period of July 2018 from the Space Communications Mission Model (SCMM), we generate the wide-area network (WAN) bandwidths of the ground links for different architecture options of the Integrated SCaN Network. We also develop a new analytical scheme to model the traffic flow and buffering mechanism of a store-and-forward network. It is found that the WAN bandwidth of the Integrated SCaN Network is an important differentiator of different architecture options, as the recurring circuit costs of certain architecture options can be prohibitively high.

  2. Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat.

    Science.gov (United States)

    Babbitt, Patricia C; Bagos, Pantelis G; Bairoch, Amos; Bateman, Alex; Chatonnet, Arnaud; Chen, Mark Jinan; Craik, David J; Finn, Robert D; Gloriam, David; Haft, Daniel H; Henrissat, Bernard; Holliday, Gemma L; Isberg, Vignir; Kaas, Quentin; Landsman, David; Lenfant, Nicolas; Manning, Gerard; Nagano, Nozomi; Srinivasan, Narayanaswamy; O'Donovan, Claire; Pruitt, Kim D; Sowdhamini, Ramanathan; Rawlings, Neil D; Saier, Milton H; Sharman, Joanna L; Spedding, Michael; Tsirigos, Konstantinos D; Vastermark, Ake; Vriend, Gerrit

    2015-01-01

    During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

  3. Outer membrane protein functions as integrator of protein import and DNA inheritance in mitochondria

    Science.gov (United States)

    Käser, Sandro; Oeljeklaus, Silke; Týč, Jiří; Vaughan, Sue; Warscheid, Bettina; Schneider, André

    2016-01-01

    Trypanosomatids are one of the earliest diverging eukaryotes that have fully functional mitochondria. pATOM36 is a trypanosomatid-specific essential mitochondrial outer membrane protein that has been implicated in protein import. Changes in the mitochondrial proteome induced by ablation of pATOM36 and in vitro assays show that pATOM36 is required for the assembly of the archaic translocase of the outer membrane (ATOM), the functional analog of the TOM complex in other organisms. Reciprocal pull-down experiments and immunofluorescence analyses demonstrate that a fraction of pATOM36 interacts and colocalizes with TAC65, a previously uncharacterized essential component of the tripartite attachment complex (TAC). The TAC links the single-unit mitochondrial genome to the basal body of the flagellum and mediates the segregation of the replicated mitochondrial genomes. RNAi experiments show that pATOM36, in line with its dual localization, is not only essential for ATOM complex assembly but also for segregation of the replicated mitochondrial genomes. However, the two functions are distinct, as a truncated version of pATOM36 lacking the 75 C-terminal amino acids can rescue kinetoplast DNA missegregation but not the lack of ATOM complex assembly. Thus, pATOM36 has a dual function and integrates mitochondrial protein import with mitochondrial DNA inheritance. PMID:27436903

  4. Microbial network for waste activated sludge cascade utilization in an integrated system of microbial electrolysis and anaerobic fermentation

    DEFF Research Database (Denmark)

    Liu, Wenzong; He, Zhangwei; Yang, Chunxue

    2016-01-01

    in an integrated system of microbial electrolysis cell (MEC) and anaerobic digestion (AD) for waste activated sludge (WAS). Microbial communities in integrated system would build a thorough energetic and metabolic interaction network regarding fermentation communities and electrode respiring communities...... to Firmicutes (Acetoanaerobium, Acetobacterium, and Fusibacter) showed synergistic relationship with exoelectrogensin the degradation of complex organic matter or recycling of MEC products (H2). High protein and polysaccharide but low fatty acid content led to the dominance of Proteiniclasticum...... biofilm. The overall performance of WAS cascade utilization was substantially related to the microbial community structures, which in turn depended on the initial pretreatment to enhance WAS fermentation. It is worth noting that species in AD and MEC communities are able to build complex networks...

  5. Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.

    Directory of Open Access Journals (Sweden)

    Eric J Foss

    2011-09-01

    Full Text Available Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.

  6. The energy innovation network : fuelling an integrated energy future

    Energy Technology Data Exchange (ETDEWEB)

    Isaacs, E. [Alberta Energy Research Inst., Edmonton, AB (Canada)

    2005-07-01

    Global primary energy demand is expected to increase by 1.7 per cent annually from 2000 to 2030, reaching an annual level of 15.3 billion tonnes of oil equivalent. Fossil fuels are expected to supply over 90 per cent of global incremental energy demand through 2030, while gas consumption is estimated to double between 2000 and 2030 due to its cost competitiveness, high availability and environmental advantages. Oil will remain the largest fuel source with demand increasing by 1.6 per cent annually. In order to tap the vast Canadian resource potential, innovative new technologies are needed to unlock the remaining conventional oil and gas reserves. It was argued that no single source of energy will be sufficient to meet world or Canadian demand. Therefore, there is also a need for a collaborative initiative to facilitate a long-term effort to implement an integrated energy innovation strategy. The Energy Innovation Network (EnergyINet) was created help industry, governments, and the research community address the challenges of ensuring an abundant supply of environmentally responsible energy. Given the right technologies, bitumen, coal, and coalbed methane have hundreds of years of production remaining. Production of those reserves depends on finding effective solutions to production costs, cost and availability of feedstocks needed to produce higher valued products, market limitations, and land, water, air, and greenhouse gas issues. The main challenge is to finance the development of such technologies into reliable, large-scale commercial applications. It was concluded that Canada's ability to maintain competitive energy supplies from conventional and non-conventional energy systems will be severely limited as the need to protect the environment, reduce greenhouse gas emissions, and conserve water moves higher on the public agenda. 13 refs.