Modelling the structure of a ceRNA-theoretical, bipartite microRNA-mRNA interaction network regulating intestinal epithelial cellular pathways using R programming.

PubMed

Robinson, J M; Henderson, W A

2018-01-12

We report a method using functional-molecular databases and network modelling to identify hypothetical mRNA-miRNA interaction networks regulating intestinal epithelial barrier function. The model forms a data-analysis component of our cell culture experiments, which produce RNA expression data from Nanostring Technologies nCounter ® system. The epithelial tight-junction (TJ) and actin cytoskeleton interact as molecular components of the intestinal epithelial barrier. Upstream regulation of TJ-cytoskeleton interaction is effected by the Rac/Rock/Rho signaling pathway and other associated pathways which may be activated or suppressed by extracellular signaling from growth factors, hormones, and immune receptors. Pathway activations affect epithelial homeostasis, contributing to degradation of the epithelial barrier associated with osmotic dysregulation, inflammation, and tumor development. The complexity underlying miRNA-mRNA interaction networks represents a roadblock for prediction and validation of competing-endogenous RNA network function. We developed a network model to identify hypothetical co-regulatory motifs in a miRNA-mRNA interaction network related to epithelial function. A mRNA-miRNA interaction list was generated using KEGG and miRWalk2.0 databases. R-code was developed to quantify and visualize inherent network structures. We identified a sub-network with a high number of shared, targeting miRNAs, of genes associated with cellular proliferation and cancer, including c-MYC and Cyclin D.

Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

PubMed Central

2013-01-01

Background In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. Results We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Conclusions Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes. PMID:23663484

Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies

PubMed Central

Blonder, Benjamin; Dornhaus, Anna

2011-01-01

Background An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. Methodology/Findings Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. Conclusions/Significance Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales. PMID:21625450

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

PubMed

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.

Modular biological function is most effectively captured by combining molecular interaction data types.

PubMed

Ames, Ryan M; Macpherson, Jamie I; Pinney, John W; Lovell, Simon C; Robertson, David L

2013-01-01

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

L-GRAAL: Lagrangian graphlet-based network aligner.

PubMed

Malod-Dognin, Noël; Pržulj, Nataša

2015-07-01

Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

PubMed Central

Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

2006-01-01

Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

The BioPlex Network: A Systematic Exploration of the Human Interactome.

PubMed

Huttlin, Edward L; Ting, Lily; Bruckner, Raphael J; Gebreab, Fana; Gygi, Melanie P; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E; De Camilli, Pietro; Paulo, Joao A; Harper, J Wade; Gygi, Steven P

2015-07-16

Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors. Copyright © 2015 Elsevier Inc. All rights reserved.

The BioPlex Network: A Systematic Exploration of the Human Interactome

PubMed Central

Huttlin, Edward L.; Ting, Lily; Bruckner, Raphael J.; Gebreab, Fana; Gygi, Melanie P.; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K.; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A.; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E.; DeCamilli, Pietro; Paulo, Joao A.; Harper, J. Wade; Gygi, Steven P.

2015-01-01

SUMMARY Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally-related proteins. Finally, BioPlex, in combination with other approaches can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial Amyotrophic Lateral Sclerosis perturb a defined community of interactors. PMID:26186194

Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs

PubMed Central

Sharifpoor, Sara; van Dyk, Dewald; Costanzo, Michael; Baryshnikova, Anastasia; Friesen, Helena; Douglas, Alison C.; Youn, Ji-Young; VanderSluis, Benjamin; Myers, Chad L.; Papp, Balázs; Boone, Charles; Andrews, Brenda J.

2012-01-01

A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions. Condition-specific screens confirmed that the spectrum of kinase dosage interactions can be expanded substantially in activating conditions. An integrated network composed of systematic SDL, negative and positive loss-of-function GIs, and literature-curated kinase–substrate interactions revealed kinase-dependent regulatory motifs predictive of novel gene-specific phenotypes. Our study provides a valuable resource to unravel novel functional relationships and pathways regulated by kinases and outlines a general strategy for deciphering mutant phenotypes from large-scale GI networks. PMID:22282571

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

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

PubMed

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.

Linking Plant Specialization to Dependence in Interactions for Seed Set in Pollination Networks

PubMed Central

Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

2013-01-01

Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent. PMID:24205187

Linking plant specialization to dependence in interactions for seed set in pollination networks.

PubMed

Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

2013-01-01

Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

PubMed Central

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

DiffNet: automatic differential functional summarization of dE-MAP networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2014-10-01

The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility. Copyright © 2014 Elsevier Inc. All rights reserved.

Revealing hidden insect-fungus interactions; moderately specialized, modular and anti-nested detritivore networks.

PubMed

Jacobsen, Rannveig M; Sverdrup-Thygeson, Anne; Kauserud, Håvard; Birkemoe, Tone

2018-04-11

Ecological networks are composed of interacting communities that influence ecosystem structure and function. Fungi are the driving force for ecosystem processes such as decomposition and carbon sequestration in terrestrial habitats, and are strongly influenced by interactions with invertebrates. Yet, interactions in detritivore communities have rarely been considered from a network perspective. In the present study, we analyse the interaction networks between three functional guilds of fungi and insects sampled from dead wood. Using DNA metabarcoding to identify fungi, we reveal a diversity of interactions differing in specificity in the detritivore networks, involving three guilds of fungi. Plant pathogenic fungi were relatively unspecialized in their interactions with insects inhabiting dead wood, while interactions between the insects and wood-decay fungi exhibited the highest degree of specialization, which was similar to estimates for animal-mediated seed dispersal networks in previous studies. The low degree of specialization for insect symbiont fungi was unexpected. In general, the pooled insect-fungus networks were significantly more specialized, more modular and less nested than randomized networks. Thus, the detritivore networks had an unusual anti-nested structure. Future studies might corroborate whether this is a common aspect of networks based on interactions with fungi, possibly owing to their often intense competition for substrate. © 2018 The Author(s).

Network Physiology: How Organ Systems Dynamically Interact

PubMed Central

Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

2015-01-01

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

Attentional networks in developmental dyscalculia

PubMed Central

2010-01-01

Background Very little is known about attention deficits in developmental dyscalculia, hence, this study was designed to provide the missing information. We examined attention abilities of participants suffering from developmental dyscalculia using the attention networks test - interactions. This test was designed to examine three different attention networks--executive function, orienting and alerting--and the interactions between them. Methods Fourteen university students that were diagnosed as suffering from developmental dyscalculia--intelligence and reading abilities in the normal range and no indication of attention-deficit hyperactivity disorder--and 14 matched controls were tested using the attention networks test - interactions. All participants were given preliminary tests to measure mathematical abilities, reading, attention and intelligence. Results The results revealed deficits in the alerting network--a larger alerting effect--and in the executive function networks--a larger congruity effect in developmental dyscalculia participants. The interaction between the alerting and executive function networks was also modulated by group. In addition, developmental dyscalculia participants were slower to respond in the non-cued conditions. Conclusions These results imply specific attentional deficits in pure developmental dyscalculia. Namely, those with developmental dyscalculia seem to be deficient in the executive function and alertness networks. They suffer from difficulty in recruiting attention, in addition to the deficits in numerical processing. PMID:20157427

Attentional networks in developmental dyscalculia.

PubMed

Askenazi, Sarit; Henik, Avishai

2010-01-07

Very little is known about attention deficits in developmental dyscalculia, hence, this study was designed to provide the missing information. We examined attention abilities of participants suffering from developmental dyscalculia using the attention networks test - interactions. This test was designed to examine three different attention networks--executive function, orienting and alerting--and the interactions between them. Fourteen university students that were diagnosed as suffering from developmental dyscalculia--intelligence and reading abilities in the normal range and no indication of attention-deficit hyperactivity disorder--and 14 matched controls were tested using the attention networks test-interactions. All participants were given preliminary tests to measure mathematical abilities, reading, attention and intelligence. The results revealed deficits in the alerting network--a larger alerting effect--and in the executive function networks--a larger congruity effect in developmental dyscalculia participants. The interaction between the alerting and executive function networks was also modulated by group. In addition, developmental dyscalculia participants were slower to respond in the non-cued conditions. These results imply specific attentional deficits in pure developmental dyscalculia. Namely, those with developmental dyscalculia seem to be deficient in the executive function and alertness networks. They suffer from difficulty in recruiting attention, in addition to the deficits in numerical processing.

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

PubMed Central

Nayak, Renuka R.; Kearns, Michael; Spielman, Richard S.; Cheung, Vivian G.

2009-01-01

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes. PMID:19797678

Topology of molecular interaction networks.

PubMed

Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

2013-09-16

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Topology of molecular interaction networks

PubMed Central

2013-01-01

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

PubMed Central

2010-01-01

Background In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships. Results The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches. Conclusions The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. PMID:21172053

Gene function prediction with gene interaction networks: a context graph kernel approach.

PubMed

Li, Xin; Chen, Hsinchun; Li, Jiexun; Zhang, Zhu

2010-01-01

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

Weak Higher-Order Interactions in Macroscopic Functional Networks of the Resting Brain.

PubMed

Huang, Xuhui; Xu, Kaibin; Chu, Congying; Jiang, Tianzi; Yu, Shan

2017-10-25

Interactions among different brain regions are usually examined through functional connectivity (FC) analysis, which is exclusively based on measuring pairwise correlations in activities. However, interactions beyond the pairwise level, that is, higher-order interactions (HOIs), are vital in understanding the behavior of many complex systems. So far, whether HOIs exist among brain regions and how they can affect the brain's activities remains largely elusive. To address these issues, here, we analyzed blood oxygenation level-dependent (BOLD) signals recorded from six typical macroscopic functional networks of the brain in 100 human subjects (46 males and 54 females) during the resting state. Through examining the binarized BOLD signals, we found that HOIs within and across individual networks were both very weak regardless of the network size, topology, degree of spatial proximity, spatial scales, and whether the global signal was regressed. To investigate the potential mechanisms underlying the weak HOIs, we analyzed the dynamics of a network model and also found that HOIs were generally weak within a wide range of key parameters provided that the overall dynamic feature of the model was similar to the empirical data and it was operating close to a linear fluctuation regime. Our results suggest that weak HOI may be a general property of brain's macroscopic functional networks, which implies the dominance of pairwise interactions in shaping brain activities at such a scale and warrants the validity of widely used pairwise-based FC approaches. SIGNIFICANCE STATEMENT To explain how activities of different brain areas are coordinated through interactions is essential to revealing the mechanisms underlying various brain functions. Traditionally, such an interaction structure is commonly studied using pairwise-based functional network analyses. It is unclear whether the interactions beyond the pairwise level (higher-order interactions or HOIs) play any role in this process. Here, we show that HOIs are generally weak in macroscopic brain networks. We also suggest a possible dynamical mechanism that may underlie this phenomenon. These results provide plausible explanation for the effectiveness of widely used pairwise-based approaches in analyzing brain networks. More importantly, it reveals a previously unknown, simple organization of the brain's macroscopic functional systems. Copyright © 2017 the authors 0270-6474/17/3710481-17$15.00/0.

Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

PubMed

Simonyan, Kristina; Fuertinger, Stefan

2015-04-01

Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.

Maximally informative pairwise interactions in networks

PubMed Central

Fitzgerald, Jeffrey D.; Sharpee, Tatyana O.

2010-01-01

Several types of biological networks have recently been shown to be accurately described by a maximum entropy model with pairwise interactions, also known as the Ising model. Here we present an approach for finding the optimal mappings between input signals and network states that allow the network to convey the maximal information about input signals drawn from a given distribution. This mapping also produces a set of linear equations for calculating the optimal Ising-model coupling constants, as well as geometric properties that indicate the applicability of the pairwise Ising model. We show that the optimal pairwise interactions are on average zero for Gaussian and uniformly distributed inputs, whereas they are nonzero for inputs approximating those in natural environments. These nonzero network interactions are predicted to increase in strength as the noise in the response functions of each network node increases. This approach also suggests ways for how interactions with unmeasured parts of the network can be inferred from the parameters of response functions for the measured network nodes. PMID:19905153

FACETS: multi-faceted functional decomposition of protein interaction networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2012-10-15

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Supplementary data are available at the Bioinformatics online. Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

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

PubMed

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.

Changes in the interaction of resting-state neural networks from adolescence to adulthood.

PubMed

Stevens, Michael C; Pearlson, Godfrey D; Calhoun, Vince D

2009-08-01

This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks. (c) 2009 Wiley-Liss, Inc.

Functional Integrative Levels in the Human Interactome Recapitulate Organ Organization

PubMed Central

Prieto, Carlos; Benkahla, Alia; De Las Rivas, Javier; Brun, Christine

2011-01-01

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization. PMID:21799769

Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study.

PubMed

Silva Pereira, Silvana; Hindriks, Rikkert; Mühlberg, Stefanie; Maris, Eric; van Ede, Freek; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo

2017-11-01

A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.

Comprehensive Analysis of Interaction Networks of Telomerase Reverse Transcriptase with Multiple Bioinformatic Approaches: Deep Mining the Potential Functions of Telomere and Telomerase.

PubMed

Hou, Chunyu; Wang, Fei; Liu, Xuewen; Chang, Guangming; Wang, Feng; Geng, Xin

2017-08-01

Telomerase reverse transcriptase (TERT) is the protein component of telomerase complex. Evidence has accumulated showing that the nontelomeric functions of TERT are independent of telomere elongation. However, the mechanisms governing the interaction between TERT and its target genes are not clearly revealed. The biological functions of TERT are not fully elucidated and have thus far been underestimated. To further explore these functions, we investigated TERT interaction networks using multiple bioinformatic databases, including BioGRID, STRING, DAVID, GeneCards, GeneMANIA, PANTHER, miRWalk, mirTarBase, miRNet, miRDB, and TargetScan. In addition, network diagrams were built using Cytoscape software. As competing endogenous RNAs (ceRNAs) are endogenous transcripts that compete for the binding of microRNAs (miRNAs) by using shared miRNA recognition elements, they are involved in creating widespread regulatory networks. Therefore, the ceRNA regulatory networks of TERT were also investigated in this study. Interestingly, we found that the three genes PABPC1, SLC7A11, and TP53 were present in both TERT interaction networks and ceRNAs target genes. It was predicted that TERT might play nontelomeric roles in the generation or development of some rare diseases, such as Rift Valley fever and dyscalculia. Thus, our data will help to decipher the interaction networks of TERT and reveal the unknown functions of telomerase in cancer and aging-related diseases.

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Network dysfunction predicts speech production after left hemisphere stroke.

PubMed

Geranmayeh, Fatemeh; Leech, Robert; Wise, Richard J S

2016-03-09

To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke. We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses. Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production. Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior. © 2016 American Academy of Neurology.

Network dysfunction predicts speech production after left hemisphere stroke

PubMed Central

Leech, Robert; Wise, Richard J.S.

2016-01-01

Objective: To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke. Methods: We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses. Results: Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production. Conclusions: Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior. PMID:26962070

Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes

PubMed Central

Liang, Yuting; Zhao, Huihui; Deng, Ye; Zhou, Jizhong; Li, Guanghe; Sun, Bo

2016-01-01

With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential “keystone” genes, defined as either “hubs” or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions. PMID:26870020

A pairwise maximum entropy model accurately describes resting-state human brain networks

PubMed Central

Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Imai, Yoshio; Machida, Toru; Shirouzu, Ichiro; Konishi, Seiki; Miyashita, Yasushi; Masuda, Naoki

2013-01-01

The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks. PMID:23340410

Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks

PubMed Central

2014-01-01

Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226

Regulatory networks and connected components of the neutral space. A look at functional islands

NASA Astrophysics Data System (ADS)

Boldhaus, G.; Klemm, K.

2010-09-01

The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into ≈ 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

PubMed Central

Stetz, Gabrielle; Verkhivker, Gennady M.

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms. PMID:28095400

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

Integration of biological networks and gene expression data using Cytoscape

PubMed Central

Cline, Melissa S; Smoot, Michael; Cerami, Ethan; Kuchinsky, Allan; Landys, Nerius; Workman, Chris; Christmas, Rowan; Avila-Campilo, Iliana; Creech, Michael; Gross, Benjamin; Hanspers, Kristina; Isserlin, Ruth; Kelley, Ryan; Killcoyne, Sarah; Lotia, Samad; Maere, Steven; Morris, John; Ono, Keiichiro; Pavlovic, Vuk; Pico, Alexander R; Vailaya, Aditya; Wang, Peng-Liang; Adler, Annette; Conklin, Bruce R; Hood, Leroy; Kuiper, Martin; Sander, Chris; Schmulevich, Ilya; Schwikowski, Benno; Warner, Guy J; Ideker, Trey; Bader, Gary D

2013-01-01

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 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 and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape. PMID:17947979

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

PubMed Central

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

A dedicated network for social interaction processing in the primate brain.

PubMed

Sliwa, J; Freiwald, W A

2017-05-19

Primate cognition requires interaction processing. Interactions can reveal otherwise hidden properties of intentional agents, such as thoughts and feelings, and of inanimate objects, such as mass and material. Where and how interaction analyses are implemented in the brain is unknown. Using whole-brain functional magnetic resonance imaging in macaque monkeys, we discovered a network centered in the medial and ventrolateral prefrontal cortex that is exclusively engaged in social interaction analysis. Exclusivity of specialization was found for no other function anywhere in the brain. Two additional networks, a parieto-premotor and a temporal one, exhibited both social and physical interaction preference, which, in the temporal lobe, mapped onto a fine-grain pattern of object, body, and face selectivity. Extent and location of a dedicated system for social interaction analysis suggest that this function is an evolutionary forerunner of human mind-reading capabilities. Copyright © 2017, American Association for the Advancement of Science.

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

PubMed Central

Hsiao, Tzu-Hung; Chiu, Yu-Chiao; Hsu, Pei-Yin; Lu, Tzu-Pin; Lai, Liang-Chuan; Tsai, Mong-Hsun; Huang, Tim H.-M.; Chuang, Eric Y.; Chen, Yidong

2016-01-01

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC. PMID:26972162

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Modeling and simulating networks of interdependent protein interactions.

PubMed

Stöcker, Bianca K; Köster, Johannes; Zamir, Eli; Rahmann, Sven

2018-05-21

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package (https://bioconda.github.io).

Trophic level, successional age and trait matching determine specialization of deadwood-based interaction networks of saproxylic beetles

PubMed Central

Gossner, Martin M.; Grass, Ingo; Arnstadt, Tobias; Hofrichter, Martin; Floren, Andreas; Linsenmair, Karl Eduard; Weisser, Wolfgang W.; Steffan-Dewenter, Ingolf

2017-01-01

The specialization of ecological networks provides important insights into possible consequences of biodiversity loss for ecosystem functioning. However, mostly mutualistic and antagonistic interactions of living organisms have been studied, whereas detritivore networks and their successional changes are largely unexplored. We studied the interactions of saproxylic (deadwood-dependent) beetles with their dead host trees. In a large-scale experiment, 764 logs of 13 tree species were exposed to analyse network structure of three trophic groups of saproxylic beetles over 3 successional years. We found remarkably high specialization of deadwood-feeding xylophages and lower specialization of fungivorous and predatory species. During deadwood succession, community composition, network specialization and network robustness changed differently for the functional groups. To reveal potential drivers of network specialization, we linked species' functional traits to their network roles, and tested for trait matching between plant (i.e. chemical compounds) and beetle (i.e. body size) traits. We found that both plant and animal traits are major drivers of species specialization, and that trait matching can be more important in explaining interactions than neutral processes reflecting species abundance distributions. High network specialization in the early successional stage and decreasing network robustness during succession indicate vulnerability of detritivore networks to reduced tree species diversity and beetle extinctions, with unknown consequences for wood decomposition and nutrient cycling. PMID:28469020

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

PubMed Central

Lee, Insuk; Li, Zhihua; Marcotte, Edward M.

2007-01-01

Background Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org. PMID:17912365

Network representation of protein interactions: Theory of graph description and analysis.

PubMed

Kurzbach, Dennis

2016-09-01

A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue-resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three-dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin. © 2016 The Protein Society.

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.

PubMed

Defoort, Jonas; Van de Peer, Yves; Vermeirssen, Vanessa

2018-06-05

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.

Plasticity of brain wave network interactions and evolution across physiologic states

PubMed Central

Liu, Kang K. L.; Bartsch, Ronny P.; Lin, Aijing; Mantegna, Rosario N.; Ivanov, Plamen Ch.

2015-01-01

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function. PMID:26578891

Decreased functional connectivity to posterior cingulate cortex in major depressive disorder.

PubMed

Yang, Rui; Gao, Chengge; Wu, Xiaoping; Yang, Junle; Li, Shengbin; Cheng, Hu

2016-09-30

The default mode network (DMN) and its interaction with other key networks such as the salience network and executive network are keys to understand psychiatric and neurological disorders including major depressive disorder (MDD). In this study, we combined independent component analysis and seed based connectivity analysis to study the posterior default mode network between 20 patients with MDD and 25 normal controls, as well as pre-treatment and post-treatment conditions of the patients. Both correlated and anti-correlated networks centered at the posterior cingulate cortex (PCC) were examined (PCC+ and PCC-). Our results showed aberrant functional connectivity of the PCC+ and PCC- networks between patients and normal controls. Specifically, normal controls exhibited significantly higher connectivity between the PCC and frontal/temporal regions for the PCC+ network and stronger connectivity strength between the PCC and the insula/middle frontal cortex for the PCC- network. The overall connectivity strength of the PCC+ and PCC- networks was also significantly lower in MDD. Because the PCC is a hub in the DMN that interacts with other networks, our result suggested a stronger interaction between the DMN and the salience network but a weak interaction between the DMN and the executive network in MDD. The treatment using sertraline did increase the functional connectivity strength, especially in the PCC+ network. Despite a large inter-subject variability in the overall connectivity strengths and change of the PCC network in response to the treatment, a high correlation between change of connectivity strength and the Hamilton depression score was observed for both the PCC+ and PCC- network. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Dynamical analysis of yeast protein interaction network during the sake brewing process.

PubMed

Mirzarezaee, Mitra; Sadeghi, Mehdi; Araabi, Babak N

2011-12-01

Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.

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

PubMed

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

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

PubMed Central

Ye, Ping; Peyser, Brian D; Spencer, Forrest A; Bader, Joel S

2005-01-01

Background In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. Results We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). Conclusion Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed). PMID:16283923

Mass Spectrometry Analysis of Spatial Protein Networks by Colocalization Analysis (COLA).

PubMed

Mardakheh, Faraz K

2017-01-01

A major challenge in systems biology is comprehensive mapping of protein interaction networks. Crucially, such interactions are often dynamic in nature, necessitating methods that can rapidly mine the interactome across varied conditions and treatments to reveal change in the interaction networks. Recently, we described a fast mass spectrometry-based method to reveal functional interactions in mammalian cells on a global scale, by revealing spatial colocalizations between proteins (COLA) (Mardakheh et al., Mol Biosyst 13:92-105, 2017). As protein localization and function are inherently linked, significant colocalization between two proteins is a strong indication for their functional interaction. COLA uses rapid complete subcellular fractionation, coupled with quantitative proteomics to generate a subcellular localization profile for each protein quantified by the mass spectrometer. Robust clustering is then applied to reveal significant similarities in protein localization profiles, indicative of colocalization.

FACETS: multi-faceted functional decomposition of protein interaction networks

PubMed Central

Seah, Boon-Siew; Bhowmick, Sourav S.; Forbes Dewey, C.

2012-01-01

Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/ PMID:22908217

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

PubMed Central

Stephen, Emily P.; Lepage, Kyle Q.; Eden, Uri T.; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S.; Guenther, Frank H.; Kramer, Mark A.

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

PubMed

Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.

Exploration and Modulation of Brain Network Interactions with Noninvasive Brain Stimulation in Combination with Neuroimaging

PubMed Central

Shafi, Mouhsin M.; Westover, M. Brandon; Fox, Michael D.; Pascual-Leone, Alvaro

2012-01-01

Much recent work in systems neuroscience has focused on how dynamic interactions between different cortical regions underlie complex brain functions such as motor coordination, language, and emotional regulation. Various studies using neuroimaging and neurophysiologic techniques have suggested that in many neuropsychiatric disorders, these dynamic brain networks are dysregulated. Here we review the utility of combined noninvasive brain stimulation and neuroimaging approaches towards greater understanding of dynamic brain networks in health and disease. Brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, use electromagnetic principles to noninvasively alter brain activity, and induce focal but also network effects beyond the stimulation site. When combined with brain imaging techniques such as functional MRI, PET and EEG, these brain stimulation techniques enable a causal assessment of the interaction between different network components, and their respective functional roles. The same techniques can also be applied to explore hypotheses regarding the changes in functional connectivity that occur during task performance and in various disease states such as stroke, depression and schizophrenia. Finally, in diseases characterized by pathologic alterations in either the excitability within a single region or in the activity of distributed networks, such techniques provide a potential mechanism to alter cortical network function and architectures in a beneficial manner. PMID:22429242

fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study.

PubMed

Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand

2015-01-07

Network study on genes and proteins offers functional basics of the complexity of gene and protein, and its interacting partners. The gene fatty acid-binding protein 4 (fabp4) is found to be highly expressed in adipose tissue, and is one of the most abundant proteins in mature adipocytes. Our investigations on functional modules of fabp4 provide useful information on the functional genes interacting with fabp4, their biochemical properties and their regulatory functions. The present study shows that there are eight set of candidate genes: acp1, ext2, insr, lipe, ostf1, sncg, usp15, and vim that are strongly and functionally linked up with fabp4. Gene ontological analysis of network modules of fabp4 provides an explicit idea on the functional aspect of fabp4 and its interacting nodes. The hierarchal mapping on gene ontology indicates gene specific processes and functions as well as their compartmentalization in tissues. The fabp4 along with its interacting genes are involved in lipid metabolic activity and are integrated in multi-cellular processes of tissues and organs. They also have important protein/enzyme binding activity. Our study elucidated disease-associated nsSNP prediction for fabp4 and it is interesting to note that there are four rsID׳s (rs1051231, rs3204631, rs140925685 and rs141169989) with disease allelic variation (T104P, T126P, G27D and G90V respectively). On the whole, our gene network analysis presents a clear insight about the interactions and functions associated with fabp4 gene network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Visualisation and graph-theoretic analysis of a large-scale protein structural interactome

PubMed Central

Bolser, Dan; Dafas, Panos; Harrington, Richard; Park, Jong; Schroeder, Michael

2003-01-01

Background Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. Results We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Conclusions Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level. PMID:14531933

An interactive graphics program for manipulation and display of panel method geometry

NASA Technical Reports Server (NTRS)

Hall, J. F.; Neuhart, D. H.; Walkley, K. B.

1983-01-01

Modern aerodynamic panel methods that handle large, complex geometries have made evident the need to interactively manipulate, modify, and view such configurations. With this purpose in mind, the GEOM program was developed. It is a menu driven, interactive program that uses the Tektronix PLOT 10 graphics software to display geometry configurations which are characterized by an abutting set of networks. These networks are composed of quadrilateral panels which are described by the coordinates of their corners. GEOM is divided into fourteen executive controlled functions. These functions are used to build configurations, scale and rotate networks, transpose networks defining M and N lines, graphically display selected networks, join and split networks, create wake networks, produce symmetric images of networks, repanel and rename networks, display configuration cross sections, and output network geometry in two formats. A data base management system is used to facilitate data transfers in this program. A sample session illustrating various capabilities of the code is included as a guide to program operation.

Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

PubMed

Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

2008-10-01

Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

Interaction Control to Synchronize Non-synchronizable Networks.

PubMed

Schröder, Malte; Chakraborty, Sagar; Witthaut, Dirk; Nagler, Jan; Timme, Marc

2016-11-17

Synchronization constitutes one of the most fundamental collective dynamics across networked systems and often underlies their function. Whether a system may synchronize depends on the internal unit dynamics as well as the topology and strength of their interactions. For chaotic units with certain interaction topologies synchronization might be impossible across all interaction strengths, meaning that these networks are non-synchronizable. Here we propose the concept of interaction control, generalizing transient uncoupling, to induce desired collective dynamics in complex networks and apply it to synchronize even such non-synchronizable systems. After highlighting that non-synchronizability prevails for a wide range of networks of arbitrary size, we explain how a simple binary control may localize interactions in state space and thereby synchronize networks. Intriguingly, localizing interactions by a fixed control scheme enables stable synchronization across all connected networks regardless of topological constraints. Interaction control may thus ease the design of desired collective dynamics even without knowledge of the networks' exact interaction topology and consequently have implications for biological and self-organizing technical systems.

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

PubMed

Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

2015-01-16

Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

Comparison of Modules of Wild Type and Mutant Huntingtin and TP53 Protein Interaction Networks: Implications in Biological Processes and Functions

PubMed Central

Basu, Mahashweta; Bhattacharyya, Nitai P.; Mohanty, Pradeep K.

2013-01-01

Disease-causing mutations usually change the interacting partners of mutant proteins. In this article, we propose that the biological consequences of mutation are directly related to the alteration of corresponding protein protein interaction networks (PPIN). Mutation of Huntingtin (HTT) which causes Huntington's disease (HD) and mutations to TP53 which is associated with different cancers are studied as two example cases. We construct the PPIN of wild type and mutant proteins separately and identify the structural modules of each of the networks. The functional role of these modules are then assessed by Gene Ontology (GO) enrichment analysis for biological processes (BPs). We find that a large number of significantly enriched () GO terms in mutant PPIN were absent in the wild type PPIN indicating the gain of BPs due to mutation. Similarly some of the GO terms enriched in wild type PPIN cease to exist in the modules of mutant PPIN, representing the loss. GO terms common in modules of mutant and wild type networks indicate both loss and gain of BPs. We further assign relevant biological function(s) to each module by classifying the enriched GO terms associated with it. It turns out that most of these biological functions in HTT networks are already known to be altered in HD and those of TP53 networks are altered in cancers. We argue that gain of BPs, and the corresponding biological functions, are due to new interacting partners acquired by mutant proteins. The methodology we adopt here could be applied to genetic diseases where mutations alter the ability of the protein to interact with other proteins. PMID:23741403

Disentangling the multigenic and pleiotropic nature of molecular function

PubMed Central

2015-01-01

Background Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. Results We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. Conclusions Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. PMID:26678917

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

PubMed

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 is indicative of their strong influence in the protein protein interaction network. Similarly the newly proposed GEADCA helped identify the transcription factors with high centrality values indicative of their key roles in transcriptional regulation. The enrichment studies provided a list of molecular functions, biological processes and biochemical pathways associated with the constructed network. The study shows how pathway based databases may be used to create and analyze a relevant protein interaction network in glioma cancer stem cells and identify the essential elements within it to gather insights into the molecular interactions that regulate the properties of glioma stem cells. How these insights may be utilized to help the development of future research towards formulation of new management strategies have been discussed from a theoretical standpoint. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Synchronization within and Interaction between the Default and Dorsal Attention Networks in Early Infancy

PubMed Central

Gilmore, John H.; Shen, Dinggang; Smith, Jeffery Keith; Zhu, Hongtu

2013-01-01

An anticorrelated interaction between the dorsal attention and the default-mode networks has been observed, although how these 2 networks establish such relationship remains elusive. Behavioral studies have reported the emergence of attention and default network–related functions and a preliminary competing relationship between them at early infancy. This study attempted to test the hypothesis—resting-state functional magnetic resonance imaging will demonstrate not only improved network synchronization of the dorsal attention and the default networks, respectively, during the first 2 years of life but also an anticorrelated network interaction pattern between the 2 networks at 1 year which will be further enhanced at 2 years old. Our results demonstrate that both networks start from an isolated region in neonates but evolve to highly synchronized networks at 1 year old. Paralleling the individual network maturation process, the anticorrelated behaviors are absent at birth but become apparent at 1 year and are further enhanced during the second year of life. Our studies elucidate not only the individual maturation process of the dorsal attention and default networks but also offer evidence that the maturation of the individual networks may be needed prior exhibiting the adult-like interaction patterns between the 2 networks. PMID:22368080

Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, Cindy

2015-07-17

The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3

PubMed Central

Su, Gang; Morris, John H.; Demchak, Barry; Bader, Gary D.

2014-01-01

Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and pathway construction. Cytoscape provides core functionality to load, visualize, search, filter and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface and performance relative to previous versions. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the network, visualizing network associated data (attributes) and identifying clusters. It also highlights new features that benefit experienced users. PMID:25199793

Exploring biological interaction networks with tailored weighted quasi-bicliques

PubMed Central

2012-01-01

Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks. PMID:22759421

Integrative Analysis of Transcription Factor Combinatorial Interactions Using a Bayesian Tensor Factorization Approach

PubMed Central

Ye, Yusen; Gao, Lin; Zhang, Shihua

2017-01-01

Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions. PMID:29033978

Integrative Analysis of Transcription Factor Combinatorial Interactions Using a Bayesian Tensor Factorization Approach.

PubMed

Ye, Yusen; Gao, Lin; Zhang, Shihua

2017-01-01

Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions.

A Scalable Approach for Discovering Conserved Active Subnetworks across Species

PubMed Central

Verfaillie, Catherine M.; Hu, Wei-Shou; Myers, Chad L.

2010-01-01

Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-species network search algorithm, neXus (Network - cross(X)-species - Search), that discovers conserved, active subnetworks based on parallel differential expression studies in multiple species. Our approach leverages functional linkage networks, which provide more comprehensive coverage of functional relationships than physical interaction networks by combining heterogeneous types of genomic data. We applied our cross-species approach to identify conserved modules that are differentially active in stem cells relative to differentiated cells based on parallel gene expression studies and functional linkage networks from mouse and human. We find hundreds of conserved active subnetworks enriched for stem cell-associated functions such as cell cycle, DNA repair, and chromatin modification processes. Using a variation of this approach, we also find a number of species-specific networks, which likely reflect mechanisms of stem cell function that have diverged between mouse and human. We assess the statistical significance of the subnetworks by comparing them with subnetworks discovered on random permutations of the differential expression data. We also describe several case examples that illustrate the utility of comparative analysis of active subnetworks. PMID:21170309

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations

PubMed Central

Beleva Guthrie, Violeta; Masica, David L; Fraser, Andrew; Federico, Joseph; Fan, Yunfan; Camps, Manel; Karchin, Rachel

2018-01-01

Abstract The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous β-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure. PMID:29522102

An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network

PubMed Central

Holden, Brian J; Pinney, John W; Lovell, Simon C; Amoutzias, Grigoris D; Robertson, David L

2007-01-01

Background Alternative representations of biochemical networks emphasise different aspects of the data and contribute to the understanding of complex biological systems. In this study we present a variety of automated methods for visualisation of a protein-protein interaction network, using the basic helix-loop-helix (bHLH) family of transcription factors as an example. Results Network representations that arrange nodes (proteins) according to either continuous or discrete information are investigated, revealing the existence of protein sub-families and the retention of interactions following gene duplication events. Methods of network visualisation in conjunction with a phylogenetic tree are presented, highlighting the evolutionary relationships between proteins, and clarifying the context of network hubs and interaction clusters. Finally, an optimisation technique is used to create a three-dimensional layout of the phylogenetic tree upon which the protein-protein interactions may be projected. Conclusion We show that by incorporating secondary genomic, functional or phylogenetic information into network visualisation, it is possible to move beyond simple layout algorithms based on network topology towards more biologically meaningful representations. These new visualisations can give structure to complex networks and will greatly help in interpreting their evolutionary origins and functional implications. Three open source software packages (InterView, TVi and OptiMage) implementing our methods are available. PMID:17683601

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues.

PubMed

Wong, Aaron K; Krishnan, Arjun; Troyanskaya, Olga G

2018-05-25

GIANT2 (Genome-wide Integrated Analysis of gene Networks in Tissues) is an interactive web server that enables biomedical researchers to analyze their proteins and pathways of interest and generate hypotheses in the context of genome-scale functional maps of human tissues. The precise actions of genes are frequently dependent on their tissue context, yet direct assay of tissue-specific protein function and interactions remains infeasible in many normal human tissues and cell-types. With GIANT2, researchers can explore predicted tissue-specific functional roles of genes and reveal changes in those roles across tissues, all through interactive multi-network visualizations and analyses. Additionally, the NetWAS approach available through the server uses tissue-specific/cell-type networks predicted by GIANT2 to re-prioritize statistical associations from GWAS studies and identify disease-associated genes. GIANT2 predicts tissue-specific interactions by integrating diverse functional genomics data from now over 61 400 experiments for 283 diverse tissues and cell-types. GIANT2 does not require any registration or installation and is freely available for use at http://giant-v2.princeton.edu.

Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function.

PubMed

Burkle, Laura A; Marlin, John C; Knight, Tiffany M

2013-03-29

Using historic data sets, we quantified the degree to which global change over 120 years disrupted plant-pollinator interactions in a temperate forest understory community in Illinois, USA. We found degradation of interaction network structure and function and extirpation of 50% of bee species. Network changes can be attributed to shifts in forb and bee phenologies resulting in temporal mismatches, nonrandom species extinctions, and loss of spatial co-occurrences between extant species in modified landscapes. Quantity and quality of pollination services have declined through time. The historic network showed flexibility in response to disturbance; however, our data suggest that networks will be less resilient to future changes.

Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering

PubMed Central

Prescott, Thomas P.; Lang, Moritz; Papachristodoulou, Antonis

2015-01-01

Large, naturally evolved biomolecular networks typically fulfil multiple functions. When modelling or redesigning such systems, functional subsystems are often analysed independently first, before subsequent integration into larger-scale computational models. In the design and analysis process, it is therefore important to quantitatively analyse and predict the dynamics of the interactions between integrated subsystems; in particular, how the incremental effect of integrating a subsystem into a network depends on the existing dynamics of that network. In this paper we present a framework for simulating the contribution of any given functional subsystem when integrated together with one or more other subsystems. This is achieved through a cascaded layering of a network into functional subsystems, where each layer is defined by an appropriate subset of the reactions. We exploit symmetries in our formulation to exhaustively quantify each subsystem’s incremental effects with minimal computational effort. When combining subsystems, their isolated behaviour may be amplified, attenuated, or be subject to more complicated effects. We propose the concept of mutual dynamics to quantify such nonlinear phenomena, thereby defining the incompatibility and cooperativity between all pairs of subsystems when integrated into any larger network. We exemplify our theoretical framework by analysing diverse behaviours in three dynamic models of signalling and metabolic pathways: the effect of crosstalk mechanisms on the dynamics of parallel signal transduction pathways; reciprocal side-effects between several integral feedback mechanisms and the subsystems they stabilise; and consequences of nonlinear interactions between elementary flux modes in glycolysis for metabolic engineering strategies. Our analysis shows that it is not sufficient to just specify subsystems and analyse their pairwise interactions; the environment in which the interaction takes place must also be explicitly defined. Our framework provides a natural representation of nonlinear interaction phenomena, and will therefore be an important tool for modelling large-scale evolved or synthetic biomolecular networks. PMID:25933116

Causal functional contributions and interactions in the attention network of the brain: an objective multi-perturbation analysis.

PubMed

Zavaglia, Melissa; Hilgetag, Claus C

2016-06-01

Spatial attention is a prime example for the distributed network functions of the brain. Lesion studies in animal models have been used to investigate intact attentional mechanisms as well as perspectives for rehabilitation in the injured brain. Here, we systematically analyzed behavioral data from cooling deactivation and permanent lesion experiments in the cat, where unilateral deactivation of the posterior parietal cortex (in the vicinity of the posterior middle suprasylvian cortex, pMS) or the superior colliculus (SC) cause a severe neglect in the contralateral hemifield. Counterintuitively, additional deactivation of structures in the opposite hemisphere reverses the deficit. Using such lesion data, we employed a game-theoretical approach, multi-perturbation Shapley value analysis (MSA), for inferring functional contributions and network interactions of bilateral pMS and SC from behavioral performance in visual attention studies. The approach provides an objective theoretical strategy for lesion inferences and allows a unique quantitative characterization of regional functional contributions and interactions on the basis of multi-perturbations. The quantitative analysis demonstrated that right posterior parietal cortex and superior colliculus made the strongest positive contributions to left-field orienting, while left brain regions had negative contributions, implying that their perturbation may reverse the effects of contralateral lesions or improve normal function. An analysis of functional modulations and interactions among the regions revealed redundant interactions (implying functional overlap) between regions within each hemisphere, and synergistic interactions between bilateral regions. To assess the reliability of the MSA method in the face of variable and incomplete input data, we performed a sensitivity analysis, investigating how much the contribution values of the four regions depended on the performance of specific configurations and on the prediction of unknown performances. The results suggest that the MSA approach is sensitive to categorical, but insensitive to gradual changes in the input data. Finally, we created a basic network model that was based on the known anatomical interactions among cortical-tectal regions and reproduced the experimentally observed behavior in visual orienting. We discuss the structural organization of the network model relative to the causal modulations identified by MSA, to aid a mechanistic understanding of the attention network of the brain.

Planting increases the abundance and structure complexity of soil core functional genes relevant to carbon and nitrogen cycling

PubMed Central

Wang, Feng; Liang, Yuting; Jiang, Yuji; Yang, Yunfeng; Xue, Kai; Xiong, Jinbo; Zhou, Jizhong; Sun, Bo

2015-01-01

Plants have an important impact on soil microbial communities and their functions. However, how plants determine the microbial composition and network interactions is still poorly understood. During a four-year field experiment, we investigated the functional gene composition of three types of soils (Phaeozem, Cambisols and Acrisol) under maize planting and bare fallow regimes located in cold temperate, warm temperate and subtropical regions, respectively. The core genes were identified using high-throughput functional gene microarray (GeoChip 3.0), and functional molecular ecological networks (fMENs) were subsequently developed with the random matrix theory (RMT)-based conceptual framework. Our results demonstrated that planting significantly (P < 0.05) increased the gene alpha-diversity in terms of richness and Shannon – Simpson’s indexes for all three types of soils and 83.5% of microbial alpha-diversity can be explained by the plant factor. Moreover, planting had significant impacts on the microbial community structure and the network interactions of the microbial communities. The calculated network complexity was higher under maize planting than under bare fallow regimes. The increase of the functional genes led to an increase in both soil respiration and nitrification potential with maize planting, indicating that changes in the soil microbial communities and network interactions influenced ecological functioning. PMID:26396042

An interactional network of genes involved in chitin synthesis in Saccharomyces cerevisiae.

PubMed

Lesage, Guillaume; Shapiro, Jesse; Specht, Charles A; Sdicu, Anne-Marie; Ménard, Patrice; Hussein, Shamiza; Tong, Amy Hin Yan; Boone, Charles; Bussey, Howard

2005-02-16

In S. cerevisiae the beta-1,4-linked N-acetylglucosamine polymer, chitin, is synthesized by a family of 3 specialized but interacting chitin synthases encoded by CHS1, CHS2 and CHS3. Chs2p makes chitin in the primary septum, while Chs3p makes chitin in the lateral cell wall and in the bud neck, and can partially compensate for the lack of Chs2p. Chs3p requires a pathway of Bni4p, Chs4p, Chs5p, Chs6p and Chs7p for its localization and activity. Chs1p is thought to have a septum repair function after cell separation. To further explore interactions in the chitin synthase family and to find processes buffering chitin synthesis, we compiled a genetic interaction network of genes showing synthetic interactions with CHS1, CHS3 and genes involved in Chs3p localization and function and made a phenotypic analysis of their mutants. Using deletion mutants in CHS1, CHS3, CHS4, CHS5, CHS6, CHS7 and BNI4 in a synthetic genetic array analysis we assembled a network of 316 interactions among 163 genes. The interaction network with CHS3, CHS4, CHS5, CHS6, CHS7 or BNI4 forms a dense neighborhood, with many genes functioning in cell wall assembly or polarized secretion. Chitin levels were altered in 54 of the mutants in individually deleted genes, indicating a functional relationship between them and chitin synthesis. 32 of these mutants triggered the chitin stress response, with elevated chitin levels and a dependence on CHS3. A large fraction of the CHS1-interaction set was distinct from that of the CHS3 network, indicating broad roles for Chs1p in buffering both Chs2p function and more global cell wall robustness. Based on their interaction patterns and chitin levels we group interacting mutants into functional categories. Genes interacting with CHS3 are involved in the amelioration of cell wall defects and in septum or bud neck chitin synthesis, and we newly assign a number of genes to these functions. Our genetic analysis of genes not interacting with CHS3 indicate expanded roles for Chs4p, Chs5p and Chs6p in secretory protein trafficking and of Bni4p in bud neck organization.

Identification of Modules in Protein-Protein Interaction Networks

NASA Astrophysics Data System (ADS)

Erten, Sinan; Koyutürk, Mehmet

In biological systems, most processes are carried out through orchestration of multiple interacting molecules. These interactions are often abstracted using network models. A key feature of cellular networks is their modularity, which contributes significantly to the robustness, as well as adaptability of biological systems. Therefore, modularization of cellular networks is likely to be useful in obtaining insights into the working principles of cellular systems, as well as building tractable models of cellular organization and dynamics. A common, high-throughput source of data on molecular interactions is in the form of physical interactions between proteins, which are organized into protein-protein interaction (PPI) networks. This chapter provides an overview on identification and analysis of functional modules in PPI networks, which has been an active area of research in the last decade.

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry.

PubMed

Pflieger, Delphine; Gonnet, Florence; de la Fuente van Bentem, Sergio; Hirt, Heribert; de la Fuente, Alberto

2011-01-01

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks. Copyright © 2010 Wiley Periodicals, Inc.

Evidence for Functional Networks within the Human Brain's White Matter.

PubMed

Peer, Michael; Nitzan, Mor; Bick, Atira S; Levin, Netta; Arzy, Shahar

2017-07-05

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry. SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders. Copyright © 2017 the authors 0270-6474/17/376394-14$15.00/0.

Interaction Control to Synchronize Non-synchronizable Networks

PubMed Central

Schröder, Malte; Chakraborty, Sagar; Witthaut, Dirk; Nagler, Jan; Timme, Marc

2016-01-01

Synchronization constitutes one of the most fundamental collective dynamics across networked systems and often underlies their function. Whether a system may synchronize depends on the internal unit dynamics as well as the topology and strength of their interactions. For chaotic units with certain interaction topologies synchronization might be impossible across all interaction strengths, meaning that these networks are non-synchronizable. Here we propose the concept of interaction control, generalizing transient uncoupling, to induce desired collective dynamics in complex networks and apply it to synchronize even such non-synchronizable systems. After highlighting that non-synchronizability prevails for a wide range of networks of arbitrary size, we explain how a simple binary control may localize interactions in state space and thereby synchronize networks. Intriguingly, localizing interactions by a fixed control scheme enables stable synchronization across all connected networks regardless of topological constraints. Interaction control may thus ease the design of desired collective dynamics even without knowledge of the networks’ exact interaction topology and consequently have implications for biological and self-organizing technical systems. PMID:27853266

Protein complexes and functional modules in molecular networks

NASA Astrophysics Data System (ADS)

Spirin, Victor; Mirny, Leonid A.

2003-10-01

Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

Dynamic Processes in Network Goods: Modeling, Analysis and Applications

ERIC Educational Resources Information Center

Paothong, Arnut

2013-01-01

The network externality function plays a very important role in the study of economic network industries. Moreover, the consumer group dynamic interactions coupled with network externality concept is going to play a dominant role in the network goods in the 21st century. The existing literature is stemmed on a choice of externality function with…

Brain network dysfunction in youth with obsessive-compulsive disorder induced by simple uni-manual behavior: The role of the dorsal anterior cingulate cortex

PubMed Central

Friedman, Amy L.; Burgess, Ashley; Ramaseshan, Karthik; Easter, Phil; Khatib, Dalal; Chowdury, Asadur; Arnold, Paul D.; Hanna, Gregory L.; Rosenberg, David R.; Diwadkar, Vaibhav A.

2017-01-01

In an effort to elucidate differences in functioning brain networks between youth with obsessive-compulsive disorder and controls, we used fMRI signals to analyze brain network interactions of the dorsal anterior cingulate cortex (dACC) during visually coordinated motor responses. Subjects made a uni-manual response to briefly presented probes, at periodic (allowing participants to maintain a “motor set”) or random intervals (demanding reactive responses). Network interactions were assessed using psycho-physiological interaction (PPI), a basic model of functional connectivity evaluating modulatory effects of the dACC in the context of each task condition. Across conditions, OCD were characterized by hyper-modulation by the dACC, with loci alternatively observed as both condition-general and condition-specific. Thus, dynamically driven task demands during simple uni-manual motor control induce compensatory network interactions in cortical-thalamic regions in OCD. These findings support previous research in OCD showing compensatory network interactions during complex memory tasks, but establish that these network effects are observed during basic sensorimotor processing. Thus, these patterns of network dysfunction may in fact be independent of the complexity of tasks used to induce brain network activity. Hypothesis-driven approaches coupled with sophisticated network analyses are a highly valuable approach in using fMRI to uncover mechanisms in disorders like OCD. PMID:27992792

Interaction of multiple networks modulated by the working memory training based on real-time fMRI

NASA Astrophysics Data System (ADS)

Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie

2015-03-01

Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

MINE: Module Identification in Networks

PubMed Central

2011-01-01

Background Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks. Results MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties. Conclusions MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans. PMID:21605434

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

PubMed

Thibodeau, Asa; Márquez, Eladio J; Luo, Oscar; Ruan, Yijun; Menghi, Francesca; Shin, Dong-Guk; Stitzel, Michael L; Vera-Licona, Paola; Ucar, Duygu

2016-06-01

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

Molecular Interaction Map of the Mammalian Cell Cycle Control and DNA Repair Systems

PubMed Central

Kohn, Kurt W.

1999-01-01

Eventually to understand the integrated function of the cell cycle regulatory network, we must organize the known interactions in the form of a diagram, map, and/or database. A diagram convention was designed capable of unambiguous representation of networks containing multiprotein complexes, protein modifications, and enzymes that are substrates of other enzymes. To facilitate linkage to a database, each molecular species is symbolically represented only once in each diagram. Molecular species can be located on the map by means of indexed grid coordinates. Each interaction is referenced to an annotation list where pertinent information and references can be found. Parts of the network are grouped into functional subsystems. The map shows how multiprotein complexes could assemble and function at gene promoter sites and at sites of DNA damage. It also portrays the richness of connections between the p53-Mdm2 subsystem and other parts of the network. PMID:10436023

Fluxoids behavior in superconducting ladders

NASA Astrophysics Data System (ADS)

Sharon, Omri J.; Haham, Noam; Shaulov, Avner; Yeshurun, Yosef

2018-03-01

The nature of the interaction between fluxoids and between them and the external magnetic field is studied in one-dimensional superconducting networks. An Ising like expression is derived for the energy of a network revealing that fluxoids behave as repulsively interacting objects driven towards the network center by the effective applied field. Competition between these two interactions determines the equilibrium arrangement of fluxoids in the network as a function of the applied field. It is demonstrated that the fluxoids configurations are not always commensurate to the network symmetry. Incommensurate, degenerated configurations may be formed even in networks with an odd number of loops.

Revealing networks from dynamics: an introduction

NASA Astrophysics Data System (ADS)

Timme, Marc; Casadiego, Jose

2014-08-01

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.

Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data.

PubMed

Deshpande, Gopikrishna; Santhanam, Priya; Hu, Xiaoping

2011-01-15

Most neuroimaging studies of resting state networks have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. In this study we investigated both FC and effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data - default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN). A method called correlation-purged Granger causality analysis was used, not only enabling the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounting for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN is very tightly coupled compared to the other networks while the DMN forms the backbone around which the other networks amalgamate. The pattern of bidirectional causal interactions indicates that posterior cingulate and posterior inferior parietal lobule of DMN act as major hubs. The pattern of unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively. Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network. Our findings indicate the following. First, regions whose activities are not synchronized interact via time-delayed causal influences. Second, the causal interactions are organized such that cingulo-parietal regions act as hubs. Finally, segregation of different resting state networks is not clear cut but only by soft boundaries. Copyright © 2010 Elsevier Inc. All rights reserved.

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.

PubMed

Peng, Jiajie; Zhang, Xuanshuo; Hui, Weiwei; Lu, Junya; Li, Qianqian; Liu, Shuhui; Shang, Xuequn

2018-03-19

Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

PubMed

Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

2018-05-01

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

A Small World of Neuronal Synchrony

PubMed Central

Yu, Shan; Huang, Debin; Singer, Wolf

2008-01-01

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of “hubs” in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding. PMID:18400792

Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications

PubMed Central

Blacklock, Kristin; Verkhivker, Gennady M.

2014-01-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks. PMID:24922508

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications.

PubMed

Blacklock, Kristin; Verkhivker, Gennady M

2014-06-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.

Networks within networks: The neuronal control of breathing

PubMed Central

Garcia, Alfredo J.; Zanella, Sebastien; Koch, Henner; Doi, Atsushi; Ramirez, Jan-Marino

2013-01-01

Breathing emerges through complex network interactions involving neurons distributed throughout the nervous system. The respiratory rhythm generating network is composed of micro networks functioning within larger networks to generate distinct rhythms and patterns that characterize breathing. The pre-Bötzinger complex, a rhythm generating network located within the ventrolateral medulla assumes a core function without which respiratory rhythm generation and breathing cease altogether. It contains subnetworks with distinct synaptic and intrinsic membrane properties that give rise to different types of respiratory rhythmic activities including eupneic, sigh, and gasping activities. While critical aspects of these rhythmic activities are preserved when isolated in in vitro preparations, the pre-Bötzinger complex functions in the behaving animal as part of a larger network that receives important inputs from areas such as the pons and parafacial nucleus. The respiratory network is also an integrator of modulatory and sensory inputs that imbue the network with the important ability to adapt to changes in the behavioral, metabolic, and developmental conditions of the organism. This review summarizes our current understanding of these interactions and relates the emerging concepts to insights gained in other rhythm generating networks. PMID:21333801

FUSE: a profit maximization approach for functional summarization of biological networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes; Yu, Hanry

2012-03-21

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

Context-based retrieval of functional modules in protein-protein interaction networks.

PubMed

Dobay, Maria Pamela; Stertz, Silke; Delorenzi, Mauro

2017-03-27

Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods. Here, we estimate the maximum performance of context filtering to isolate a Kyoto Encyclopedia of Genes and Genomes (KEGG) network Ki from a union of KEGG networks and its network neighborhood. The work gives insights on the use of human PPIs in network neighborhood approaches for functional inference. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, Ye; Zhang, Ping; Qin, Yujia

When trying to discern network interactions among different species/populations in microbial communities interests have been evoked in recent years, but little information is available about temporal dynamics of microbial network interactions in response to environmental perturbations. We modified the random matrix theory-based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the networkmore » interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140–269 days). Unlike previous studies in other habitats, negative interactions predominated in a time-series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. In particular, several keystone sulfate-reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations.« less

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

PubMed

Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

2010-10-25

Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large-scale datasets. Software and data sets are available at http://www.sfu.ca/~ester/software/DECOB.zip.

Predicting protein functions from redundancies in large-scale protein interaction networks

NASA Technical Reports Server (NTRS)

Samanta, Manoj Pratim; Liang, Shoudan

2003-01-01

Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

PubMed Central

2012-01-01

Background The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment. Results Our findings demonstrate that a miRNA’s functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set. Conclusions We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment. PMID:22929553

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

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 .

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG.

PubMed

Rojas, Gonzalo M; Fuentes, Jorge A; Gálvez, Marcelo

2016-01-01

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10-20 EEG electrodes with Yeo's seven functional connectivity networks.

Prediction of interface residue based on the features of residue interaction network.

PubMed

Jiao, Xiong; Ranganathan, Shoba

2017-11-07

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Locating overlapping dense subgraphs in gene (protein) association networks and predicting novel protein functional groups among these subgraphs

NASA Astrophysics Data System (ADS)

Palla, Gergely; Derenyi, Imre; Farkas, Illes J.; Vicsek, Tamas

2006-03-01

Most tasks in a cell are performed not by individual proteins, but by functional groups of proteins (either physically interacting with each other or associated in other ways). In gene (protein) association networks these groups show up as sets of densely connected nodes. In the yeast, Saccharomyces cerevisiae, known physically interacting groups of proteins (called protein complexes) strongly overlap: the total number of proteins contained by these complexes by far underestimates the sum of their sizes (2750 vs. 8932). Thus, most functional groups of proteins, both physically interacting and other, are likely to share many of their members with other groups. However, current algorithms searching for dense groups of nodes in networks usually exclude overlaps. With the aim to discover both novel functions of individual proteins and novel protein functional groups we combine in protein association networks (i) a search for overlapping dense subgraphs based on the Clique Percolation Method (CPM) (Palla, G., et.al. Nature 435, 814-818 (2005), http://angel.elte.hu/clustering), which explicitly allows for overlaps among the groups, and (ii) a verification and characterization of the identified groups of nodes (proteins) with the help of standard annotation databases listing known functions.

The World as Functional Learning Environment: An Intercultural Learning Network. Interactive Technology Laboratory Report #7.

ERIC Educational Resources Information Center

Cohen, Moshe; And Others

Electronic networks provide new opportunities to create functional learning environments which allow students in many different locations to carry out joint educational activities. A set of participant observation studies was conducted in the context of a cross-cultural, cross-language network called the Intercultural Learning Network in order to…

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.

PubMed

Tudor, Catalina O; Ross, Karen E; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

2015-01-01

Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases. © The Author(s) 2015. Published by Oxford University Press.

Predicting Human Protein Subcellular Locations by the Ensemble of Multiple Predictors via Protein-Protein Interaction Network with Edge Clustering Coefficients

PubMed Central

Du, Pufeng; Wang, Lusheng

2014-01-01

One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods. PMID:24466278

Interactions between the default network and dorsal attention network vary across default subsystems, time, and cognitive states.

PubMed

Dixon, Matthew L; Andrews-Hanna, Jessica R; Spreng, R Nathan; Irving, Zachary C; Mills, Caitlin; Girn, Manesh; Christoff, Kalina

2017-02-15

Anticorrelation between the default network (DN) and dorsal attention network (DAN) is thought to be an intrinsic aspect of functional brain organization reflecting competing functions. However, the effect size of functional connectivity (FC) between the DN and DAN has yet to be established. Furthermore, the stability of anticorrelations across distinct DN subsystems, different contexts, and time, remains unexplored. In study 1 we summarize effect sizes of DN-DAN FC from 20 studies, and in study 2 we probe the variability of DN-DAN interactions across six different cognitive states in a new data set. We show that: (i) the DN and DAN have an independent rather than anticorrelated relationship when global signal regression is not used (median effect size across studies: r=-.06; 95% CI: -.15 to .08); (ii) the DAN exhibits weak negative FC with the DN Core subsystem but is uncorrelated with the dorsomedial prefrontal and medial temporal lobe subsystems; (iii) DN-DAN interactions vary significantly across different cognitive states; (iv) DN-DAN FC fluctuates across time between periods of anticorrelation and periods of positive correlation; and (v) changes across time in the strength of DN-DAN coupling are coordinated with interactions involving the frontoparietal control network (FPCN). Overall, the observed weak effect sizes related to DN-DAN anticorrelation suggest the need to re-conceptualize the nature of interactions between these networks. Furthermore, our findings demonstrate that DN-DAN interactions are not stable, but rather, exhibit substantial variability across time and context, and are coordinated with broader network dynamics involving the FPCN. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Emergent properties of interacting populations of spiking neurons.

PubMed

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

Symptom-specific amygdala hyperactivity modulates motor control network in conversion disorder.

PubMed

Hassa, Thomas; Sebastian, Alexandra; Liepert, Joachim; Weiller, Cornelius; Schmidt, Roger; Tüscher, Oliver

2017-01-01

Initial historical accounts as well as recent data suggest that emotion processing is dysfunctional in conversion disorder patients and that this alteration may be the pathomechanistic neurocognitive basis for symptoms in conversion disorder. However, to date evidence of direct interaction of altered negative emotion processing with motor control networks in conversion disorder is still lacking. To specifically study the neural correlates of emotion processing interacting with motor networks we used a task combining emotional and sensorimotor stimuli both separately as well as simultaneously during functional magnetic resonance imaging in a well characterized group of 13 conversion disorder patients with functional hemiparesis and 19 demographically matched healthy controls. We performed voxelwise statistical parametrical mapping for a priori regions of interest within emotion processing and motor control networks. Psychophysiological interaction (PPI) was used to test altered functional connectivity of emotion and motor control networks. Only during simultaneous emotional stimulation and passive movement of the affected hand patients displayed left amygdala hyperactivity. PPI revealed increased functional connectivity in patients between the left amygdala and the (pre-)supplemental motor area and the subthalamic nucleus, key regions within the motor control network. These findings suggest a novel mechanistic direct link between dysregulated emotion processing and motor control circuitry in conversion disorder.

Emergent Properties of Interacting Populations of Spiking Neurons

PubMed Central

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations. PMID:22207844

Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network.

PubMed

Deng, Lei; Wu, Hongjie; Liu, Chuyao; Zhan, Weihua; Zhang, Jingpu

2018-06-01

Long non-coding RNAs (lncRNAs) are involved in many biological processes, such as immune response, development, differentiation and gene imprinting and are associated with diseases and cancers. But the functions of the vast majority of lncRNAs are still unknown. Predicting the biological functions of lncRNAs is one of the key challenges in the post-genomic era. In our work, We first build a global network including a lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network according to the expressions and interactions, then extract the topological feature vectors of the global network. Using these features, we present an SVM-based machine learning approach, PLNRGO, to annotate human lncRNAs. In PLNRGO, we construct a training data set according to the proteins with GO annotations and train a binary classifier for each GO term. We assess the performance of PLNRGO on our manually annotated lncRNA benchmark and a protein-coding gene benchmark with known functional annotations. As a result, the performance of our method is significantly better than that of other state-of-the-art methods in terms of maximum F-measure and coverage. Copyright © 2018 Elsevier Ltd. All rights reserved.

A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities

PubMed Central

Vizeacoumar, Franco J; Arnold, Roland; Vizeacoumar, Frederick S; Chandrashekhar, Megha; Buzina, Alla; Young, Jordan T F; Kwan, Julian H M; Sayad, Azin; Mero, Patricia; Lawo, Steffen; Tanaka, Hiromasa; Brown, Kevin R; Baryshnikova, Anastasia; Mak, Anthony B; Fedyshyn, Yaroslav; Wang, Yadong; Brito, Glauber C; Kasimer, Dahlia; Makhnevych, Taras; Ketela, Troy; Datti, Alessandro; Babu, Mohan; Emili, Andrew; Pelletier, Laurence; Wrana, Jeff; Wainberg, Zev; Kim, Philip M; Rottapel, Robert; O'Brien, Catherine A; Andrews, Brenda; Boone, Charles; Moffat, Jason

2013-01-01

Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large-scale sequencing efforts. Using genome-scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co-culture competition assays to generate a high-confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non-isogenic cancer cell lines. For example, the PTEN−/− DiE genes reveal a signature that can preferentially classify PTEN-dependent genotypes across a series of non-isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model. PMID:24104479

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.

PubMed

Reinharz, Vladimir; Soulé, Antoine; Westhof, Eric; Waldispühl, Jérôme; Denise, Alain

2018-05-04

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.

An interactional network of genes involved in chitin synthesis in Saccharomyces cerevisiae

PubMed Central

Lesage, Guillaume; Shapiro, Jesse; Specht, Charles A; Sdicu, Anne-Marie; Ménard, Patrice; Hussein, Shamiza; Tong, Amy Hin Yan; Boone, Charles; Bussey, Howard

2005-01-01

Background In S. cerevisiae the β-1,4-linked N-acetylglucosamine polymer, chitin, is synthesized by a family of 3 specialized but interacting chitin synthases encoded by CHS1, CHS2 and CHS3. Chs2p makes chitin in the primary septum, while Chs3p makes chitin in the lateral cell wall and in the bud neck, and can partially compensate for the lack of Chs2p. Chs3p requires a pathway of Bni4p, Chs4p, Chs5p, Chs6p and Chs7p for its localization and activity. Chs1p is thought to have a septum repair function after cell separation. To further explore interactions in the chitin synthase family and to find processes buffering chitin synthesis, we compiled a genetic interaction network of genes showing synthetic interactions with CHS1, CHS3 and genes involved in Chs3p localization and function and made a phenotypic analysis of their mutants. Results Using deletion mutants in CHS1, CHS3, CHS4, CHS5, CHS6, CHS7 and BNI4 in a synthetic genetic array analysis we assembled a network of 316 interactions among 163 genes. The interaction network with CHS3, CHS4, CHS5, CHS6, CHS7 or BNI4 forms a dense neighborhood, with many genes functioning in cell wall assembly or polarized secretion. Chitin levels were altered in 54 of the mutants in individually deleted genes, indicating a functional relationship between them and chitin synthesis. 32 of these mutants triggered the chitin stress response, with elevated chitin levels and a dependence on CHS3. A large fraction of the CHS1-interaction set was distinct from that of the CHS3 network, indicating broad roles for Chs1p in buffering both Chs2p function and more global cell wall robustness. Conclusion Based on their interaction patterns and chitin levels we group interacting mutants into functional categories. Genes interacting with CHS3 are involved in the amelioration of cell wall defects and in septum or bud neck chitin synthesis, and we newly assign a number of genes to these functions. Our genetic analysis of genes not interacting with CHS3 indicate expanded roles for Chs4p, Chs5p and Chs6p in secretory protein trafficking and of Bni4p in bud neck organization. PMID:15715908

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks.

PubMed

Havugimana, Pierre C; Hu, Pingzhao; Emili, Andrew

2017-10-01

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

Architecture of the human interactome defines protein communities and disease networks

PubMed Central

Huttlin, Edward L.; Bruckner, Raphael J.; Paulo, Joao A.; Cannon, Joe R.; Ting, Lily; Baltier, Kurt; Colby, Greg; Gebreab, Fana; Gygi, Melanie P.; Parzen, Hannah; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Pontano-Vaites, Laura; Swarup, Sharan; White, Anne E.; Schweppe, Devin K.; Rad, Ramin; Erickson, Brian K.; Obar, Robert A.; Guruharsha, K.G.; Li, Kejie; Artavanis-Tsakonas, Spyros; Gygi, Steven P.; Harper, J. Wade

2017-01-01

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization. PMID:28514442

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks

PubMed Central

Thibodeau, Asa; Márquez, Eladio J.; Luo, Oscar; Ruan, Yijun; Shin, Dong-Guk; Stitzel, Michael L.; Ucar, Duygu

2016-01-01

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/. PMID:27336171

Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation.

PubMed

Deng, Ye; Zhang, Ping; Qin, Yujia; Tu, Qichao; Yang, Yunfeng; He, Zhili; Schadt, Christopher Warren; Zhou, Jizhong

2016-01-01

Discerning network interactions among different species/populations in microbial communities has evoked substantial interests in recent years, but little information is available about temporal dynamics of microbial network interactions in response to environmental perturbations. Here, we modified the random matrix theory-based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the network interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140-269 days). Unlike previous studies in other habitats, negative interactions predominated in a time-series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. Particularly, several keystone sulfate-reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

Prediction and functional analysis of the sweet orange protein-protein interaction network.

PubMed

Ding, Yu-Duan; Chang, Ji-Wei; Guo, Jing; Chen, Dijun; Li, Sen; Xu, Qiang; Deng, Xiu-Xin; Cheng, Yun-Jiang; Chen, Ling-Ling

2014-08-05

Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species. In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks. Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis.

PubMed

Hao, Tong; Zeng, Zheng; Wang, Bin; Zhang, Yichen; Liu, Yichen; Geng, Xuyun; Sun, Jinsheng

2014-03-27

The protein-protein interaction network (PIN) is an effective information tool for understanding the complex biological processes inside the cell and solving many biological problems such as signaling pathway identification and prediction of protein functions. Eriocheir sinensis is a highly-commercial aquaculture species with an unclear proteome background which hinders the construction and development of PIN for E. sinensis. However, in recent years, the development of next-generation deep-sequencing techniques makes it possible to get high throughput data of E. sinensis tanscriptome and subsequently obtain a systematic overview of the protein-protein interaction system. In this work we sequenced the transcriptional RNA of eyestalk, Y-organ and hepatopancreas in E. sinensis and generated a PIN of E. sinensis which included 3,223 proteins and 35,787 interactions. Each protein-protein interaction in the network was scored according to the homology and genetic relationship. The signaling sub-network, representing the signal transduction pathways in E. sinensis, was extracted from the global network, which depicted a global view of the signaling systems in E. sinensis. Seven basic signal transduction pathways were identified in E. sinensis. By investigating the evolution paths of the seven pathways, we found that these pathways got mature in different evolutionary stages. Moreover, the functions of unclassified proteins and unigenes in the PIN of E. sinensis were predicted. Specifically, the functions of 549 unclassified proteins related to 864 unclassified unigenes were assigned, which respectively covered 76% and 73% of all the unclassified proteins and unigenes in the network. The PIN generated in this work is the first large-scale PIN of aquatic crustacean, thereby providing a paradigmatic blueprint of the aquatic crustacean interactome. Signaling sub-network extracted from the global PIN depicts the interaction of different signaling proteins and the evolutionary paths of the identified signal transduction pathways. Furthermore, the function assignment of unclassified proteins based on the PIN offers a new reference in protein function exploration. More importantly, the construction of the E. sinensis PIN provides necessary experience for the exploration of PINs in other aquatic crustacean species.

Using the underlying biological organization of the Mycobacterium tuberculosis functional network for protein function prediction.

PubMed

Mazandu, Gaston K; Mulder, Nicola J

2012-07-01

Despite ever-increasing amounts of sequence and functional genomics data, there is still a deficiency of functional annotation for many newly sequenced proteins. For Mycobacterium tuberculosis (MTB), more than half of its genome is still uncharacterized, which hampers the search for new drug targets within the bacterial pathogen and limits our understanding of its pathogenicity. As for many other genomes, the annotations of proteins in the MTB proteome were generally inferred from sequence homology, which is effective but its applicability has limitations. We have carried out large-scale biological data integration to produce an MTB protein functional interaction network. Protein functional relationships were extracted from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and additional functional interactions from microarray, sequence and protein signature data. The confidence level of protein relationships in the additional functional interaction data was evaluated using a dynamic data-driven scoring system. This functional network has been used to predict functions of uncharacterized proteins using Gene Ontology (GO) terms, and the semantic similarity between these terms measured using a state-of-the-art GO similarity metric. To achieve better trade-off between improvement of quality, genomic coverage and scalability, this prediction is done by observing the key principles driving the biological organization of the functional network. This study yields a new functionally characterized MTB strain CDC1551 proteome, consisting of 3804 and 3698 proteins out of 4195 with annotations in terms of the biological process and molecular function ontologies, respectively. These data can contribute to research into the Development of effective anti-tubercular drugs with novel biological mechanisms of action. Copyright © 2011 Elsevier B.V. All rights reserved.

Discovering functional interdependence relationship in PPI networks for protein complex identification.

PubMed

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, PCIFI was used to identify protein complexes in real PPI network data and the protein complexes it found were matched against those that were previously known in MIPS. The results show that PCIFI can be an effective technique for the identification of protein complexes. The protein complexes it found can match more known protein complexes with a smaller false-alarm rate and can provide useful insights into the understanding of the functional interdependence relationships between proteins in protein complexes.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.

PubMed

Tsvetanov, Kamen A; Henson, Richard N A; Tyler, Lorraine K; Razi, Adeel; Geerligs, Linda; Ham, Timothy E; Rowe, James B

2016-03-16

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18-88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. Copyright © 2016 Tsvetanov et al.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation

PubMed Central

Henson, Richard N.A.; Tyler, Lorraine K.; Razi, Adeel; Geerligs, Linda; Ham, Timothy E.; Rowe, James B.

2016-01-01

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18–88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. PMID:26985024

Integrated inference and evaluation of host–fungi interaction networks

PubMed Central

Remmele, Christian W.; Luther, Christian H.; Balkenhol, Johannes; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus T.

2015-01-01

Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data. PMID:26300851

Does the social capital in networks of “fish and fire” scientists and managers suggest learning?

Treesearch

A. Paige Fischer; Ken Vance-Borland; Kelly M. Burnett; Susan Hummel; Janean H. Creighton; Sherri L. Johnson; Lorien Jasny

2014-01-01

Patterns of social interaction influence how knowledge is generated, communicated, and applied. Theories of social capital and organizational learning suggest that interactions within disciplinary or functional groups foster communication of knowledge, whereas interactions across groups foster generation of new knowledge. We used social network analysis to examine...

Bilingual Lexical Interactions in an Unsupervised Neural Network Model

ERIC Educational Resources Information Center

Zhao, Xiaowei; Li, Ping

2010-01-01

In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network.

PubMed

Huang, Yezhou; Li, Shao

2010-01-18

Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity. With prior-defined pathways and incorporated protein-protein interaction (PPI) data, we counted PPIs between corresponding gene sets of each pair of distinct pathways to construct a comprehensive pathway network. Then we proposed a novel concept, characteristic sub pathway network (CSPN), to realize the phenotype-specific pathway interactions. By adding gene expression data regarding a given phenotype, angiogenesis, active PPIs corresponding to stimulation of interleukin-1 (IL-1) and tumor necrosis factor alpha (TNF-alpha) on human umbilical vein endothelial cells (HUVECs) respectively were derived. Two kinds of CSPN, namely the static or the dynamic CSPN, were detected by counting active PPIs. A comprehensive pathway network containing 37 signalling pathways as nodes and 263 pathway interactions were obtained. Two phenotype-specific CSPNs for angiogenesis, corresponding to stimulation of IL-1 and TNF-alpha on HUVEC respectively, were addressed. From phenotype-specific CSPNs, a static CSPN involving interactions among B cell receptor, T cell receptor, Toll-like receptor, MAPK, VEGF, and ErbB signalling pathways, and a dynamic CSPN involving interactions among TGF-beta, Wnt, p53 signalling pathways and cell cycle pathway, were detected for angiogenesis on HUVEC after stimulation of IL-1 and TNF-alpha respectively. We inferred that, in certain case, the static CSPN maintains related basic functions of the cells, whereas the dynamic CSPN manifests the cells' plastic responses to stimulus and therefore reflects the cells' phenotypic plasticity. The comprehensive pathway network helps us realize the cooperative behaviours among pathways. Moreover, two kinds of potential CSPNs found in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function of HUVEC and its phenotypic plasticity in regard to angiogenesis.

Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network

PubMed Central

2010-01-01

Background Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity. Methods With prior-defined pathways and incorporated protein-protein interaction (PPI) data, we counted PPIs between corresponding gene sets of each pair of distinct pathways to construct a comprehensive pathway network. Then we proposed a novel concept, characteristic sub pathway network (CSPN), to realize the phenotype-specific pathway interactions. By adding gene expression data regarding a given phenotype, angiogenesis, active PPIs corresponding to stimulation of interleukin-1 (IL-1) and tumor necrosis factor α (TNF-α) on human umbilical vein endothelial cells (HUVECs) respectively were derived. Two kinds of CSPN, namely the static or the dynamic CSPN, were detected by counting active PPIs. Results A comprehensive pathway network containing 37 signalling pathways as nodes and 263 pathway interactions were obtained. Two phenotype-specific CSPNs for angiogenesis, corresponding to stimulation of IL-1 and TNF-α on HUVEC respectively, were addressed. From phenotype-specific CSPNs, a static CSPN involving interactions among B cell receptor, T cell receptor, Toll-like receptor, MAPK, VEGF, and ErbB signalling pathways, and a dynamic CSPN involving interactions among TGF-β, Wnt, p53 signalling pathways and cell cycle pathway, were detected for angiogenesis on HUVEC after stimulation of IL-1 and TNF-α respectively. We inferred that, in certain case, the static CSPN maintains related basic functions of the cells, whereas the dynamic CSPN manifests the cells' plastic responses to stimulus and therefore reflects the cells' phenotypic plasticity. Conclusion The comprehensive pathway network helps us realize the cooperative behaviours among pathways. Moreover, two kinds of potential CSPNs found in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function of HUVEC and its phenotypic plasticity in regard to angiogenesis. PMID:20122205

Development and use of the Cytoscape app GFD-Net for measuring semantic dissimilarity of gene networks

PubMed Central

Diaz-Montana, Juan J.; Diaz-Diaz, Norberto

2014-01-01

Gene networks are one of the main computational models used to study the interaction between different elements during biological processes being widely used to represent gene–gene, or protein–protein interaction complexes. We present GFD-Net, a Cytoscape app for visualizing and analyzing the functional dissimilarity of gene networks. PMID:25400907

Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production.

PubMed

Beaty, Roger E; Christensen, Alexander P; Benedek, Mathias; Silvia, Paul J; Schacter, Daniel L

2017-03-01

Functional neuroimaging research has recently revealed brain network interactions during performance on creative thinking tasks-particularly among regions of the default and executive control networks-but the cognitive mechanisms related to these interactions remain poorly understood. Here we test the hypothesis that the executive control network can interact with the default network to inhibit salient conceptual knowledge (i.e., pre-potent responses) elicited from memory during creative idea production. Participants studied common noun-verb pairs and were given a cued-recall test with corrective feedback to strengthen the paired association in memory. They then completed a verb generation task that presented either a previously studied noun (high-constraint) or an unstudied noun (low-constraint), and were asked to "think creatively" while searching for a novel verb to relate to the presented noun. Latent Semantic Analysis of verbal responses showed decreased semantic distance values in the high-constraint (i.e., interference) condition, which corresponded to increased neural activity within regions of the default (posterior cingulate cortex and bilateral angular gyri), salience (right anterior insula), and executive control (left dorsolateral prefrontal cortex) networks. Independent component analysis of intrinsic functional connectivity networks extended this finding by revealing differential interactions among these large-scale networks across the task conditions. The results suggest that interactions between the default and executive control networks underlie response inhibition during constrained idea production, providing insight into specific neurocognitive mechanisms supporting creative cognition. Copyright © 2017 Elsevier Inc. All rights reserved.

Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes.

PubMed

Timóteo, Sérgio; Correia, Marta; Rodríguez-Echeverría, Susana; Freitas, Helena; Heleno, Ruben

2018-01-10

Species interaction networks are traditionally explored as discrete entities with well-defined spatial borders, an oversimplification likely impairing their applicability. Using a multilayer network approach, explicitly accounting for inter-habitat connectivity, we investigate the spatial structure of seed-dispersal networks across the Gorongosa National Park, Mozambique. We show that the overall seed-dispersal network is composed by spatially explicit communities of dispersers spanning across habitats, functionally linking the landscape mosaic. Inter-habitat connectivity determines spatial structure, which cannot be accurately described with standard monolayer approaches either splitting or merging habitats. Multilayer modularity cannot be predicted by null models randomizing either interactions within each habitat or those linking habitats; however, as habitat connectivity increases, random processes become more important for overall structure. The importance of dispersers for the overall network structure is captured by multilayer versatility but not by standard metrics. Highly versatile species disperse many plant species across multiple habitats, being critical to landscape functional cohesion.

A new multi-scale method to reveal hierarchical modular structures in biological networks.

PubMed

Jiao, Qing-Ju; Huang, Yan; Shen, Hong-Bin

2016-11-15

Biological networks are effective tools for studying molecular interactions. Modular structure, in which genes or proteins may tend to be associated with functional modules or protein complexes, is a remarkable feature of biological networks. Mining modular structure from biological networks enables us to focus on a set of potentially important nodes, which provides a reliable guide to future biological experiments. The first fundamental challenge in mining modular structure from biological networks is that the quality of the observed network data is usually low owing to noise and incompleteness in the obtained networks. The second problem that poses a challenge to existing approaches to the mining of modular structure is that the organization of both functional modules and protein complexes in networks is far more complicated than was ever thought. For instance, the sizes of different modules vary considerably from each other and they often form multi-scale hierarchical structures. To solve these problems, we propose a new multi-scale protocol for mining modular structure (named ISIMB) driven by a node similarity metric, which works in an iteratively converged space to reduce the effects of the low data quality of the observed network data. The multi-scale node similarity metric couples both the local and the global topology of the network with a resolution regulator. By varying this resolution regulator to give different weightings to the local and global terms in the metric, the ISIMB method is able to fit the shape of modules and to detect them on different scales. Experiments on protein-protein interaction and genetic interaction networks show that our method can not only mine functional modules and protein complexes successfully, but can also predict functional modules from specific to general and reveal the hierarchical organization of protein complexes.

Brain network dysregulation, emotion, and complaints after mild traumatic brain injury.

PubMed

van der Horn, Harm J; Liemburg, Edith J; Scheenen, Myrthe E; de Koning, Myrthe E; Marsman, Jan-Bernard C; Spikman, Jacoba M; van der Naalt, Joukje

2016-04-01

To assess the role of brain networks in emotion regulation and post-traumatic complaints in the sub-acute phase after non-complicated mild traumatic brain injury (mTBI). Fifty-four patients with mTBI (34 with and 20 without complaints) and 20 healthy controls (group-matched for age, sex, education, and handedness) were included. Resting-state fMRI was performed at four weeks post-injury. Static and dynamic functional connectivity were studied within and between the default mode, executive (frontoparietal and bilateral frontal network), and salience network. The hospital anxiety and depression scale (HADS) was used to measure anxiety (HADS-A) and depression (HADS-D). Regarding within-network functional connectivity, none of the selected brain networks were different between groups. Regarding between-network interactions, patients with complaints exhibited lower functional connectivity between the bilateral frontal and salience network compared to patients without complaints. In the total patient group, higher HADS-D scores were related to lower functional connectivity between the bilateral frontal network and both the right frontoparietal and salience network, and to higher connectivity between the right frontoparietal and salience network. Furthermore, whereas higher HADS-D scores were associated with lower connectivity within the parietal midline areas of the bilateral frontal network, higher HADS-A scores were related to lower connectivity within medial prefrontal areas of the bilateral frontal network. Functional interactions of the executive and salience networks were related to emotion regulation and complaints after mTBI, with a key role for the bilateral frontal network. These findings may have implications for future studies on the effect of psychological interventions. © 2016 Wiley Periodicals, Inc.

Genetic interaction networks: better understand to better predict

PubMed Central

Boucher, Benjamin; Jenna, Sarah

2013-01-01

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG

PubMed Central

Rojas, Gonzalo M.; Fuentes, Jorge A.; Gálvez, Marcelo

2016-01-01

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10–20 EEG electrodes with Yeo’s seven functional connectivity networks. PMID:27807416

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

PubMed

Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

2009-03-11

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene-encoded proteins are attached to the core at more peripheral positions of the networks.

Aberrant functioning of the theory-of-mind network in children and adolescents with autism.

PubMed

Kana, Rajesh K; Maximo, Jose O; Williams, Diane L; Keller, Timothy A; Schipul, Sarah E; Cherkassky, Vladimir L; Minshew, Nancy J; Just, Marcel Adam

2015-01-01

Theory-of-mind (ToM), the ability to infer people's thoughts and feelings, is a pivotal skill in effective social interactions. Individuals with autism spectrum disorders (ASD) have been found to have altered ToM skills, which significantly impacts the quality of their social interactions. Neuroimaging studies have reported altered activation of the ToM cortical network, especially in adults with autism, yet little is known about the brain responses underlying ToM in younger individuals with ASD. This functional magnetic resonance imaging (fMRI) study investigated the neural mechanisms underlying ToM in high-functioning children and adolescents with ASD and matched typically developing (TD) peers. fMRI data were acquired from 13 participants with ASD and 13 TD control participants while they watched animations involving two "interacting" geometrical shapes. Participants with ASD showed significantly reduced activation, relative to TD controls, in regions considered part of the ToM network, the mirror network, and the cerebellum. Functional connectivity analyses revealed underconnectivity between frontal and posterior regions during task performance in the ASD participants. Overall, the findings of this study reveal disruptions in the brain circuitry underlying ToM in ASD at multiple levels, including decreased activation and decreased functional connectivity.

A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways

PubMed Central

Taipale, Mikko; Tucker, George; Peng, Jian; Krykbaeva, Irina; Lin, Zhen-Yuan; Larsen, Brett; Choi, Hyungwon; Berger, Bonnie; Gingras, Anne-Claude; Lindquist, Susan

2014-01-01

Chaperones are abundant cellular proteins that promote the folding and function of their substrate proteins (clients). In vivo, chaperones also associate with a large and diverse set of co-factors (co-chaperones) that regulate their specificity and function. However, how these co-chaperones regulate protein folding and whether they have chaperone-independent biological functions is largely unknown. We have combined mass spectrometry and quantitative high-throughput LUMIER assays to systematically characterize the chaperone/co-chaperone/client interaction network in human cells. We uncover hundreds of novel chaperone clients, delineate their participation in specific co-chaperone complexes, and establish a surprisingly distinct network of protein/protein interactions for co-chaperones. As a salient example of the power of such analysis, we establish that NUDC family co-chaperones specifically associate with structurally related but evolutionarily distinct β-propeller folds. We provide a framework for deciphering the proteostasis network, its regulation in development and disease, and expand the use of chaperones as sensors for drug/target engagement. PMID:25036637

Extensive site-directed mutagenesis reveals interconnected functional units in the alkaline phosphatase active site

PubMed Central

Sunden, Fanny; Peck, Ariana; Salzman, Julia; Ressl, Susanne; Herschlag, Daniel

2015-01-01

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modes of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes. DOI: http://dx.doi.org/10.7554/eLife.06181.001 PMID:25902402

Extensive site-directed mutagenesis reveals interconnected functional units in the alkaline phosphatase active site

DOE PAGES

Sunden, Fanny; Peck, Ariana; Salzman, Julia; ...

2015-04-22

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modesmore » of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes.« less

Integrating physical and genetic maps: from genomes to interaction networks

PubMed Central

Beyer, Andreas; Bandyopadhyay, Sourav; Ideker, Trey

2009-01-01

Physical and genetic mapping data have become as important to network biology as they once were to the Human Genome Project. Integrating physical and genetic networks currently faces several challenges: increasing the coverage of each type of network; establishing methods to assemble individual interaction measurements into contiguous pathway models; and annotating these pathways with detailed functional information. A particular challenge involves reconciling the wide variety of interaction types that are currently available. For this purpose, recent studies have sought to classify genetic and physical interactions along several complementary dimensions, such as ordered versus unordered, alleviating versus aggravating, and first versus second degree. PMID:17703239

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sunden, Fanny; Peck, Ariana; Salzman, Julia

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modesmore » of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes.« less

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial.

PubMed

Kulin, Merima; Fortuna, Carolina; De Poorter, Eli; Deschrijver, Dirk; Moerman, Ingrid

2016-06-01

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

PubMed Central

Kulin, Merima; Fortuna, Carolina; De Poorter, Eli; Deschrijver, Dirk; Moerman, Ingrid

2016-01-01

Data science or “data-driven research” is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves. PMID:27258286

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

PubMed

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks

PubMed Central

Li, Min; Li, Dongyan; Tang, Yu; Wang, Jianxin

2017-01-01

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster. PMID:28858211

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

AlignNemo: a local network alignment method to integrate homology and topology.

PubMed

Ciriello, Giovanni; Mina, Marco; Guzzi, Pietro H; Cannataro, Mario; Guerra, Concettina

2012-01-01

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation: Competition in bioremediation system

DOE PAGES

Deng, Ye; Zhang, Ping; Qin, Yujia; ...

2015-08-11

When trying to discern network interactions among different species/populations in microbial communities interests have been evoked in recent years, but little information is available about temporal dynamics of microbial network interactions in response to environmental perturbations. We modified the random matrix theory-based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the networkmore » interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140–269 days). Unlike previous studies in other habitats, negative interactions predominated in a time-series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. In particular, several keystone sulfate-reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations.« less

Functional brain networks related to individual differences in human intelligence at rest.

PubMed

Hearne, Luke J; Mattingley, Jason B; Cocchi, Luca

2016-08-26

Intelligence is a fundamental ability that sets humans apart from other animal species. Despite its importance in defining human behaviour, the neural networks responsible for intelligence are not well understood. The dominant view from neuroimaging work suggests that intelligent performance on a range of tasks is underpinned by segregated interactions in a fronto-parietal network of brain regions. Here we asked whether fronto-parietal interactions associated with intelligence are ubiquitous, or emerge from more widespread associations in a task-free context. First we undertook an exploratory mapping of the existing literature on functional connectivity associated with intelligence. Next, to empirically test hypotheses derived from the exploratory mapping, we performed network analyses in a cohort of 317 unrelated participants from the Human Connectome Project. Our results revealed a novel contribution of across-network interactions between default-mode and fronto-parietal networks to individual differences in intelligence at rest. Specifically, we found that greater connectivity in the resting state was associated with higher intelligence scores. Our findings highlight the need to broaden the dominant fronto-parietal conceptualisation of intelligence to encompass more complex and context-specific network dynamics.

Bacterial molecular networks: bridging the gap between functional genomics and dynamical modelling.

PubMed

van Helden, Jacques; Toussaint, Ariane; Thieffry, Denis

2012-01-01

This introductory review synthesizes the contents of the volume Bacterial Molecular Networks of the series Methods in Molecular Biology. This volume gathers 9 reviews and 16 method chapters describing computational protocols for the analysis of metabolic pathways, protein interaction networks, and regulatory networks. Each protocol is documented by concrete case studies dedicated to model bacteria or interacting populations. Altogether, the chapters provide a representative overview of state-of-the-art methods for data integration and retrieval, network visualization, graph analysis, and dynamical modelling.

Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules

PubMed Central

Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel; Castellani, Gastone; Milanesi, Luciano

2016-01-01

A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD. PMID:27731320

From structure to function, via dynamics

NASA Astrophysics Data System (ADS)

Stetter, O.; Soriano, J.; Geisel, T.; Battaglia, D.

2013-01-01

Neurons in the brain are wired into a synaptic network that spans multiple scales, from local circuits within cortical columns to fiber tracts interconnecting distant areas. However, brain function require the dynamic control of inter-circuit interactions on time-scales faster than synaptic changes. In particular, strength and direction of causal influences between neural populations (described by the so-called directed functional connectivity) must be reconfigurable even when the underlying structural connectivity is fixed. Such directed functional influences can be quantified resorting to causal analysis of time-series based on tools like Granger Causality or Transfer Entropy. The ability to quickly reorganize inter-areal interactions is a chief requirement for performance in a changing natural environment. But how can manifold functional networks stem "on demand" from an essentially fixed structure? We explore the hypothesis that the self-organization of neuronal synchronous activity underlies the control of brain functional connectivity. Based on simulated and real recordings of critical neuronal cultures in vitro, as well as on mean-field and spiking network models of interacting brain areas, we have found that "function follows dynamics", rather than structure. Different dynamic states of a same structural network, characterized by different synchronization properties, are indeed associated to different functional digraphs (functional multiplicity). We also highlight the crucial role of dynamics in establishing a structure-to-function link, by showing that whenever different structural topologies lead to similar dynamical states, than the associated functional connectivities are also very similar (structural degeneracy).

Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks.

PubMed

Zhang, Qiushi; Zhang, Gaoyan; Yao, Li; Zhao, Xiaojie

2015-01-01

Working memory (WM) refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual's cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex (PFC) activation using real-time functional magnetic resonance imaging (rtfMRI), which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN), the default mode network (DMN) and the salience network (SN), and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis (ICA) and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

Towards a comprehensive understanding of emerging dynamics and function of pancreatic islets: A complex network approach. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Loppini, Alessandro

2018-03-01

Complex network theory represents a comprehensive mathematical framework to investigate biological systems, ranging from sub-cellular and cellular scales up to large-scale networks describing species interactions and ecological systems. In their exhaustive and comprehensive work [1], Gosak et al. discuss several scenarios in which the network approach was able to uncover general properties and underlying mechanisms of cells organization and regulation, tissue functions and cell/tissue failure in pathology, by the study of chemical reaction networks, structural networks and functional connectivities.

Connexin-Dependent Neuroglial Networking as a New Therapeutic Target.

PubMed

Charvériat, Mathieu; Naus, Christian C; Leybaert, Luc; Sáez, Juan C; Giaume, Christian

2017-01-01

Astrocytes and neurons dynamically interact during physiological processes, and it is now widely accepted that they are both organized in plastic and tightly regulated networks. Astrocytes are connected through connexin-based gap junction channels, with brain region specificities, and those networks modulate neuronal activities, such as those involved in sleep-wake cycle, cognitive, or sensory functions. Additionally, astrocyte domains have been involved in neurogenesis and neuronal differentiation during development; they participate in the "tripartite synapse" with both pre-synaptic and post-synaptic neurons by tuning down or up neuronal activities through the control of neuronal synaptic strength. Connexin-based hemichannels are also involved in those regulations of neuronal activities, however, this feature will not be considered in the present review. Furthermore, neuronal processes, transmitting electrical signals to chemical synapses, stringently control astroglial connexin expression, and channel functions. Long-range energy trafficking toward neurons through connexin-coupled astrocytes and plasticity of those networks are hence largely dependent on neuronal activity. Such reciprocal interactions between neurons and astrocyte networks involve neurotransmitters, cytokines, endogenous lipids, and peptides released by neurons but also other brain cell types, including microglial and endothelial cells. Over the past 10 years, knowledge about neuroglial interactions has widened and now includes effects of CNS-targeting drugs such as antidepressants, antipsychotics, psychostimulants, or sedatives drugs as potential modulators of connexin function and thus astrocyte networking activity. In physiological situations, neuroglial networking is consequently resulting from a two-way interaction between astrocyte gap junction-mediated networks and those made by neurons. As both cell types are modulated by CNS drugs we postulate that neuroglial networking may emerge as new therapeutic targets in neurological and psychiatric disorders.

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases.

PubMed

Moulos, Panagiotis; Klein, Julie; Jupp, Simon; Stevens, Robert; Bascands, Jean-Loup; Schanstra, Joost P

2013-07-24

Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

PubMed Central

2013-01-01

Background Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. Results In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. Conclusions The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner. PMID:23883183

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies

PubMed Central

2013-01-01

Background The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the “correlation” of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. Results Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method’s performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. Conclusions The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network. PMID:23432934

Functional Alignment of Metabolic Networks.

PubMed

Mazza, Arnon; Wagner, Allon; Ruppin, Eytan; Sharan, Roded

2016-05-01

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Increased Global Interaction Across Functional Brain Modules During Cognitive Emotion Regulation.

PubMed

Brandl, Felix; Mulej Bratec, Satja; Xie, Xiyao; Wohlschläger, Afra M; Riedl, Valentin; Meng, Chun; Sorg, Christian

2017-07-13

Cognitive emotion regulation (CER) enables humans to flexibly modulate their emotions. While local theories of CER neurobiology suggest interactions between specialized local brain circuits underlying CER, e.g., in subparts of amygdala and medial prefrontal cortices (mPFC), global theories hypothesize global interaction increases among larger functional brain modules comprising local circuits. We tested the global CER hypothesis using graph-based whole-brain network analysis of functional MRI data during aversive emotional processing with and without CER. During CER, global between-module interaction across stable functional network modules increased. Global interaction increase was particularly driven by subregions of amygdala and cuneus-nodes of highest nodal participation-that overlapped with CER-specific local activations, and by mPFC and posterior cingulate as relevant connector hubs. Results provide evidence for the global nature of human CER, complementing functional specialization of embedded local brain circuits during successful CER. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Differential Occurrence of Interactions and Interaction Domains in Proteins Containing Homopolymeric Amino Acid Repeats

PubMed Central

Pelassa, Ilaria; Fiumara, Ferdinando

2015-01-01

Homopolymeric amino acids repeats (AARs), which are widespread in proteomes, have often been viewed simply as spacers between protein domains, or even as “junk” sequences with no obvious function but with a potential to cause harm upon expansion as in genetic diseases associated with polyglutamine or polyalanine expansions, including Huntington disease and cleidocranial dysplasia. A growing body of evidence indicates however that at least some AARs can form organized, functional protein structures, and can regulate protein function. In particular, certain AARs can mediate protein-protein interactions, either through homotypic AAR-AAR contacts or through heterotypic contacts with other protein domains. It is still unclear however, whether AARs may have a generalized, proteome-wide role in shaping protein-protein interaction networks. Therefore, we have undertaken here a bioinformatics screening of the human proteome and interactome in search of quantitative evidence of such a role. We first identified the sets of proteins that contain repeats of any one of the 20 amino acids, as well as control sets of proteins chosen at random in the proteome. We then analyzed the connectivity between the proteins of the AAR-containing protein sets and we compared it with that observed in the corresponding control networks. We find evidence for different degrees of connectivity in the different AAR-containing protein networks. Indeed, networks of proteins containing polyglutamine, polyglutamate, polyproline, and other AARs show significantly increased levels of connectivity, whereas networks containing polyleucine and other hydrophobic repeats show lower degrees of connectivity. Furthermore, we observed that numerous protein-protein, -nucleic acid, and -lipid interaction domains are significantly enriched in specific AAR protein groups. These findings support the notion of a generalized, combinatorial role of AARs, together with conventional protein interaction domains, in shaping the interaction networks of the human proteome, and define proteome-wide knowledge that may guide the informed biological exploration of the role of AARs in protein interactions. PMID:26734058

miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

PubMed Central

Fan, Yannan; Siklenka, Keith; Arora, Simran K.; Ribeiro, Paula; Kimmins, Sarah; Xia, Jianguo

2016-01-01

MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc. These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca. PMID:27105848

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

NASA Astrophysics Data System (ADS)

Makhtar, Siti Noormiza; Senik, Mohd Harizal

2018-02-01

The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

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

PubMed

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.

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

Protein-protein interaction networks (PPI) and complex diseases

PubMed Central

Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

2014-01-01

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

Disrupted sensorimotor and social–cognitive networks underlie symptoms in childhood-onset schizophrenia

PubMed Central

Gotts, Stephen J.; McAdams, Harrison M.; Greenstein, Dede; Lalonde, Francois; Clasen, Liv; Watsky, Rebecca E.; Shora, Lorie; Ordonez, Anna E.; Raznahan, Armin; Martin, Alex; Gogtay, Nitin; Rapoport, Judith

2016-01-01

Abstract See Lancaster and Hall (doi: 10.1093/awv330 ) for a scientific commentary on this article . Schizophrenia is increasingly recognized as a neurodevelopmental disorder with altered connectivity among brain networks. In the current study we examined large-scale network interactions in childhood-onset schizophrenia, a severe form of the disease with salient genetic and neurobiological abnormalities. Using a data-driven analysis of resting-state functional magnetic resonance imaging fluctuations, we characterized data from 19 patients with schizophrenia and 26 typically developing controls, group matched for age, sex, handedness, and magnitude of head motion during scanning. This approach identified 26 regions with decreased functional correlations in schizophrenia compared to controls. These regions were found to organize into two function-related networks, the first with regions associated with social and higher-level cognitive processing, and the second with regions involved in somatosensory and motor processing. Analyses of across- and within-network regional interactions revealed pronounced across-network decreases in functional connectivity in the schizophrenia group, as well as a set of across-network relationships with overall negative coupling indicating competitive or opponent network dynamics. Critically, across-network decreases in functional connectivity in schizophrenia predicted the severity of positive symptoms in the disorder, such as hallucinations and delusions. By contrast, decreases in functional connectivity within the social-cognitive network of regions predicted the severity of negative symptoms, such as impoverished speech and flattened affect. These results point toward the role that abnormal integration of sensorimotor and social-cognitive processing may play in the pathophysiology and symptomatology of schizophrenia. PMID:26493637

Aligning Biomolecular Networks Using Modular Graph Kernels

NASA Astrophysics Data System (ADS)

Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant

Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.

Ant-Plant Interaction in a Tropical Savanna: May the Network Structure Vary over Time and Influence on the Outcomes of Associations?

PubMed Central

Lange, Denise; Del-Claro, Kleber

2014-01-01

Plant-animal interactions occur in a community context of dynamic and complex ecological interactive networks. The understanding of who interacts with whom is a basic information, but the outcomes of interactions among associates are fundamental to draw valid conclusions about the functional structure of the network. Ecological networks studies in general gave little importance to know the true outcomes of interactions and how they may change over time. We evaluate the dynamic of an interaction network between ants and plants with extrafloral nectaries, by verifying the temporal variation in structure and outcomes of mutualism for the plant community (leaf herbivory). To reach this goal, we used two tools: bipartite network analysis and experimental manipulation. The networks exhibited the same general pattern as other mutualistic networks: nestedness, asymmetry and low specialization and this pattern was maintained over time, but with internal changes (species degree, connectance and ant abundance). These changes influenced the protection effectiveness of plants by ants, which varied over time. Our study shows that interaction networks between ants and plants are dynamic over time, and that these alterations affect the outcomes of mutualisms. In addition, our study proposes that the set of single systems that shape ecological networks can be manipulated for a greater understanding of the entire system. PMID:25141007

Major component analysis of dynamic networks of physiologic organ interactions

NASA Astrophysics Data System (ADS)

Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

2015-09-01

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family

PubMed Central

Uhart, Marina; Flores, Gabriel; Bustos, Diego M.

2016-01-01

Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems. PMID:27195976

Metabolic networks in motion: 13C-based flux analysis

PubMed Central

Sauer, Uwe

2006-01-01

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism. PMID:17102807

Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing.

PubMed

Ghadie, Mohamed Ali; Lambourne, Luke; Vidal, Marc; Xia, Yu

2017-08-01

Alternative splicing is known to remodel protein-protein interaction networks ("interactomes"), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing.

Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing

PubMed Central

Lambourne, Luke; Vidal, Marc

2017-01-01

Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing. PMID:28846689

Learning contextual gene set interaction networks of cancer with condition specificity

PubMed Central

2013-01-01

Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations. Conclusions The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes. PMID:23418942

Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex.

PubMed

Yeo, B T Thomas; Krienen, Fenna M; Chee, Michael W L; Buckner, Randy L

2014-03-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. © 2013.

Estimates of Segregation and Overlap of Functional Connectivity Networks in the Human Cerebral Cortex

PubMed Central

Yeo, BT Thomas; Krienen, Fenna M; Chee, Michael WL; Buckner, Randy L

2014-01-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1,000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. PMID:24185018

Synchronization within, and interactions between, the default mode and dorsal attention networks in relapsing-remitting multiple sclerosis.

PubMed

Huang, Muhua; Zhou, Fuqing; Wu, Lin; Wang, Bo; Wan, Hui; Li, Fangjun; Zeng, Xianjun; Gong, Honghan

2018-01-01

The effects of the interactions between the default mode network (DMN) and the dorsal attention network (DAN), which present anticorrelated behaviors, in relapsing-remitting multiple sclerosis (RRMS) are poorly understood. This study used resting-state functional connectivity (FC) and the Granger causality test (GCT) to examine changes in the undirected and effective functional network connectivity (FNC) between the two networks during the remitting phase in RRMS patients. Thirty-three patients experiencing a clinically diagnosed remitting phase of RRMS and 33 well-matched healthy control subjects participated in this study. First, an independent component (IC) analysis was performed to preprocess the functional magnetic resonance imaging data and select resting-state networks. Then, an FNC analysis and the GCT were combined to examine the temporal correlations between the ICs of the DMN and DAN and to identify correlations with clinical markers. Compared with the healthy subjects, the RRMS patients in the remitting phase showed the following: 1) significantly decreased FC within the DAN in the postcentral gyrus and decreased FC within the DMN in several regions except the parahippocampal gyrus, where increased FC was observed; 2) a relatively stable interaction between the two anticorrelated networks as well as a driving connectivity from the DAN to DMN (IC15); and 3) significantly positive correlations between the connectivity coefficient of the right superior temporal gyrus and the Modified Fatigue Impact Scale score ( ρ = 0.379, p = 0.036). Adaptive mechanisms that maintain stable interactions might occur between the DMN and DAN during the remitting phase in RRMS patients.

Allosteric Regulation of the Hsp90 Dynamics and Stability by Client Recruiter Cochaperones: Protein Structure Network Modeling

PubMed Central

Blacklock, Kristin; Verkhivker, Gennady M.

2014-01-01

The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect molecular mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the experiments, we have determined that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network analysis of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equilibrium. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network analysis has recapitulated a broad range of structural and mutagenesis experiments, particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be determined by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins. PMID:24466147

Allosteric regulation of the Hsp90 dynamics and stability by client recruiter cochaperones: protein structure network modeling.

PubMed

Blacklock, Kristin; Verkhivker, Gennady M

2014-01-01

The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect molecular mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the experiments, we have determined that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network analysis of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equilibrium. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network analysis has recapitulated a broad range of structural and mutagenesis experiments, particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be determined by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins.

Loss of functional diversity and network modularity in introduced plant–fungal symbioses

PubMed Central

Cooper, Jerry A.; Bufford, Jennifer L.; Hulme, Philip E.; Bates, Scott T.

2017-01-01

The introduction of alien plants into a new range can result in the loss of co-evolved symbiotic organisms, such as mycorrhizal fungi, that are essential for normal plant physiological functions. Prior studies of mycorrhizal associations in alien plants have tended to focus on individual plant species on a case-by-case basis. This approach limits broad scale understanding of functional shifts and changes in interaction network structure that may occur following introduction. Here we use two extensive datasets of plant–fungal interactions derived from fungal sporocarp observations and recorded plant hosts in two island archipelago nations: New Zealand (NZ) and the United Kingdom (UK). We found that the NZ dataset shows a lower functional diversity of fungal hyphal foraging strategies in mycorrhiza of alien when compared with native trees. Across species this resulted in fungal foraging strategies associated with alien trees being much more variable in functional composition compared with native trees, which had a strikingly similar functional composition. The UK data showed no functional difference in fungal associates of alien and native plant genera. Notwithstanding this, both the NZ and UK data showed a substantial difference in interaction network structure of alien trees compared with native trees. In both cases, fungal associates of native trees showed strong modularity, while fungal associates of alien trees generally integrated into a single large module. The results suggest a lower functional diversity (in one dataset) and a simplification of network structure (in both) as a result of introduction, potentially driven by either limited symbiont co-introductions or disruption of habitat as a driver of specificity due to nursery conditions, planting, or plant edaphic-niche expansion. Recognizing these shifts in function and network structure has important implications for plant invasions and facilitation of secondary invasions via shared mutualist populations. PMID:28039116

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

PubMed

Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

2017-09-13

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

NASA Astrophysics Data System (ADS)

Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

2004-07-01

In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.

Disrupted functional connectivity of cerebellar default network areas in attention-deficit/hyperactivity disorder

PubMed Central

Kucyi, Aaron; Hove, Michael J.; Biederman, Joseph; Van Dijk, Koene R.A.; Valera, Eve M.

2015-01-01

Attention-deficit/hyperactivity disorder (ADHD) is increasingly understood as a disorder of spontaneous brain-network interactions. The default mode network (DMN), implicated in ADHD-linked behaviors including mind-wandering and attentional fluctuations, has been shown to exhibit abnormal spontaneous functional connectivity (FC) within-network and with other networks (salience, dorsal attention and frontoparietal) in ADHD. Although the cerebellum has been implicated in the pathophysiology of ADHD, it remains unknown whether cerebellar areas of the DMN (CerDMN) exhibit altered FC with cortical networks in ADHD. Here, 23 adults with ADHD and 23 age-, IQ-, and sex-matched controls underwent resting state fMRI. The mean time series of CerDMN areas was extracted, and FC with the whole brain was calculated. Whole-brain between-group differences in FC were assessed. Additionally, relationships between inattention and individual differences in FC were assessed for between-group interactions. In ADHD, CerDMN areas showed positive FC (in contrast to average FC in the negative direction in controls) with widespread regions of salience, dorsal attention and sensorimotor networks. ADHD individuals also exhibited higher FC (more positive correlation) of CerDMN areas with frontoparietal and visual network regions. Within the control group, but not in ADHD, participants with higher inattention had higher FC between CerDMN and regions in the visual and dorsal attention networks. This work provides novel evidence of impaired CerDMN coupling with cortical networks in ADHD and highlights a role of the cerebro-cerebellar interactions in cognitive function. These data provide support for the potential targeting of CerDMN areas for therapeutic interventions in ADHD. PMID:26109476

Age-Related Differences in Dynamic Interactions Among Default Mode, Frontoparietal Control, and Dorsal Attention Networks during Resting-State and Interference Resolution.

PubMed

Avelar-Pereira, Bárbara; Bäckman, Lars; Wåhlin, Anders; Nyberg, Lars; Salami, Alireza

2017-01-01

Resting-state fMRI (rs-fMRI) can identify large-scale brain networks, including the default mode (DMN), frontoparietal control (FPN) and dorsal attention (DAN) networks. Interactions among these networks are critical for supporting complex cognitive functions, yet the way in which they are modulated across states is not well understood. Moreover, it remains unclear whether these interactions are similarly affected in aging regardless of cognitive state. In this study, we investigated age-related differences in functional interactions among the DMN, FPN and DAN during rest and the Multi-Source Interference task (MSIT). Networks were identified using independent component analysis (ICA), and functional connectivity was measured during rest and task. We found that the FPN was more coupled with the DMN during rest and with the DAN during the MSIT. The degree of FPN-DMN connectivity was lower in older compared to younger adults, whereas no age-related differences were observed in FPN-DAN connectivity in either state. This suggests that dynamic interactions of the FPN are stable across cognitive states. The DMN and DAN were anti correlated and age-sensitive during the MSIT only, indicating variation in a task-dependent manner. Increased levels of anticorrelation from rest to task also predicted successful interference resolution. Additional analyses revealed that the degree of DMN-DAN anticorrelation during the MSIT was associated to resting cerebral blood flow (CBF) within the DMN. This suggests that reduced DMN neural activity during rest underlies an impaired ability to achieve higher levels of anticorrelation during a task. Taken together, our results suggest that only parts of age-related differences in connectivity are uncovered at rest and thus, should be studied in the functional connectome across multiple states for a more comprehensive picture.

Age-Related Differences in Dynamic Interactions Among Default Mode, Frontoparietal Control, and Dorsal Attention Networks during Resting-State and Interference Resolution

PubMed Central

Avelar-Pereira, Bárbara; Bäckman, Lars; Wåhlin, Anders; Nyberg, Lars; Salami, Alireza

2017-01-01

Resting-state fMRI (rs-fMRI) can identify large-scale brain networks, including the default mode (DMN), frontoparietal control (FPN) and dorsal attention (DAN) networks. Interactions among these networks are critical for supporting complex cognitive functions, yet the way in which they are modulated across states is not well understood. Moreover, it remains unclear whether these interactions are similarly affected in aging regardless of cognitive state. In this study, we investigated age-related differences in functional interactions among the DMN, FPN and DAN during rest and the Multi-Source Interference task (MSIT). Networks were identified using independent component analysis (ICA), and functional connectivity was measured during rest and task. We found that the FPN was more coupled with the DMN during rest and with the DAN during the MSIT. The degree of FPN-DMN connectivity was lower in older compared to younger adults, whereas no age-related differences were observed in FPN-DAN connectivity in either state. This suggests that dynamic interactions of the FPN are stable across cognitive states. The DMN and DAN were anti correlated and age-sensitive during the MSIT only, indicating variation in a task-dependent manner. Increased levels of anticorrelation from rest to task also predicted successful interference resolution. Additional analyses revealed that the degree of DMN-DAN anticorrelation during the MSIT was associated to resting cerebral blood flow (CBF) within the DMN. This suggests that reduced DMN neural activity during rest underlies an impaired ability to achieve higher levels of anticorrelation during a task. Taken together, our results suggest that only parts of age-related differences in connectivity are uncovered at rest and thus, should be studied in the functional connectome across multiple states for a more comprehensive picture. PMID:28588476

An ensemble framework for clustering protein-protein interaction networks.

PubMed

Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

2007-07-01

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

Consequences of plant invasions on compartmentalization and species’ roles in plant–pollinator networks

PubMed Central

Albrecht, Matthias; Padrón, Benigno; Bartomeus, Ignasi; Traveset, Anna

2014-01-01

Compartmentalization—the organization of ecological interaction networks into subsets of species that do not interact with other subsets (true compartments) or interact more frequently among themselves than with other species (modules)—has been identified as a key property for the functioning, stability and evolution of ecological communities. Invasions by entomophilous invasive plants may profoundly alter the way interaction networks are compartmentalized. We analysed a comprehensive dataset of 40 paired plant–pollinator networks (invaded versus uninvaded) to test this hypothesis. We show that invasive plants have higher generalization levels with respect to their pollinators than natives. The consequences for network topology are that—rather than displacing native species from the network—plant invaders attracting pollinators into invaded modules tend to play new important topological roles (i.e. network hubs, module hubs and connectors) and cause role shifts in native species, creating larger modules that are more connected among each other. While the number of true compartments was lower in invaded compared with uninvaded networks, the effect of invasion on modularity was contingent on the study system. Interestingly, the generalization level of the invasive plants partially explains this pattern, with more generalized invaders contributing to a lower modularity. Our findings indicate that the altered interaction structure of invaded networks makes them more robust against simulated random secondary species extinctions, but more vulnerable when the typically highly connected invasive plants go extinct first. The consequences and pathways by which biological invasions alter the interaction structure of plant–pollinator communities highlighted in this study may have important dynamical and functional implications, for example, by influencing multi-species reciprocal selection regimes and coevolutionary processes. PMID:24943368

Effects of biotic and abiotic factors on the temporal dynamic of bat-fruit interactions

NASA Astrophysics Data System (ADS)

Laurindo, Rafael de Souza; Gregorin, Renato; Tavares, Davi Castro

2017-08-01

Mutualistic interactions between animals and plants vary over time and space based on the abundance of fruits or animals and seasonality. Little is known about this temporal dynamic and the influence of biotic and abiotic factors on the structure of interaction networks. We evaluated changes in the structure of network interactions between bats and fruits in relation to variations in rainfall. Our results suggest that fruit abundance is the main variable responsible for temporal changes in network attributes, such as network size, connectance, and number of interactions. In the same way, temperature positively affected the abundance of fruits and bats. An increase in temperature and alterations in rainfall patterns, due to human induced climate change, can cause changes in phenological patterns and fruit production, with negative consequences to biodiversity maintenance, ecological interactions, and ecosystem functioning.

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

PubMed Central

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

Brain Network Interactions in Auditory, Visual and Linguistic Processing

ERIC Educational Resources Information Center

Horwitz, Barry; Braun, Allen R.

2004-01-01

In the paper, we discuss the importance of network interactions between brain regions in mediating performance of sensorimotor and cognitive tasks, including those associated with language processing. Functional neuroimaging, especially PET and fMRI, provide data that are obtained essentially simultaneously from much of the brain, and thus are…

Social Networks and Community-Based Natural Resource Management

NASA Astrophysics Data System (ADS)

Lauber, T. Bruce; Decker, Daniel J.; Knuth, Barbara A.

2008-10-01

We conducted case studies of three successful examples of collaborative, community-based natural resource conservation and development. Our purpose was to: (1) identify the functions served by interactions within the social networks of involved stakeholders; (2) describe key structural properties of these social networks; and (3) determine how these structural properties varied when the networks were serving different functions. The case studies relied on semi-structured, in-depth interviews of 8 to 11 key stakeholders at each site who had played a significant role in the collaborative projects. Interview questions focused on the roles played by key stakeholders and the functions of interactions between them. Interactions allowed the exchange of ideas, provided access to funding, and enabled some stakeholders to influence others. The exchange of ideas involved the largest number of stakeholders, the highest percentage of local stakeholders, and the highest density of interactions. Our findings demonstrated the value of tailoring strategies for involving stakeholders to meet different needs during a collaborative, community-based natural resource management project. Widespread involvement of local stakeholders may be most appropriate when ideas for a project are being developed. During efforts to exert influence to secure project approvals or funding, however, involving specific individuals with political connections or influence on possible sources of funds may be critical. Our findings are consistent with past work that has postulated that social networks may require specific characteristics to meet different needs in community-based environmental management.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

PubMed Central

Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

2016-01-01

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

Algorithm to Identify Frequent Coupled Modules from Two-Layered Network Series: Application to Study Transcription and Splicing Coupling

PubMed Central

Li, Wenyuan; Dai, Chao; Liu, Chun-Chi

2012-01-01

Abstract Current network analysis methods all focus on one or multiple networks of the same type. However, cells are organized by multi-layer networks (e.g., transcriptional regulatory networks, splicing regulatory networks, protein-protein interaction networks), which interact and influence each other. Elucidating the coupling mechanisms among those different types of networks is essential in understanding the functions and mechanisms of cellular activities. In this article, we developed the first computational method for pattern mining across many two-layered graphs, with the two layers representing different types yet coupled biological networks. We formulated the problem of identifying frequent coupled clusters between the two layers of networks into a tensor-based computation problem, and proposed an efficient solution to solve the problem. We applied the method to 38 two-layered co-transcription and co-splicing networks, derived from 38 RNA-seq datasets. With the identified atlas of coupled transcription-splicing modules, we explored to what extent, for which cellular functions, and by what mechanisms transcription-splicing coupling takes place. PMID:22697243

Intranasal oxytocin modulates neural functional connectivity during human social interaction.

PubMed

Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

2018-02-10

Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

Hidden long evolutionary memory in a model biochemical network

NASA Astrophysics Data System (ADS)

Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-04-01

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

Segregated Systems of Human Brain Networks.

PubMed

Wig, Gagan S

2017-12-01

The organization of the brain network enables its function. Evaluation of this organization has revealed that large-scale brain networks consist of multiple segregated subnetworks of interacting brain areas. Descriptions of resting-state network architecture have provided clues for understanding the functional significance of these segregated subnetworks, many of which correspond to distinct brain systems. The present report synthesizes accumulating evidence to reveal how maintaining segregated brain systems renders the human brain network functionally specialized, adaptable to task demands, and largely resilient following focal brain damage. The organizational properties that support system segregation are harmonious with the properties that promote integration across the network, but confer unique and important features to the brain network that are central to its function and behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrating In Silico Resources to Map a Signaling Network

PubMed Central

Liu, Hanqing; Beck, Tim N.; Golemis, Erica A.; Serebriiskii, Ilya G.

2013-01-01

The abundance of publicly available life science databases offer a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol to building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature. PMID:24233784

Self-organization of network dynamics into local quantized states.

PubMed

Nicolaides, Christos; Juanes, Ruben; Cueto-Felgueroso, Luis

2016-02-17

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of the Swift-Hohenberg continuum model-a minimal-ingredients model of nodal activation and interaction within a complex network-is able to produce a complex suite of localized patterns. Hence, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

PubMed

Yang, Lei; Zhao, Xudong; Tang, Xianglong

2014-01-01

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

Functional brain networks related to individual differences in human intelligence at rest

PubMed Central

Hearne, Luke J.; Mattingley, Jason B.; Cocchi, Luca

2016-01-01

Intelligence is a fundamental ability that sets humans apart from other animal species. Despite its importance in defining human behaviour, the neural networks responsible for intelligence are not well understood. The dominant view from neuroimaging work suggests that intelligent performance on a range of tasks is underpinned by segregated interactions in a fronto-parietal network of brain regions. Here we asked whether fronto-parietal interactions associated with intelligence are ubiquitous, or emerge from more widespread associations in a task-free context. First we undertook an exploratory mapping of the existing literature on functional connectivity associated with intelligence. Next, to empirically test hypotheses derived from the exploratory mapping, we performed network analyses in a cohort of 317 unrelated participants from the Human Connectome Project. Our results revealed a novel contribution of across-network interactions between default-mode and fronto-parietal networks to individual differences in intelligence at rest. Specifically, we found that greater connectivity in the resting state was associated with higher intelligence scores. Our findings highlight the need to broaden the dominant fronto-parietal conceptualisation of intelligence to encompass more complex and context-specific network dynamics. PMID:27561736

Optimality principles in the regulation of metabolic networks.

PubMed

Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas

2012-08-29

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

Structure-function clustering in multiplex brain networks

NASA Astrophysics Data System (ADS)

Crofts, J. J.; Forrester, M.; O'Dea, R. D.

2016-10-01

A key question in neuroscience is to understand how a rich functional repertoire of brain activity arises within relatively static networks of structurally connected neural populations: elucidating the subtle interactions between evoked “functional connectivity” and the underlying “structural connectivity” has the potential to address this. These structural-functional networks (and neural networks more generally) are more naturally described using a multilayer or multiplex network approach, in favour of standard single-layer network analyses that are more typically applied to such systems. In this letter, we address such issues by exploring important structure-function relations in the Macaque cortical network by modelling it as a duplex network that comprises an anatomical layer, describing the known (macro-scale) network topology of the Macaque monkey, and a functional layer derived from simulated neural activity. We investigate and characterize correlations between structural and functional layers, as system parameters controlling simulated neural activity are varied, by employing recently described multiplex network measures. Moreover, we propose a novel measure of multiplex structure-function clustering which allows us to investigate the emergence of functional connections that are distinct from the underlying cortical structure, and to highlight the dependence of multiplex structure on the neural dynamical regime.

Influences of brain development and ageing on cortical interactive networks.

PubMed

Zhu, Chengyu; Guo, Xiaoli; Jin, Zheng; Sun, Junfeng; Qiu, Yihong; Zhu, Yisheng; Tong, Shanbao

2011-02-01

To study the effect of brain development and ageing on the pattern of cortical interactive networks. By causality analysis of multichannel electroencephalograph (EEG) with partial directed coherence (PDC), we investigated the different neural networks involved in the whole cortex as well as the anterior and posterior areas in three age groups, i.e., children (0-10 years), mid-aged adults (26-38 years) and the elderly (56-80 years). By comparing the cortical interactive networks in different age groups, the following findings were concluded: (1) the cortical interactive network in the right hemisphere develops earlier than its left counterpart in the development stage; (2) the cortical interactive network of anterior cortex, especially at C3 and F3, is demonstrated to undergo far more extensive changes, compared with the posterior area during brain development and ageing; (3) the asymmetry of the cortical interactive networks declines during ageing with more loss of connectivity in the left frontal and central areas. The age-related variation of cortical interactive networks from resting EEG provides new insights into brain development and ageing. Our findings demonstrated that the PDC analysis of EEG is a powerful approach for characterizing the cortical functional connectivity during brain development and ageing. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Construction of reliable protein-protein interaction networks with a new interaction generality measure.

PubMed

Saito, Rintaro; Suzuki, Harukazu; Hayashizaki, Yoshihide

2003-04-12

Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.

Deconstructing the brain’s moral network: dissociable functionality between the temporoparietal junction and ventro-medial prefrontal cortex

PubMed Central

Mobbs, Dean; Dalgleish, Tim

2014-01-01

Research has illustrated that the brain regions implicated in moral cognition comprise a robust and broadly distributed network. However, understanding how these brain regions interact and give rise to the complex interplay of cognitive processes underpinning human moral cognition is still in its infancy. We used functional magnetic resonance imaging to examine patterns of activation for ‘difficult’ and ‘easy’ moral decisions relative to matched non-moral comparators. This revealed an activation pattern consistent with a relative functional double dissociation between the temporoparietal junction (TPJ) and ventro-medial prefrontal cortex (vmPFC). Difficult moral decisions activated bilateral TPJ and deactivated the vmPFC and OFC. In contrast, easy moral decisions revealed patterns of activation in the vmPFC and deactivation in bilateral TPJ and dorsolateral PFC. Together these results suggest that moral cognition is a dynamic process implemented by a distributed network that involves interacting, yet functionally dissociable networks. PMID:23322890

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

PubMed Central

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

Abnormal regional activity and functional connectivity in resting-state brain networks associated with etiology confirmed unilateral pulsatile tinnitus in the early stage of disease.

PubMed

Lv, Han; Zhao, Pengfei; Liu, Zhaohui; Li, Rui; Zhang, Ling; Wang, Peng; Yan, Fei; Liu, Liheng; Wang, Guopeng; Zeng, Rong; Li, Ting; Dong, Cheng; Gong, Shusheng; Wang, Zhenchang

2017-03-01

Abnormal neural activities can be revealed by resting-state functional magnetic resonance imaging (rs-fMRI) using analyses of the regional activity and functional connectivity (FC) of the networks in the brain. This study was designed to demonstrate the functional network alterations in the patients with pulsatile tinnitus (PT). In this study, we recruited 45 patients with unilateral PT in the early stage of disease (less than 48 months of disease duration) and 45 normal controls. We used regional homogeneity (ReHo) and seed-based FC computational methods to reveal resting-state brain activity features associated with pulsatile tinnitus. Compared with healthy controls, PT patients showed regional abnormalities mainly in the left middle occipital gyrus (MOG), posterior cingulate gyrus (PCC), precuneus and right anterior insula (AI). When these regions were defined as seeds, we demonstrated widespread modification of interaction between the auditory and non-auditory networks. The auditory network was positively connected with the cognitive control network (CCN), which may associate with tinnitus related distress. Both altered regional activity and changed FC were found in the visual network. The modification of interactions of higher order networks were mainly found in the DMN, CCN and limbic networks. Functional connectivity between the left MOG and left parahippocampal gyrus could also be an index to reflect the disease duration. This study helped us gain a better understanding of the characteristics of neural network modifications in patients with pulsatile tinnitus. Copyright © 2017 Elsevier B.V. All rights reserved.

Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis

PubMed Central

Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

2015-01-01

It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant–wild-type and 16 matched SNP—wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation. PMID:26170328

A protein domain-based interactome network for C. elegans early embryogenesis

PubMed Central

Boxem, Mike; Maliga, Zoltan; Klitgord, Niels; Li, Na; Lemmens, Irma; Mana, Miyeko; de Lichtervelde, Lorenzo; Mul, Joram D.; van de Peut, Diederik; Devos, Maxime; Simonis, Nicolas; Yildirim, Muhammed A.; Cokol, Murat; Kao, Huey-Ling; de Smet, Anne-Sophie; Wang, Haidong; Schlaitz, Anne-Lore; Hao, Tong; Milstein, Stuart; Fan, Changyu; Tipsword, Mike; Drew, Kevin; Galli, Matilde; Rhrissorrakrai, Kahn; Drechsel, David; Koller, Daphne; Roth, Frederick P.; Iakoucheva, Lilia M.; Dunker, A. Keith; Bonneau, Richard; Gunsalus, Kristin C.; Hill, David E.; Piano, Fabio; Tavernier, Jan; van den Heuvel, Sander; Hyman, Anthony A.; Vidal, Marc

2008-01-01

Summary Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or “interactome” networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed new insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms. PMID:18692475

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii.

PubMed

Anitha, P; Anbarasu, Anand; Ramaiah, Sudha

2014-05-01

Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, Penicillin-Binding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

PubMed Central

Franke, Lude; Bakel, Harm van; Fokkens, Like; de Jong, Edwin D.; Egmont-Petersen, Michael; Wijmenga, Cisca

2006-01-01

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown. PMID:16685651

Complexity of generic biochemical circuits: topology versus strength of interactions.

PubMed

Tikhonov, Mikhail; Bialek, William

2016-12-06

The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.

[Identification and analysis of the proteins interacted with Prestin in cochlear outer hair cells of guinea pig].

PubMed

Luo, X; Wang, J Y; Zhang, F L; Xia, Y

2018-01-07

Objective: To explore the regulation and mechanism of Prestin protein by identifying the proteins interacted with Prestin in cochlear outer hair cell(OHC) and analyzing their biological function. Methods: Co-immunoprecipitation combined mass spectrometry technology was used to isolate and identify the proteins interacted with Prestin protein of OHC, bioinformatics was used to construct Prestin protein interaction network. The proteins interacted with Prestin in OHC of guinea pig were determined by matching primary interaction mass spectrometry with protein interaction network, and annotated their functions. Results: The results of co-immunoprecipitation combined with mass spectrometry showed that 116 kinds of credible proteins could interact with Prestin. By constructing Prestin protein interaction network, matching the results of mass spectrometry and analyzing of sub-cellular localization, eight kinds of proteins were confirmed that they interacted with Prestin directly, namely EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1, and ERH, respectively, which were mainly involved in the synthesis and transportation, transmembrane folding and localization, structural stability and signal transduction of Prestin protein. Conclusion: EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1 and ERH provide molecular basis for sensory amplification function of OHCs by participating in biotransformation, transmembrane folding and localization, signal transduction and other biological processes of Prestin protein.

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

PubMed Central

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S.; Theis, Fabian J.

2015-01-01

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic. PMID:26694379

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.

PubMed

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S; Theis, Fabian J

2015-12-18

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method "miRlastic", which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic.

Probing the cross-effect of strains in non-linear elasticity of nearly regular polymer networks by pure shear deformation.

PubMed

Katashima, Takuya; Urayama, Kenji; Chung, Ung-il; Sakai, Takamasa

2015-05-07

The pure shear deformation of the Tetra-polyethylene glycol gels reveals the presence of an explicit cross-effect of strains in the strain energy density function even for the polymer networks with nearly regular structure including no appreciable amount of structural defect such as trapped entanglement. This result is in contrast to the expectation of the classical Gaussian network model (Neo Hookean model), i.e., the vanishing of the cross effect in regular networks with no trapped entanglement. The results show that (1) the cross effect of strains is not dependent on the network-strand length; (2) the cross effect is not affected by the presence of non-network strands; (3) the cross effect is proportional to the network polymer concentration including both elastically effective and ineffective strands; (4) no cross effect is expected exclusively in zero limit of network concentration in real polymer networks. These features indicate that the real polymer networks with regular network structures have an explicit cross-effect of strains, which originates from some interaction between network strands (other than entanglement effect) such as nematic interaction, topological interaction, and excluded volume interaction.

Hippocampal functional connectivity and episodic memory in early childhood

PubMed Central

Riggins, Tracy; Geng, Fengji; Blankenship, Sarah L.; Redcay, Elizabeth

2016-01-01

Episodic memory relies on a distributed network of brain regions, with the hippocampus playing a critical and irreplaceable role. Few studies have examined how changes in this network contribute to episodic memory development early in life. The present addressed this gap by examining relations between hippocampal functional connectivity and episodic memory in 4-and 6-year-old children (n=40). Results revealed similar hippocampal functional connectivity between age groups, which included lateral temporal regions, precuneus, and multiple parietal and prefrontal regions, and functional specialization along the longitudinal axis. Despite these similarities, developmental differences were also observed. Specifically, 3 (of 4) regions within the hippocampal memory network were positively associated with episodic memory in 6-year-old children, but negatively associated with episodic memory in 4-year-old children. In contrast, all 3 regions outside the hippocampal memory network were negatively associated with episodic memory in older children, but positively associated with episodic memory in younger children. These interactions are interpreted within an interactive specialization framework and suggest the hippocampus becomes functionally integrated with cortical regions that are part of the hippocampal memory network in adults and functionally segregated from regions unrelated to memory in adults, both of which are associated with age-related improvements in episodic memory ability. PMID:26900967

Hippocampal functional connectivity and episodic memory in early childhood.

PubMed

Riggins, Tracy; Geng, Fengji; Blankenship, Sarah L; Redcay, Elizabeth

2016-06-01

Episodic memory relies on a distributed network of brain regions, with the hippocampus playing a critical and irreplaceable role. Few studies have examined how changes in this network contribute to episodic memory development early in life. The present addressed this gap by examining relations between hippocampal functional connectivity and episodic memory in 4- and 6-year-old children (n=40). Results revealed similar hippocampal functional connectivity between age groups, which included lateral temporal regions, precuneus, and multiple parietal and prefrontal regions, and functional specialization along the longitudinal axis. Despite these similarities, developmental differences were also observed. Specifically, 3 (of 4) regions within the hippocampal memory network were positively associated with episodic memory in 6-year-old children, but negatively associated with episodic memory in 4-year-old children. In contrast, all 3 regions outside the hippocampal memory network were negatively associated with episodic memory in older children, but positively associated with episodic memory in younger children. These interactions are interpreted within an interactive specialization framework and suggest the hippocampus becomes functionally integrated with cortical regions that are part of the hippocampal memory network in adults and functionally segregated from regions unrelated to memory in adults, both of which are associated with age-related improvements in episodic memory ability. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Effect of tumor resection on the characteristics of functional brain networks.

PubMed

Wang, H; Douw, L; Hernández, J M; Reijneveld, J C; Stam, C J; Van Mieghem, P

2010-08-01

Brain functioning such as cognitive performance depends on the functional interactions between brain areas, namely, the functional brain networks. The functional brain networks of a group of patients with brain tumors are measured before and after tumor resection. In this work, we perform a weighted network analysis to understand the effect of neurosurgery on the characteristics of functional brain networks. Statistically significant changes in network features have been discovered in the beta (13-30 Hz) band after neurosurgery: the link weight correlation around nodes and within triangles increases which implies improvement in local efficiency of information transfer and robustness; the clustering of high link weights in a subgraph becomes stronger, which enhances the global transport capability; and the decrease in the synchronization or virus spreading threshold, revealed by the increase in the largest eigenvalue of the adjacency matrix, which suggests again the improvement of information dissemination.

Correspondence of the brain's functional architecture during activation and rest.

PubMed

Smith, Stephen M; Fox, Peter T; Miller, Karla L; Glahn, David C; Fox, P Mickle; Mackay, Clare E; Filippini, Nicola; Watkins, Kate E; Toro, Roberto; Laird, Angela R; Beckmann, Christian F

2009-08-04

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Insights into the fold organization of TIM barrel from interaction energy based structure networks.

PubMed

Vijayabaskar, M S; Vishveshwara, Saraswathi

2012-01-01

There are many well-known examples of proteins with low sequence similarity, adopting the same structural fold. This aspect of sequence-structure relationship has been extensively studied both experimentally and theoretically, however with limited success. Most of the studies consider remote homology or "sequence conservation" as the basis for their understanding. Recently "interaction energy" based network formalism (Protein Energy Networks (PENs)) was developed to understand the determinants of protein structures. In this paper we have used these PENs to investigate the common non-covalent interactions and their collective features which stabilize the TIM barrel fold. We have also developed a method of aligning PENs in order to understand the spatial conservation of interactions in the fold. We have identified key common interactions responsible for the conservation of the TIM fold, despite high sequence dissimilarity. For instance, the central beta barrel of the TIM fold is stabilized by long-range high energy electrostatic interactions and low-energy contiguous vdW interactions in certain families. The other interfaces like the helix-sheet or the helix-helix seem to be devoid of any high energy conserved interactions. Conserved interactions in the loop regions around the catalytic site of the TIM fold have also been identified, pointing out their significance in both structural and functional evolution. Based on these investigations, we have developed a novel network based phylogenetic analysis for remote homologues, which can perform better than sequence based phylogeny. Such an analysis is more meaningful from both structural and functional evolutionary perspective. We believe that the information obtained through the "interaction conservation" viewpoint and the subsequently developed method of structure network alignment, can shed new light in the fields of fold organization and de novo computational protein design.

Specificity of V1-V2 Orientation Networks in the Primate Visual Cortex

PubMed Central

Roe, Anna W.; Ts'o, Daniel Y.

2015-01-01

The computation of texture and shape involves integration of features of various orientations. Orientation networks within V1 tend to involve cells which share similar orientation selectivity. However, emergent properties in V2 require the integration of multiple orientations. We now show that, unlike interactions within V1, V1-V2 orientation interactions are much less synchronized and are not necessarily orientation dependent. We find V1-V2 orientation networks are of two types: a more tightly synchronized, orientation-preserving network and a less synchronized orientation-diverse network. We suggest that such diversity of V1-V2 interactions underlies the spatial and functional integration required for computation of higher order contour and shape in V2. PMID:26314798

Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis

PubMed Central

2018-01-01

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 allows cells to tune where endocytosis occurs, providing sensitive control over cargo uptake via clathrin-coated vesicles. PMID:29518071

Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

PubMed

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 allows cells to tune where endocytosis occurs, providing sensitive control over cargo uptake via clathrin-coated vesicles.

Advanced functional network analysis in the geosciences: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen

2013-04-01

Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.

Framework based on communicability and flow to analyze complex network dynamics

NASA Astrophysics Data System (ADS)

Gilson, M.; Kouvaris, N. E.; Deco, G.; Zamora-López, G.

2018-05-01

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

Abnormal Functional Activation and Connectivity in the Working Memory Network in Early-Onset Schizophrenia

ERIC Educational Resources Information Center

Kyriakopoulos, Marinos; Dima, Danai; Roiser, Jonathan P.; Corrigall, Richard; Barker, Gareth J.; Frangou, Sophia

2012-01-01

Objective: Disruption within the working memory (WM) neural network is considered an integral feature of schizophrenia. The WM network, and the dorsolateral prefrontal cortex (DLPFC) in particular, undergo significant remodeling in late adolescence. Potential interactions between developmental changes in the WM network and disease-related…

Self-organization of network dynamics into local quantized states

DOE PAGES

Nicolaides, Christos; Juanes, Ruben; Cueto-Felgueroso, Luis

2016-02-17

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of themore » Swift-Hohenberg continuum model—a minimal-ingredients model of nodal activation and interaction within a complex network—is able to produce a complex suite of localized patterns. Thus, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.« less

Self-organization of network dynamics into local quantized states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nicolaides, Christos; Juanes, Ruben; Cueto-Felgueroso, Luis

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of themore » Swift-Hohenberg continuum model—a minimal-ingredients model of nodal activation and interaction within a complex network—is able to produce a complex suite of localized patterns. Thus, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.« less

cisPath: an R/Bioconductor package for cloud users for visualization and management of functional protein interaction networks.

PubMed

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.

cisPath: an R/Bioconductor package for cloud users for visualization and management of functional protein interaction networks

PubMed Central

2015-01-01

Background 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. Results 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. Conclusions 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. PMID:25708840

Disrupted functional connectivity of cerebellar default network areas in attention-deficit/hyperactivity disorder.

PubMed

Kucyi, Aaron; Hove, Michael J; Biederman, Joseph; Van Dijk, Koene R A; Valera, Eve M

2015-09-01

Attention-deficit/hyperactivity disorder (ADHD) is increasingly understood as a disorder of spontaneous brain-network interactions. The default mode network (DMN), implicated in ADHD-linked behaviors including mind-wandering and attentional fluctuations, has been shown to exhibit abnormal spontaneous functional connectivity (FC) within-network and with other networks (salience, dorsal attention and frontoparietal) in ADHD. Although the cerebellum has been implicated in the pathophysiology of ADHD, it remains unknown whether cerebellar areas of the DMN (CerDMN) exhibit altered FC with cortical networks in ADHD. Here, 23 adults with ADHD and 23 age-, IQ-, and sex-matched controls underwent resting state fMRI. The mean time series of CerDMN areas was extracted, and FC with the whole brain was calculated. Whole-brain between-group differences in FC were assessed. Additionally, relationships between inattention and individual differences in FC were assessed for between-group interactions. In ADHD, CerDMN areas showed positive FC (in contrast to average FC in the negative direction in controls) with widespread regions of salience, dorsal attention and sensorimotor networks. ADHD individuals also exhibited higher FC (more positive correlation) of CerDMN areas with frontoparietal and visual network regions. Within the control group, but not in ADHD, participants with higher inattention had higher FC between CerDMN and regions in the visual and dorsal attention networks. This work provides novel evidence of impaired CerDMN coupling with cortical networks in ADHD and highlights a role of cerebro-cerebellar interactions in cognitive function. These data provide support for the potential targeting of CerDMN areas for therapeutic interventions in ADHD. © 2015 Wiley Periodicals, Inc.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences.

PubMed

Raz, Gal; Touroutoglou, Alexandra; Wilson-Mendenhall, Christine; Gilam, Gadi; Lin, Tamar; Gonen, Tal; Jacob, Yael; Atzil, Shir; Admon, Roee; Bleich-Cohen, Maya; Maron-Katz, Adi; Hendler, Talma; Barrett, Lisa Feldman

2016-08-01

Recent theoretical and empirical work has highlighted the role of domain-general, large-scale brain networks in generating emotional experiences. These networks are hypothesized to process aspects of emotional experiences that are not unique to a specific emotional category (e.g., "sadness," "happiness"), but rather that generalize across categories. In this article, we examined the dynamic interactions (i.e., changing cohesiveness) between specific domain-general networks across time while participants experienced various instances of sadness, fear, and anger. We used a novel method for probing the network connectivity dynamics between two salience networks and three amygdala-based networks. We hypothesized, and found, that the functional connectivity between these networks covaried with the intensity of different emotional experiences. Stronger connectivity between the dorsal salience network and the medial amygdala network was associated with more intense ratings of emotional experience across six different instances of the three emotion categories examined. Also, stronger connectivity between the dorsal salience network and the ventrolateral amygdala network was associated with more intense ratings of emotional experience across five out of the six different instances. Our findings demonstrate that a variety of emotional experiences are associated with dynamic interactions of domain-general neural systems.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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.

BIND: the Biomolecular Interaction Network Database

PubMed Central

Bader, Gary D.; Betel, Doron; Hogue, Christopher W. V.

2003-01-01

The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD. PMID:12519993

From brain topography to brain topology: relevance of graph theory to functional neuroscience.

PubMed

Minati, Ludovico; Varotto, Giulia; D'Incerti, Ludovico; Panzica, Ferruccio; Chan, Dennis

2013-07-10

Although several brain regions show significant specialization, higher functions such as cross-modal information integration, abstract reasoning and conscious awareness are viewed as emerging from interactions across distributed functional networks. Analytical approaches capable of capturing the properties of such networks can therefore enhance our ability to make inferences from functional MRI, electroencephalography and magnetoencephalography data. Graph theory is a branch of mathematics that focuses on the formal modelling of networks and offers a wide range of theoretical tools to quantify specific features of network architecture (topology) that can provide information complementing the anatomical localization of areas responding to given stimuli or tasks (topography). Explicit modelling of the architecture of axonal connections and interactions among areas can furthermore reveal peculiar topological properties that are conserved across diverse biological networks, and highly sensitive to disease states. The field is evolving rapidly, partly fuelled by computational developments that enable the study of connectivity at fine anatomical detail and the simultaneous interactions among multiple regions. Recent publications in this area have shown that graph-based modelling can enhance our ability to draw causal inferences from functional MRI experiments, and support the early detection of disconnection and the modelling of pathology spread in neurodegenerative disease, particularly Alzheimer's disease. Furthermore, neurophysiological studies have shown that network topology has a profound link to epileptogenesis and that connectivity indices derived from graph models aid in modelling the onset and spread of seizures. Graph-based analyses may therefore significantly help understand the bases of a range of neurological conditions. This review is designed to provide an overview of graph-based analyses of brain connectivity and their relevance to disease aimed principally at general neuroscientists and clinicians.

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

PubMed Central

Xia, Kai; Dong, Dong; Han, Jing-Dong J

2006-01-01

Background Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. Results By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. Conclusion IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer. PMID:17112386

The plastid and mitochondrial peptidase network in Arabidopsis thaliana: a foundation for testing genetic interactions and functions in organellar proteostasis

USDA-ARS?s Scientific Manuscript database

Plant plastids and mitochondria have dynamic proteomes. To maintain their protein homeostasis, a proteostasis network containing protein chaperones, peptidases and their substrate recognition factors exists, but many peptidases, their functional connections and substrates are poorly characterized. T...

Dynamic Network-Based Relevance Score Reveals Essential Proteins and Functional Modules in Directed Differentiation

PubMed Central

Wu, Chia-Chou; Lin, Che

2015-01-01

The induction of stem cells toward a desired differentiation direction is required for the advancement of stem cell-based therapies. Despite successful demonstrations of the control of differentiation direction, the effective use of stem cell-based therapies suffers from a lack of systematic knowledge regarding the mechanisms underlying directed differentiation. Using dynamic modeling and the temporal microarray data of three differentiation stages, three dynamic protein-protein interaction networks were constructed. The interaction difference networks derived from the constructed networks systematically delineated the evolution of interaction variations and the underlying mechanisms. A proposed relevance score identified the essential components in the directed differentiation. Inspection of well-known proteins and functional modules in the directed differentiation showed the plausibility of the proposed relevance score, with the higher scores of several proteins and function modules indicating their essential roles in the directed differentiation. During the differentiation process, the proteins and functional modules with higher relevance scores also became more specific to the neuronal identity. Ultimately, the essential components revealed by the relevance scores may play a role in controlling the direction of differentiation. In addition, these components may serve as a starting point for understanding the systematic mechanisms of directed differentiation and for increasing the efficiency of stem cell-based therapies. PMID:25977693

Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment.

PubMed

Chand, Ganesh B; Wu, Junjie; Hajjar, Ihab; Qiu, Deqiang

2017-09-01

Previous functional magnetic resonance imaging (fMRI) investigations suggest that the intrinsically organized large-scale networks and the interaction between them might be crucial for cognitive activities. A triple network model, which consists of the default-mode network, salience network, and central-executive network, has been recently used to understand the connectivity patterns of the cognitively normal brains versus the brains with disorders. This model suggests that the salience network dynamically controls the default-mode and central-executive networks in healthy young individuals. However, the patterns of interactions have remained largely unknown in healthy aging or those with cognitive decline. In this study, we assess the patterns of interactions between the three networks using dynamical causal modeling in resting state fMRI data and compare them between subjects with normal cognition and mild cognitive impairment (MCI). In healthy elderly subjects, our analysis showed that the salience network, especially its dorsal subnetwork, modulates the interaction between the default-mode network and the central-executive network (Mann-Whitney U test; p < 0.05), which was consistent with the pattern of interaction reported in young adults. In contrast, this pattern of modulation by salience network was disrupted in MCI (p < 0.05). Furthermore, the degree of disruption in salience network control correlated significantly with lower overall cognitive performance measured by Montreal Cognitive Assessment (r = 0.295; p < 0.05). This study suggests that a disruption of the salience network control, especially the dorsal salience network, over other networks provides a neuronal basis for cognitive decline and may be a candidate neuroimaging biomarker of cognitive impairment.

Construction of ontology augmented networks for protein complex prediction.

PubMed

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.

The effect of individual variation on the structure and function of interaction networks in harvester ants

PubMed Central

Pinter-Wollman, Noa; Wollman, Roy; Guetz, Adam; Holmes, Susan; Gordon, Deborah M.

2011-01-01

Social insects exhibit coordinated behaviour without central control. Local interactions among individuals determine their behaviour and regulate the activity of the colony. Harvester ants are recruited for outside work, using networks of brief antennal contacts, in the nest chamber closest to the nest exit: the entrance chamber. Here, we combine empirical observations, image analysis and computer simulations to investigate the structure and function of the interaction network in the entrance chamber. Ant interactions were distributed heterogeneously in the chamber, with an interaction hot-spot at the entrance leading further into the nest. The distribution of the total interactions per ant followed a right-skewed distribution, indicating the presence of highly connected individuals. Numbers of ant encounters observed positively correlated with the duration of observation. Individuals varied in interaction frequency, even after accounting for the duration of observation. An ant's interaction frequency was explained by its path shape and location within the entrance chamber. Computer simulations demonstrate that variation among individuals in connectivity accelerates information flow to an extent equivalent to an increase in the total number of interactions. Individual variation in connectivity, arising from variation among ants in location and spatial behaviour, creates interaction centres, which may expedite information flow. PMID:21490001

Functional neural networks underlying response inhibition in adolescents and adults.

PubMed

Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2007-07-19

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

Functional neural networks underlying response inhibition in adolescents and adults

PubMed Central

Stevens, Michael C.; Kiehl, Kent A.; Pearlson, Godfrey D.; Calhoun, Vince D.

2008-01-01

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally-integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by frontostriatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development. PMID:17467816

The Association of Multiple Interacting Genes with Specific Phenotypes in Rice Using Gene Coexpression Networks1[C][W][OA

PubMed Central

Ficklin, Stephen P.; Luo, Feng; Feltus, F. Alex

2010-01-01

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes. PMID:20668062

The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks.

PubMed

Ficklin, Stephen P; Luo, Feng; Feltus, F Alex

2010-09-01

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes.

Weighted and directed interactions in evolving large-scale epileptic brain networks

NASA Astrophysics Data System (ADS)

Dickten, Henning; Porz, Stephan; Elger, Christian E.; Lehnertz, Klaus

2016-10-01

Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only - in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

Protein intrinsic disorder in plants.

PubMed

Pazos, Florencio; Pietrosemoli, Natalia; García-Martín, Juan A; Solano, Roberto

2013-09-12

To some extent contradicting the classical paradigm of the relationship between protein 3D structure and function, now it is clear that large portions of the proteomes, especially in higher organisms, lack a fixed structure and still perform very important functions. Proteins completely or partially unstructured in their native (functional) form are involved in key cellular processes underlain by complex networks of protein interactions. The intrinsic conformational flexibility of these disordered proteins allows them to bind multiple partners in transient interactions of high specificity and low affinity. In concordance, in plants this type of proteins has been found in processes requiring these complex and versatile interaction networks. These include transcription factor networks, where disordered proteins act as integrators of different signals or link different transcription factor subnetworks due to their ability to interact (in many cases simultaneously) with different partners. Similarly, they also serve as signal integrators in signaling cascades, such as those related to response to external stimuli. Disordered proteins have also been found in plants in many stress-response processes, acting as protein chaperones or protecting other cellular components and structures. In plants, it is especially important to have complex and versatile networks able to quickly and efficiently respond to changing environmental conditions since these organisms cannot escape and have no other choice than adapting to them. Consequently, protein disorder can play an especially important role in plants, providing them with a fast mechanism to obtain complex, interconnected and versatile molecular networks.

Protein intrinsic disorder in plants

PubMed Central

Pazos, Florencio; Pietrosemoli, Natalia; García-Martín, Juan A.; Solano, Roberto

2013-01-01

To some extent contradicting the classical paradigm of the relationship between protein 3D structure and function, now it is clear that large portions of the proteomes, especially in higher organisms, lack a fixed structure and still perform very important functions. Proteins completely or partially unstructured in their native (functional) form are involved in key cellular processes underlain by complex networks of protein interactions. The intrinsic conformational flexibility of these disordered proteins allows them to bind multiple partners in transient interactions of high specificity and low affinity. In concordance, in plants this type of proteins has been found in processes requiring these complex and versatile interaction networks. These include transcription factor networks, where disordered proteins act as integrators of different signals or link different transcription factor subnetworks due to their ability to interact (in many cases simultaneously) with different partners. Similarly, they also serve as signal integrators in signaling cascades, such as those related to response to external stimuli. Disordered proteins have also been found in plants in many stress-response processes, acting as protein chaperones or protecting other cellular components and structures. In plants, it is especially important to have complex and versatile networks able to quickly and efficiently respond to changing environmental conditions since these organisms cannot escape and have no other choice than adapting to them. Consequently, protein disorder can play an especially important role in plants, providing them with a fast mechanism to obtain complex, interconnected and versatile molecular networks. PMID:24062761

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

PubMed

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.

Neural Connectivity Evidence for a Categorical-Dimensional Hybrid Model of Autism Spectrum Disorder.

PubMed

Elton, Amanda; Di Martino, Adriana; Hazlett, Heather Cody; Gao, Wei

2016-07-15

Autism spectrum disorder (ASD) encompasses a complex manifestation of symptoms that include deficits in social interaction and repetitive or stereotyped interests and behaviors. In keeping with the increasing recognition of the dimensional characteristics of ASD symptoms and the categorical nature of a diagnosis, we sought to delineate the neural mechanisms of ASD symptoms based on the functional connectivity of four known neural networks (i.e., default mode network, dorsal attention network, salience network, and executive control network). We leveraged an open data resource (Autism Brain Imaging Data Exchange) providing resting-state functional magnetic resonance imaging data sets from 90 boys with ASD and 95 typically developing boys. This data set also included the Social Responsiveness Scale as a dimensional measure of ASD traits. Seed-based functional connectivity was paired with linear regression to identify functional connectivity abnormalities associated with categorical effects of ASD diagnosis, dimensional effects of ASD-like behaviors, and their interaction. Our results revealed the existence of dimensional mechanisms of ASD uniquely affecting each network based on the presence of connectivity-behavioral relationships; these were independent of diagnostic category. However, we also found evidence of categorical differences (i.e., diagnostic group differences) in connectivity strength for each network as well as categorical differences in connectivity-behavioral relationships (i.e., diagnosis-by-behavior interactions), supporting the coexistence of categorical mechanisms of ASD. Our findings support a hybrid model for ASD characterization that includes a combination of categorical and dimensional brain mechanisms and provide a novel understanding of the neural underpinnings of ASD. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Food Web Designer: a flexible tool to visualize interaction networks.

PubMed

Sint, Daniela; Traugott, Michael

Species are embedded in complex networks of ecological interactions and assessing these networks provides a powerful approach to understand what the consequences of these interactions are for ecosystem functioning and services. This is mandatory to develop and evaluate strategies for the management and control of pests. Graphical representations of networks can help recognize patterns that might be overlooked otherwise. However, there is a lack of software which allows visualizing these complex interaction networks. Food Web Designer is a stand-alone, highly flexible and user friendly software tool to quantitatively visualize trophic and other types of bipartite and tripartite interaction networks. It is offered free of charge for use on Microsoft Windows platforms. Food Web Designer is easy to use without the need to learn a specific syntax due to its graphical user interface. Up to three (trophic) levels can be connected using links cascading from or pointing towards the taxa within each level to illustrate top-down and bottom-up connections. Link width/strength and abundance of taxa can be quantified, allowing generating fully quantitative networks. Network datasets can be imported, saved for later adjustment and the interaction webs can be exported as pictures for graphical display in different file formats. We show how Food Web Designer can be used to draw predator-prey and host-parasitoid food webs, demonstrating that this software is a simple and straightforward tool to graphically display interaction networks for assessing pest control or any other type of interaction in both managed and natural ecosystems from an ecological network perspective.

Functional Module Search in Protein Networks based on Semantic Similarity Improves the Analysis of Proteomics Data*

PubMed Central

Boyanova, Desislava; Nilla, Santosh; Klau, Gunnar W.; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus

2014-01-01

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system. PMID:24807868

Frequency–specific network connectivity increases underlie accurate spatiotemporal memory retrieval

PubMed Central

Watrous, Andrew J.; Tandon, Nitin; Connor, Chris; Pieters, Thomas; Ekstrom, Arne D.

2013-01-01

The medial temporal lobes, prefrontal cortex, and parts of parietal cortex form the neural underpinnings of episodic memory, which includes remembering both where and when an event occurred. Yet how these three key regions interact during retrieval of spatial and temporal context remains largely untested. Here, we employed simultaneous electrocorticographical recordings across multiple lobular regions, employing phase synchronization as a measure of network functional connectivity, while patients retrieved spatial and temporal context associated with an episode. Successful memory retrieval was characterized by greater global connectivity compared to incorrect retrieval, with the MTL acting as a convergence hub for these interactions. Spatial vs. temporal context retrieval resulted in prominent differences in both the spectral and temporal patterns of network interactions. These results emphasize dynamic network interactions as central to episodic memory retrieval, providing novel insight into how multiple contexts underlying a single event can be recreated within the same network. PMID:23354333

Modular analysis of the probabilistic genetic interaction network.

PubMed

Hou, Lin; Wang, Lin; Qian, Minping; Li, Dong; Tang, Chao; Zhu, Yunping; Deng, Minghua; Li, Fangting

2011-03-15

Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network. Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules.

Evolution and function of CAG/polyglutamine repeats in protein–protein interaction networks

PubMed Central

Schaefer, Martin H.; Wanker, Erich E.; Andrade-Navarro, Miguel A.

2012-01-01

Expanded runs of consecutive trinucleotide CAG repeats encoding polyglutamine (polyQ) stretches are observed in the genes of a large number of patients with different genetic diseases such as Huntington's and several Ataxias. Protein aggregation, which is a key feature of most of these diseases, is thought to be triggered by these expanded polyQ sequences in disease-related proteins. However, polyQ tracts are a normal feature of many human proteins, suggesting that they have an important cellular function. To clarify the potential function of polyQ repeats in biological systems, we systematically analyzed available information stored in sequence and protein interaction databases. By integrating genomic, phylogenetic, protein interaction network and functional information, we obtained evidence that polyQ tracts in proteins stabilize protein interactions. This happens most likely through structural changes whereby the polyQ sequence extends a neighboring coiled-coil region to facilitate its interaction with a coiled-coil region in another protein. Alteration of this important biological function due to polyQ expansion results in gain of abnormal interactions, leading to pathological effects like protein aggregation. Our analyses suggest that research on polyQ proteins should shift focus from expanded polyQ proteins into the characterization of the influence of the wild-type polyQ on protein interactions. PMID:22287626

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

Genetic adaptation of the antibacterial human innate immunity network.

PubMed

Casals, Ferran; Sikora, Martin; Laayouni, Hafid; Montanucci, Ludovica; Muntasell, Aura; Lazarus, Ross; Calafell, Francesc; Awadalla, Philip; Netea, Mihai G; Bertranpetit, Jaume

2011-07-11

Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.

Genetic adaptation of the antibacterial human innate immunity network

PubMed Central

2011-01-01

Background Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Results Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. Conclusions We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response. PMID:21745391

Altered default mode, fronto-parietal and salience networks in adolescents with Internet addiction.

PubMed

Wang, Lubin; Shen, Hui; Lei, Yu; Zeng, Ling-Li; Cao, Fenglin; Su, Linyan; Yang, Zheng; Yao, Shuqiao; Hu, Dewen

2017-07-01

Internet addiction (IA) is a condition characterized by loss of control over Internet use, leading to a variety of negative psychosocial consequences. Recent neuroimaging studies have begun to identify IA-related changes in specific brain regions and connections. However, whether and how the interactions within and between the large-scale brain networks are disrupted in individuals with IA remain largely unexplored. Using group independent component analysis, we extracted five intrinsic connectivity networks (ICNs) from the resting-state fMRI data of 26 adolescents with IA and 43 controls, including the anterior and posterior default mode network (DMN), left and right fronto-parietal network (FPN), and salience network (SN). We then examined the possible group differences in the functional connectivity within each ICN and between the ICNs. We found that, compared with controls, IA subjects showed: (1) reduced inter-hemispheric functional connectivity of the right FPN, whereas increased intra-hemispheric functional connectivity of the left FPN; (2) reduced functional connectivity in the dorsal medial prefrontal cortex (mPFC) of the anterior DMN; (3) reduced functional connectivity between the SN and anterior DMN. Our findings suggest that IA is associated with imbalanced interactions among the DMN, FPN and SN, which may serve as system-level neural underpinnings for the uncontrollable Internet-using behaviors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting multicellular function through multi-layer tissue networks

PubMed Central

Zitnik, Marinka; Leskovec, Jure

2017-01-01

Abstract Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu PMID:28881986

RAID: a comprehensive resource for human RNA-associated (RNA–RNA/RNA–protein) interaction

PubMed Central

Zhang, Xiaomeng; Wu, Deng; Chen, Liqun; Li, Xiang; Yang, Jinxurong; Fan, Dandan; Dong, Tingting; Liu, Mingyue; Tan, Puwen; Xu, Jintian; Yi, Ying; Wang, Yuting; Zou, Hua; Hu, Yongfei; Fan, Kaili; Kang, Juanjuan; Huang, Yan; Miao, Zhengqiang; Bi, Miaoman; Jin, Nana; Li, Kongning; Li, Xia; Xu, Jianzhen; Wang, Dong

2014-01-01

Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA–RNA/RNA–protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA–RNA interactions and 1619 RNA–protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA–RNA/RNA–protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA–RNA/RNA–protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network. PMID:24803509

Subgenual anterior cingulate cortex controls sadness-induced modulations of cognitive and emotional network hubs.

PubMed

Ramirez-Mahaluf, Juan P; Perramon, Joan; Otal, Begonya; Villoslada, Pablo; Compte, Albert

2018-06-04

The regulation of cognitive and emotional processes is critical for proper executive functions and social behavior, but its specific mechanisms remain unknown. Here, we addressed this issue by studying with functional magnetic resonance imaging the changes in network topology that underlie competitive interactions between emotional and cognitive networks in healthy participants. Our behavioral paradigm contrasted periods with high emotional and cognitive demands by including a sadness provocation task followed by a spatial working memory task. The sharp contrast between successive tasks was designed to enhance the separability of emotional and cognitive networks and reveal areas that regulate the flow of information between them (hubs). By applying graph analysis methods on functional connectivity between 20 regions of interest in 22 participants we identified two main brain network modules, one dorsal and one ventral, and their hub areas: the left dorsolateral prefrontal cortex (dlPFC) and the left medial frontal pole (mFP). These hub areas did not modulate their mutual functional connectivity following sadness but they did so through an interposed area, the subgenual anterior cingulate cortex (sACC). Our results identify dlPFC and mFP as areas regulating interactions between emotional and cognitive networks, and suggest that their modulation by sadness experience is mediated by sACC.

Exploring Molecular Mechanisms of Paradoxical Activation in the BRAF Kinase Dimers: Atomistic Simulations of Conformational Dynamics and Modeling of Allosteric Communication Networks and Signaling Pathways

PubMed Central

Tse, Amanda; Verkhivker, Gennady M.

2016-01-01

The recent studies have revealed that most BRAF inhibitors can paradoxically induce kinase activation by promoting dimerization and enzyme transactivation. Despite rapidly growing number of structural and functional studies about the BRAF dimer complexes, the molecular basis of paradoxical activation phenomenon is poorly understood and remains largely hypothetical. In this work, we have explored the relationships between inhibitor binding, protein dynamics and allosteric signaling in the BRAF dimers using a network-centric approach. Using this theoretical framework, we have combined molecular dynamics simulations with coevolutionary analysis and modeling of the residue interaction networks to determine molecular determinants of paradoxical activation. We have investigated functional effects produced by paradox inducer inhibitors PLX4720, Dabrafenib, Vemurafenib and a paradox breaker inhibitor PLX7904. Functional dynamics and binding free energy analyses of the BRAF dimer complexes have suggested that negative cooperativity effect and dimer-promoting potential of the inhibitors could be important drivers of paradoxical activation. We have introduced a protein structure network model in which coevolutionary residue dependencies and dynamic maps of residue correlations are integrated in the construction and analysis of the residue interaction networks. The results have shown that coevolutionary residues in the BRAF structures could assemble into independent structural modules and form a global interaction network that may promote dimerization. We have also found that BRAF inhibitors could modulate centrality and communication propensities of global mediating centers in the residue interaction networks. By simulating allosteric communication pathways in the BRAF structures, we have determined that paradox inducer and breaker inhibitors may activate specific signaling routes that correlate with the extent of paradoxical activation. While paradox inducer inhibitors may facilitate a rapid and efficient communication via an optimal single pathway, the paradox breaker may induce a broader ensemble of suboptimal and less efficient communication routes. The central finding of our study is that paradox breaker PLX7904 could mimic structural, dynamic and network features of the inactive BRAF-WT monomer that may be required for evading paradoxical activation. The results of this study rationalize the existing structure-functional experiments by offering a network-centric rationale of the paradoxical activation phenomenon. We argue that BRAF inhibitors that amplify dynamic features of the inactive BRAF-WT monomer and intervene with the allosteric interaction networks may serve as effective paradox breakers in cellular environment. PMID:27861609

Optimality Principles in the Regulation of Metabolic Networks

PubMed Central

Berkhout, Jan; Bruggeman, Frank J.; Teusink, Bas

2012-01-01

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide. PMID:24957646

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

NASA Astrophysics Data System (ADS)

Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

2016-05-01

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

Dancing through Life: Molecular Dynamics Simulations and Network-Centric Modeling of Allosteric Mechanisms in Hsp70 and Hsp110 Chaperone Proteins.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2015-01-01

Hsp70 and Hsp110 chaperones play an important role in regulating cellular processes that involve protein folding and stabilization, which are essential for the integrity of signaling networks. Although many aspects of allosteric regulatory mechanisms in Hsp70 and Hsp110 chaperones have been extensively studied and significantly advanced in recent experimental studies, the atomistic picture of signal propagation and energetics of dynamics-based communication still remain unresolved. In this work, we have combined molecular dynamics simulations and protein stability analysis of the chaperone structures with the network modeling of residue interaction networks to characterize molecular determinants of allosteric mechanisms. We have shown that allosteric mechanisms of Hsp70 and Hsp110 chaperones may be primarily determined by nucleotide-induced redistribution of local conformational ensembles in the inter-domain regions and the substrate binding domain. Conformational dynamics and energetics of the peptide substrate binding with the Hsp70 structures has been analyzed using free energy calculations, revealing allosteric hotspots that control negative cooperativity between regulatory sites. The results have indicated that cooperative interactions may promote a population-shift mechanism in Hsp70, in which functional residues are organized in a broad and robust allosteric network that can link the nucleotide-binding site and the substrate-binding regions. A smaller allosteric network in Hsp110 structures may elicit an entropy-driven allostery that occurs in the absence of global structural changes. We have found that global mediating residues with high network centrality may be organized in stable local communities that are indispensable for structural stability and efficient allosteric communications. The network-centric analysis of allosteric interactions has also established that centrality of functional residues could correlate with their sensitivity to mutations across diverse chaperone functions. This study reconciles a wide spectrum of structural and functional experiments by demonstrating how integration of molecular simulations and network-centric modeling may explain thermodynamic and mechanistic aspects of allosteric regulation in chaperones.

Dancing through Life: Molecular Dynamics Simulations and Network-Centric Modeling of Allosteric Mechanisms in Hsp70 and Hsp110 Chaperone Proteins

PubMed Central

Stetz, Gabrielle; Verkhivker, Gennady M.

2015-01-01

Hsp70 and Hsp110 chaperones play an important role in regulating cellular processes that involve protein folding and stabilization, which are essential for the integrity of signaling networks. Although many aspects of allosteric regulatory mechanisms in Hsp70 and Hsp110 chaperones have been extensively studied and significantly advanced in recent experimental studies, the atomistic picture of signal propagation and energetics of dynamics-based communication still remain unresolved. In this work, we have combined molecular dynamics simulations and protein stability analysis of the chaperone structures with the network modeling of residue interaction networks to characterize molecular determinants of allosteric mechanisms. We have shown that allosteric mechanisms of Hsp70 and Hsp110 chaperones may be primarily determined by nucleotide-induced redistribution of local conformational ensembles in the inter-domain regions and the substrate binding domain. Conformational dynamics and energetics of the peptide substrate binding with the Hsp70 structures has been analyzed using free energy calculations, revealing allosteric hotspots that control negative cooperativity between regulatory sites. The results have indicated that cooperative interactions may promote a population-shift mechanism in Hsp70, in which functional residues are organized in a broad and robust allosteric network that can link the nucleotide-binding site and the substrate-binding regions. A smaller allosteric network in Hsp110 structures may elicit an entropy-driven allostery that occurs in the absence of global structural changes. We have found that global mediating residues with high network centrality may be organized in stable local communities that are indispensable for structural stability and efficient allosteric communications. The network-centric analysis of allosteric interactions has also established that centrality of functional residues could correlate with their sensitivity to mutations across diverse chaperone functions. This study reconciles a wide spectrum of structural and functional experiments by demonstrating how integration of molecular simulations and network-centric modeling may explain thermodynamic and mechanistic aspects of allosteric regulation in chaperones. PMID:26619280

Emergent structure-function relations in emphysema and asthma.

PubMed

Winkler, Tilo; Suki, Béla

2011-01-01

Structure-function relationships in the respiratory system are often a result of the emergence of self-organized patterns or behaviors that are characteristic of certain respiratory diseases. Proper description of such self-organized behavior requires network models that include nonlinear interactions among different parts of the system. This review focuses on 2 models that exhibit self-organized behavior: a network model of the lung parenchyma during the progression of emphysema that is driven by mechanical force-induced breakdown, and an integrative model of bronchoconstriction in asthma that describes interactions among airways within the bronchial tree. Both models suggest that the transition from normal to pathologic states is a nonlinear process that includes a tipping point beyond which interactions among the system components are reinforced by positive feedback, further promoting the progression of pathologic changes. In emphysema, the progressive destruction of tissue is irreversible, while in asthma, it is possible to recover from a severe bronchoconstriction. These concepts may have implications for pulmonary medicine. Specifically, we suggest that structure-function relationships emerging from network behavior across multiple scales should be taken into account when the efficacy of novel treatments or drug therapy is evaluated. Multiscale, computational, network models will play a major role in this endeavor.

Sex differences in interactions between nucleus accumbens and visual cortex by explicit visual erotic stimuli: an fMRI study.

PubMed

Lee, S W; Jeong, B S; Choi, J; Kim, J-W

2015-01-01

Men tend to have greater positive responses than women to explicit visual erotic stimuli (EVES). However, it remains unclear, which brain network makes men more sensitive to EVES and which factors contribute to the brain network activity. In this study, we aimed to assess the effect of sex difference on brain connectivity patterns by EVES. We also investigated the association of testosterone with brain connection that showed the effects of sex difference. During functional magnetic resonance imaging scans, 14 males and 14 females were asked to see alternating blocks of pictures that were either erotic or non-erotic. Psychophysiological interaction analysis was performed to investigate the functional connectivity of the nucleus accumbens (NA) as it related to EVES. Men showed significantly greater EVES-specific functional connection between the right NA and the right lateral occipital cortex (LOC). In addition, the right NA and the right LOC network activity was positively correlated with the plasma testosterone level in men. Our results suggest that the reason men are sensitive to EVES is the increased interaction in the visual reward networks, which is modulated by their plasma testosterone level.

Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks.

PubMed

Voss, Michelle W; Prakash, Ruchika Shaurya; Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2012-01-02

We used the Space Fortress videogame, originally developed by cognitive psychologists to study skill acquisition, as a platform to examine learning-induced plasticity of interacting brain networks. Novice videogame players learned Space Fortress using one of two training strategies: (a) focus on all aspects of the game during learning (fixed priority), or (b) focus on improving separate game components in the context of the whole game (variable priority). Participants were scanned during game play using functional magnetic resonance imaging (fMRI), both before and after 20 h of training. As expected, variable priority training enhanced learning, particularly for individuals who initially performed poorly. Functional connectivity analysis revealed changes in brain network interaction reflective of more flexible skill learning and retrieval with variable priority training, compared to procedural learning and skill implementation with fixed priority training. These results provide the first evidence for differences in the interaction of large-scale brain networks when learning with different training strategies. Our approach and findings also provide a foundation for exploring the brain plasticity involved in transfer of trained abilities to novel real-world tasks such as driving, sport, or neurorehabilitation. Copyright © 2011 Elsevier Inc. All rights reserved.

Direction of information flow in large-scale resting-state networks is frequency-dependent.

PubMed

Hillebrand, Arjan; Tewarie, Prejaas; van Dellen, Edwin; Yu, Meichen; Carbo, Ellen W S; Douw, Linda; Gouw, Alida A; van Straaten, Elisabeth C W; Stam, Cornelis J

2016-04-05

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

A graph-theoretical analysis algorithm for quantifying the transition from sensory input to motor output by an emotional stimulus.

PubMed

Karmonik, Christof; Fung, Steve H; Dulay, M; Verma, A; Grossman, Robert G

2013-01-01

Graph-theoretical analysis algorithms have been used for identifying subnetworks in the human brain during the Default Mode State. Here, these methods are expanded to determine the interaction of the sensory and the motor subnetworks during the performance of an approach-avoidance paradigm utilizing the correlation strength between the signal intensity time courses as measure of synchrony. From functional magnetic resonance imaging (fMRI) data of 9 healthy volunteers, two signal time courses, one from the primary visual cortex (sensory input) and one from the motor cortex (motor output) were identified and a correlation difference map was calculated. Graph networks were created from this map and visualized with spring-embedded layouts and 3D layouts in the original anatomical space. Functional clusters in these networks were identified with the MCODE clustering algorithm. Interactions between the sensory sub-network and the motor sub-network were quantified through the interaction strengths of these clusters. The percentages of interactions involving the visual cortex ranged from 85 % to 18 % and the motor cortex ranged from 40 % to 9 %. Other regions with high interactions were: frontal cortex (19 ± 18 %), insula (17 ± 22 %), cuneus (16 ± 15 %), supplementary motor area (SMA, 11 ± 18 %) and subcortical regions (11 ± 10 %). Interactions between motor cortex, SMA and visual cortex accounted for 12 %, between visual cortex and cuneus for 8 % and between motor cortex, SMA and cuneus for 6 % of all interactions. These quantitative findings are supported by the visual impressions from the 2D and 3D network layouts.

Scanning number and brightness yields absolute protein concentrations in live cells: a crucial parameter controlling functional bio-molecular interaction networks.

PubMed

Papini, Christina; Royer, Catherine A

2018-02-01

Biological function results from properly timed bio-molecular interactions that transduce external or internal signals, resulting in any number of cellular fates, including triggering of cell-state transitions (division, differentiation, transformation, apoptosis), metabolic homeostasis and adjustment to changing physical or nutritional environments, amongst many more. These bio-molecular interactions can be modulated by chemical modifications of proteins, nucleic acids, lipids and other small molecules. They can result in bio-molecular transport from one cellular compartment to the other and often trigger specific enzyme activities involved in bio-molecular synthesis, modification or degradation. Clearly, a mechanistic understanding of any given high level biological function requires a quantitative characterization of the principal bio-molecular interactions involved and how these may change dynamically. Such information can be obtained using fluctation analysis, in particular scanning number and brightness, and used to build and test mechanistic models of the functional network to define which characteristics are the most important for its regulation.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

PubMed

Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

2017-04-01

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

Reveal genes functionally associated with ACADS by a network study.

PubMed

Chen, Yulong; Su, Zhiguang

2015-09-15

Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

An experimental analysis of granivory in a desert ecosystem: Progress report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, J.H.

1987-03-01

Controlled, replicated experiments are revealing the network of interactions that determine structure, dynamics, and energy transfer in a desert community that is functionally interconnected by the consumption of seeds (granivory). This community includes seed-eating rodents, ants, and birds, seed-producing annual and perennial plants, and other kinds of organisms that interact with these. The experiments entail removal of important species or functional groups of granivores or plants and supplementation of seed resources. The results demonstrate a large number of direct and indirect interactions that have important effects on the abundance of species and functional groups, the structure of the community, andmore » the dynamics of energy flow. The results suggest that networks of interaction are structured with sufficient overlap in resource requirements and interconnections through indirect pathways that community- and ecosystem-level processes, such as energy flow, are relatively insensitive to major perturbations in the abundance of particular species or functional groups. This preliminary finding has important implications for understanding the response of ecosystems to natural and human-caused perturbations, for the management of agricultural and other human-modified ecosystems, and for the design of perturbation-resistant networks for acquisition and distribution of human resources such energy and information. 44 refs.« less

The function of neurocognitive networks. Comment on “Understanding brain networks and brain organization” by Pessoa

NASA Astrophysics Data System (ADS)

Bressler, Steven L.

2014-09-01

Pessoa [5] has performed a valuable service by reviewing the extant literature on brain networks and making a number of interesting proposals about their cognitive function. The term function is at the core of understanding the brain networks of cognition, or neurocognitive networks (NCNs) [1]. The great Russian neuropsychologist, Luria [4], defined brain function as the common task executed by a distributed brain network of complex dynamic structures united by the demands of cognition. Casting Luria in a modern light, we can say that function emerges from the interactions of brain regions in NCNs as they dynamically self-organize according to cognitive demands. Pessoa rightly details the mapping between brain function and structure, emphasizing both its pluripotency (one structure having multiple functions) and degeneracy (many structures having the same function). However, he fails to consider the potential importance of a one-to-one mapping between NCNs and function. If NCNs are uniquely composed of specific collections of brain areas, then each NCN has a unique function determined by that composition.

Arabidopsis Ensemble Reverse-Engineered Gene Regulatory Network Discloses Interconnected Transcription Factors in Oxidative Stress[W

PubMed Central

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-01-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. PMID:25549671

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.

PubMed

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-12-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. © 2014 American Society of Plant Biologists. All rights reserved.

Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

PubMed Central

Cerliani, Leonardo; Mennes, Maarten; Thomas, Rajat M.; Di Martino, Adriana; Thioux, Marc; Keysers, Christian

2016-01-01

Importance Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. Objectives To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. Design, Setting, and Participants We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. Main Outcomes and Measures We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. Results Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r ≥ 0.21, P < .0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r ≤ −0.09, P < .012 corrected), although this association was significant only in typically developing participants (all r ≤ −0.13, P < .009 corrected). Conclusions and Relevance Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior. PMID:26061743

GENIUS: web server to predict local gene networks and key genes for biological functions.

PubMed

Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

2017-03-01

GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods. GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius . genius.psbl@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Heteroclinic switching between chimeras

NASA Astrophysics Data System (ADS)

Bick, Christian

2018-05-01

Functional oscillator networks, such as neuronal networks in the brain, exhibit switching between metastable states involving many oscillators. We give exact results how such global dynamics can arise in paradigmatic phase oscillator networks: Higher-order network interactions give rise to metastable chimeras—localized frequency synchrony patterns—which are joined by heteroclinic connections. Moreover, we illuminate the mechanisms that underly the switching dynamics in these experimentally accessible networks.

Virtual Interactomics of Proteins from Biochemical Standpoint

PubMed Central

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel

2012-01-01

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations. PMID:22928109

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

PubMed

Vo, Tommy V; Das, Jishnu; Meyer, Michael J; Cordero, Nicolas A; Akturk, Nurten; Wei, Xiaomu; Fair, Benjamin J; Degatano, Andrew G; Fragoza, Robert; Liu, Lisa G; Matsuyama, Akihisa; Trickey, Michelle; Horibata, Sachi; Grimson, Andrew; Yamano, Hiroyuki; Yoshida, Minoru; Roth, Frederick P; Pleiss, Jeffrey A; Xia, Yu; Yu, Haiyuan

2016-01-14

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

Fusing literature and full network data improves disease similarity computation.

PubMed

Li, Ping; Nie, Yaling; Yu, Jingkai

2016-08-30

Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

Modularity, pollination systems, and interaction turnover in plant-pollinator networks across space.

PubMed

Carstensen, Daniel W; Sabatino, Malena; Morellato, Leonor Patricia C

2016-05-01

Mutualistic interaction networks have been shown to be structurally conserved over space and time while pairwise interactions show high variability. In such networks, modularity is the division of species into compartments, or modules, where species within modules share more interactions with each other than they do with species from other modules. Such a modular structure is common in mutualistic networks and several evolutionary and ecological mechanisms have been proposed as underlying drivers. One prominent explanation is the existence of pollination syndromes where flowers tend to attract certain pollinators as determined by a set of traits. We investigate the modularity of seven community level plant-pollinator networks sampled in rupestrian grasslands, or campos rupestres, in SE Brazil. Defining pollination systems as corresponding groups of flower syndromes and pollinator functional groups, we test the two hypotheses that (1) interacting species from the same pollination system are more often assigned to the same module than interacting species from different pollination systems and; that (2) interactions between species from the same pollination system are more consistent across space than interactions between species from different pollination systems. Specifically we ask (1) whether networks are consistently modular across space; (2) whether interactions among species of the same pollination system occur more often inside modules, compared to interactions among species of different pollination systems, and finally; (3) whether the spatial variation in interaction identity, i.e., spatial interaction rewiring, is affected by trait complementarity among species as indicated by pollination systems. We confirm that networks are consistently modular across space and that interactions within pollination systems principally occur inside modules. Despite a strong tendency, we did not find a significant effect of pollination systems on the spatial consistency of pairwise interactions. These results indicate that the spatial rewiring of interactions could be constrained by pollination systems, resulting in conserved network structures in spite of high variation in pairwise interactions. Our findings suggest a relevant role of pollination systems in structuring plant-pollinator networks and we argue that structural patterns at the sub-network level can help us to fully understand how and why interactions vary across space and time.

Analytical theory of polymer-network-mediated interaction between colloidal particles

PubMed Central

Di Michele, Lorenzo; Zaccone, Alessio; Eiser, Erika

2012-01-01

Nanostructured materials based on colloidal particles embedded in a polymer network are used in a variety of applications ranging from nanocomposite rubbers to organic-inorganic hybrid solar cells. Further, polymer-network-mediated colloidal interactions are highly relevant to biological studies whereby polymer hydrogels are commonly employed to probe the mechanical response of living cells, which can determine their biological function in physiological environments. The performance of nanomaterials crucially relies upon the spatial organization of the colloidal particles within the polymer network that depends, in turn, on the effective interactions between the particles in the medium. Existing models based on nonlocal equilibrium thermodynamics fail to clarify the nature of these interactions, precluding the way toward the rational design of polymer-composite materials. In this article, we present a predictive analytical theory of these interactions based on a coarse-grained model for polymer networks. We apply the theory to the case of colloids partially embedded in cross-linked polymer substrates and clarify the origin of attractive interactions recently observed experimentally. Monte Carlo simulation results that quantitatively confirm the theoretical predictions are also presented. PMID:22679289

Effect of memory in non-Markovian Boolean networks illustrated with a case study: A cell cycling process

NASA Astrophysics Data System (ADS)

Ebadi, H.; Saeedian, M.; Ausloos, M.; Jafari, G. R.

2016-11-01

The Boolean network is one successful model to investigate discrete complex systems such as the gene interacting phenomenon. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self-organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function —one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of the yeast cell cycle network, we discover a power-law-like memory with a more robust dynamics than the Markovian dynamics.

Reconfiguration of Intrinsic Functional Coupling Patterns Following Circumscribed Network Lesions.

PubMed

Eldaief, Mark C; McMains, Stephanie; Hutchison, R Matthew; Halko, Mark A; Pascual-Leone, Alvaro

2017-05-01

Communication between cortical regions is necessary for optimal cognitive processing. Functional relationships between cortical regions can be inferred through measurements of temporal synchrony in spontaneous activity patterns. These relationships can be further elaborated by surveying effects of cortical lesions upon inter-regional connectivity. Lesions to cortical hubs and heteromodal association regions are expected to induce distributed connectivity changes and higher-order cognitive deficits, yet their functional consequences remain relatively unexplored. Here, we used resting-state fMRI to investigate intrinsic functional connectivity (FC) and graph theoretical metrics in 12 patients with circumscribed lesions of the medial prefrontal cortex (mPFC) portion of the Default Network (DN), and compared these metrics with those observed in healthy matched comparison participants and a sample of 1139 healthy individuals. Despite significant mPFC destruction, patients did not demonstrate weakened intrinsic FC among undamaged DN nodes. Instead, network-specific changes were manifested as weaker negative correlations between the DN and attentional and somatomotor networks. These findings conflict with the DN being a homogenous system functionally anchored at mPFC. Rather, they implicate a role for mPFC in mediating cross-network functional interactions. More broadly, our data suggest that lesions to association cortical hubs might induce clinical deficits by disrupting communication between interacting large-scale systems. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Revealing the Strong Functional Association of adipor2 and cdh13 with adipoq: A Gene Network Study.

PubMed

Bag, Susmita; Anbarasu, Anand

2015-04-01

In the present study, we have analyzed functional gene interactions of adiponectin gene (adipoq). The key role of adipoq is in regulating energy homeostasis and it functions as a novel signaling molecule for adipose tissue. Modules of highly inter-connected genes in disease-specific adipoq network are derived by integrating gene function and protein interaction data. Among twenty genes in adipoq web, adipoq is effectively conjoined with two genes: Adiponectin receptor 2 (adipor2) and cadherin 13 (cdh13). The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with adipoq are adipor2 and cdh13. Interestingly, the ontological aspect of adipor2 and cdh13 in the adipoq network reveal the fact that adipoq and adipor2 are involved mostly in glucose and lipid metabolic processes. The gene cdh13 indulge in cell adhesion process with adipoq and adipor2. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of adipoq, adipor2, and cdh13 with not only with obesity but also with breast cancer, leukemia, renal cancer, lung cancer, and cervical cancer. The current study provides researchers a comprehensible layout of adipoq network, its functional strategies and candidate disease approach associated with adipoq network.

Plant pollinator networks along a gradient of urbanisation.

PubMed

Geslin, Benoît; Gauzens, Benoit; Thébault, Elisa; Dajoz, Isabelle

2013-01-01

Habitat loss is one of the principal causes of the current pollinator decline. With agricultural intensification, increasing urbanisation is among the main drivers of habitat loss. Consequently studies focusing on pollinator community structure along urbanisation gradients have increased in recent years. However, few studies have investigated how urbanisation affects plant-pollinator interaction networks. Here we assessed modifications of plant-pollinator interactions along an urbanisation gradient based on the study of their morphological relationships. Along an urbanisation gradient comprising four types of landscape contexts (semi-natural, agricultural, suburban, urban), we set up experimental plant communities containing two plant functional groups differing in their morphological traits ("open flowers" and "tubular flowers"). Insect visitations on these communities were recorded to build plant-pollinator networks. A total of 17 857 interactions were recorded between experimental plant communities and flower-visitors. The number of interactions performed by flower-visitors was significantly lower in urban landscape context than in semi-natural and agricultural ones. In particular, insects such as Syrphidae and solitary bees that mostly visited the open flower functional group were significantly impacted by urbanisation, which was not the case for bumblebees. Urbanisation also impacted the generalism of flower-visitors and we detected higher interaction evenness in urban landscape context than in agricultural and suburban ones. Finally, in urban context, these modifications lowered the potential reproductive success of the open flowers functional group. Our findings show that open flower plant species and their specific flower-visitors are especially sensitive to increasing urbanisation. These results provide new clues to improve conservation measures within urbanised areas in favour of specialist flower-visitors. To complete this functional approach, studies using networks resolved to the species level along urbanised gradients would be required.

Plant Pollinator Networks along a Gradient of Urbanisation

PubMed Central

Geslin, Benoît; Gauzens, Benoit; Thébault, Elisa; Dajoz, Isabelle

2013-01-01

Background Habitat loss is one of the principal causes of the current pollinator decline. With agricultural intensification, increasing urbanisation is among the main drivers of habitat loss. Consequently studies focusing on pollinator community structure along urbanisation gradients have increased in recent years. However, few studies have investigated how urbanisation affects plant-pollinator interaction networks. Here we assessed modifications of plant-pollinator interactions along an urbanisation gradient based on the study of their morphological relationships. Methodology/Principal Findings Along an urbanisation gradient comprising four types of landscape contexts (semi-natural, agricultural, suburban, urban), we set up experimental plant communities containing two plant functional groups differing in their morphological traits (“open flowers” and “tubular flowers”). Insect visitations on these communities were recorded to build plant-pollinator networks. A total of 17 857 interactions were recorded between experimental plant communities and flower-visitors. The number of interactions performed by flower-visitors was significantly lower in urban landscape context than in semi-natural and agricultural ones. In particular, insects such as Syrphidae and solitary bees that mostly visited the open flower functional group were significantly impacted by urbanisation, which was not the case for bumblebees. Urbanisation also impacted the generalism of flower-visitors and we detected higher interaction evenness in urban landscape context than in agricultural and suburban ones. Finally, in urban context, these modifications lowered the potential reproductive success of the open flowers functional group. Conclusions/Significance Our findings show that open flower plant species and their specific flower-visitors are especially sensitive to increasing urbanisation. These results provide new clues to improve conservation measures within urbanised areas in favour of specialist flower-visitors. To complete this functional approach, studies using networks resolved to the species level along urbanised gradients would be required. PMID:23717421

Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ba, Qian; Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing; Li, Junyang

2015-03-01

Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong tomore » the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.« less

Data-driven analysis of functional brain interactions during free listening to music and speech.

PubMed

Fang, Jun; Hu, Xintao; Han, Junwei; Jiang, Xi; Zhu, Dajiang; Guo, Lei; Liu, Tianming

2015-06-01

Natural stimulus functional magnetic resonance imaging (N-fMRI) such as fMRI acquired when participants were watching video streams or listening to audio streams has been increasingly used to investigate functional mechanisms of the human brain in recent years. One of the fundamental challenges in functional brain mapping based on N-fMRI is to model the brain's functional responses to continuous, naturalistic and dynamic natural stimuli. To address this challenge, in this paper we present a data-driven approach to exploring functional interactions in the human brain during free listening to music and speech streams. Specifically, we model the brain responses using N-fMRI by measuring the functional interactions on large-scale brain networks with intrinsically established structural correspondence, and perform music and speech classification tasks to guide the systematic identification of consistent and discriminative functional interactions when multiple subjects were listening music and speech in multiple categories. The underlying premise is that the functional interactions derived from N-fMRI data of multiple subjects should exhibit both consistency and discriminability. Our experimental results show that a variety of brain systems including attention, memory, auditory/language, emotion, and action networks are among the most relevant brain systems involved in classic music, pop music and speech differentiation. Our study provides an alternative approach to investigating the human brain's mechanism in comprehension of complex natural music and speech.

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks.

PubMed

Li, Min; Li, Qi; Ganegoda, Gamage Upeksha; Wang, JianXin; Wu, FangXiang; Pan, Yi

2014-11-01

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies. However, it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments. With the advances of the high-throughput techniques, a large number of protein-protein interactions have been produced. Therefore, to address this issue, several methods based on protein interaction network have been proposed. In this paper, we propose a shortest path-based algorithm, named SPranker, to prioritize disease-causing genes in protein interaction networks. Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes, we further propose an improved algorithm SPGOranker by integrating the semantic similarity of GO annotations. SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account. The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches, ICN, VS and RWR. The experimental results show that SPranker and SPGOranker outperform ICN, VS, and RWR for the prioritization of orphan disease-causing genes. Importantly, for the case study of severe combined immunodeficiency, SPranker and SPGOranker predict several novel causal genes.

Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions.

PubMed

Baltoumas, Fotis A; Theodoropoulou, Margarita C; Hamodrakas, Stavros J

2016-06-01

A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions

NASA Astrophysics Data System (ADS)

Baltoumas, Fotis A.; Theodoropoulou, Margarita C.; Hamodrakas, Stavros J.

2016-06-01

A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

Social play among juvenile wild Japanese macaques (Macaca fuscata) strengthens their social bonds.

PubMed

Shimada, Masaki; Sueur, Cédric

2018-01-01

Social play and grooming are typical affiliative interactions for many primate species, and are thought to have similar biological functions. However, grooming increases with age, whereas social play decreases. We proposed the hypothesis that both social grooming and social play in juveniles strengthen their social bonds in daily activities. We carried out field research on the social relationships among juvenile wild Japanese macaques in a troop in Kinkazan, Miyagi Prefecture, Japan, from fall 2007 to spring 2008 to investigate this hypothesis. We evaluated three relationships among juveniles, play indices (PI), grooming indices (GI), and 3-m-proximity indices (3mI) of each dyad (i.e., interacting pair), and compared these social networks based on the matrices of the indices. The play and grooming networks were correlated with the association network throughout the two research periods. The multiple network level measurements of the play network, but not the grooming network, resembled those of the association network. Using a causal step approach, we showed that social play and grooming interactions in fall seem to predict associations in the following spring, controlling for the PI and GI matrix in spring, respectively. Social play and grooming for each juvenile were negatively correlated. The results partially support our predictions; therefore, the hypothesis that the biological function of social play among immature Japanese macaques is to strengthen their social bonds in the near future and develop their social life appears to be correct. For juvenile macaques, social play, rather than grooming, functions as an important social mechanism to strengthen affiliative relationships. © 2017 Wiley Periodicals, Inc.

The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies* | Office of Cancer Genomics

Cancer.gov

As genomics advances reveal the cancer gene landscape, a daunting task is to understand how these genes contribute to dysregulated oncogenic pathways. Integration of cancer genes into networks offers opportunities to reveal protein–protein interactions (PPIs) with functional and therapeutic significance. Here, we report the generation of a cancer-focused PPI network, termed OncoPPi, and identification of >260 cancer-associated PPIs not in other large-scale interactomes.

Functional annotation of regulatory pathways.

PubMed

Pandey, Jayesh; Koyutürk, Mehmet; Kim, Yohan; Szpankowski, Wojciech; Subramaniam, Shankar; Grama, Ananth

2007-07-01

Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.

Cross-Sectional Study of Young Adults Diagnosed With Juvenile Fibromyalgia: Social Support and Its Impact on Functioning and Mood.

PubMed

Lynch-Jordan, Anne M; Sil, Soumitri; Bromberg, Maggie; Ting, Tracy V; Kashikar-Zuck, Susmita

2015-11-01

Juvenile-onset fibromyalgia (JFM) affects physical, social, and emotional functioning. Little is known about how social support and social interactions are impacted in the transition to young adulthood for patients diagnosed with JFM. Young adults (Mage = 21.6) diagnosed with JFM during adolescence (N = 94) and matched healthy controls (N = 33) completed measures of social network size and diversity, perceived social support, physical functioning, and depressive symptoms as part of a cross-sectional survey study. No difference in social network diversity was found, although JFM patients reported fewer total people within their social networks. JFM patients reported poorer emotional and tangible support and fewer positive social interactions than healthy controls. After controlling for condition and pain intensity, the level of perceived social support was a significant predictor of physical functioning and depressive symptoms, whereas social network size also contributed uniquely to physical functioning. Given the developmental importance of social support in adolescence and young adulthood, interventions should include methods of improving social support into fibromyalgia management. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Correspondence of the brain's functional architecture during activation and rest

PubMed Central

Smith, Stephen M.; Fox, Peter T.; Miller, Karla L.; Glahn, David C.; Fox, P. Mickle; Mackay, Clare E.; Filippini, Nicola; Watkins, Kate E.; Toro, Roberto; Laird, Angela R.; Beckmann, Christian F.

2009-01-01

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.” PMID:19620724

Evidence for network evolution in an arabidopsis interactome map

USDA-ARS?s Scientific Manuscript database

Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the pl...

Change Point Detection in Correlation Networks

NASA Astrophysics Data System (ADS)

Barnett, Ian; Onnela, Jukka-Pekka

2016-01-01

Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.

Process-based network decomposition reveals backbone motif structure

PubMed Central

Wang, Guanyu; Du, Chenghang; Chen, Hao; Simha, Rahul; Rong, Yongwu; Xiao, Yi; Zeng, Chen

2010-01-01

A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated). PMID:20498084

Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

PubMed

Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

2017-03-01

Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls.

PubMed

Shen, John Paul; Ideker, Trey

2018-06-19

Synthetic lethal interactions, in which the simultaneous loss-of-function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three FDA approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic. Copyright © 2018. Published by Elsevier Ltd.

Random Evolution of Idiotypic Networks: Dynamics and Architecture

NASA Astrophysics Data System (ADS)

Brede, Markus; Behn, Ulrich

The paper deals with modelling a subsystem of the immune system, the so-called idiotypic network (INW). INWs, conceived by N.K. Jerne in 1974, are functional networks of interacting antibodies and B cells. In principle, Jernes' framework provides solutions to many issues in immunology, such as immunological memory, mechanisms for antigen recognition and self/non-self discrimination. Explaining the interconnection between the elementary components, local dynamics, network formation and architecture, and possible modes of global system function appears to be an ideal playground of statistical mechanics. We present a simple cellular automaton model, based on a graph representation of the system. From a simplified description of idiotypic interactions, rules for the random evolution of networks of occupied and empty sites on these graphs are derived. In certain biologically relevant parameter ranges the resultant dynamics leads to stationary states. A stationary state is found to correspond to a specific pattern of network organization. It turns out that even these very simple rules give rise to a multitude of different kinds of patterns. We characterize these networks by classifying `static' and `dynamic' network-patterns. A type of `dynamic' network is found to display many features of real INWs.

Default and Executive Network Coupling Supports Creative Idea Production

PubMed Central

Beaty, Roger E.; Benedek, Mathias; Barry Kaufman, Scott; Silvia, Paul J.

2015-01-01

The role of attention in creative cognition remains controversial. Neuroimaging studies have reported activation of brain regions linked to both cognitive control and spontaneous imaginative processes, raising questions about how these regions interact to support creative thought. Using functional magnetic resonance imaging (fMRI), we explored this question by examining dynamic interactions between brain regions during a divergent thinking task. Multivariate pattern analysis revealed a distributed network associated with divergent thinking, including several core hubs of the default (posterior cingulate) and executive (dorsolateral prefrontal cortex) networks. The resting-state network affiliation of these regions was confirmed using data from an independent sample of participants. Graph theory analysis assessed global efficiency of the divergent thinking network, and network efficiency was found to increase as a function of individual differences in divergent thinking ability. Moreover, temporal connectivity analysis revealed increased coupling between default and salience network regions (bilateral insula) at the beginning of the task, followed by increased coupling between default and executive network regions at later stages. Such dynamic coupling suggests that divergent thinking involves cooperation between brain networks linked to cognitive control and spontaneous thought, which may reflect focused internal attention and the top-down control of spontaneous cognition during creative idea production. PMID:26084037

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

PubMed Central

Ibarra-Arellano, Miguel A.; Campos-González, Adrián I.; Treviño-Quintanilla, Luis G.; Tauch, Andreas; Freyre-González, Julio A.

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them. Database URL: http://abasy.ccg.unam.mx PMID:27242034

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

PubMed

Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

2015-04-01

The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

PubMed Central

Cannistraci, Carlo Vittorio; 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. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable. Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions. Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction. Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. Availability: https://sites.google.com/site/carlovittoriocannistraci/home Contact: kalokagathos.agon@gmail.com or timothy.ravasi@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23812985

Multi-function robots with speech interaction and emotion feedback

NASA Astrophysics Data System (ADS)

Wang, Hongyu; Lou, Guanting; Ma, Mengchao

2018-03-01

Nowadays, the service robots have been applied in many public circumstances; however, most of them still don’t have the function of speech interaction, especially the function of speech-emotion interaction feedback. To make the robot more humanoid, Arduino microcontroller was used in this study for the speech recognition module and servo motor control module to achieve the functions of the robot’s speech interaction and emotion feedback. In addition, W5100 was adopted for network connection to achieve information transmission via Internet, providing broad application prospects for the robot in the area of Internet of Things (IoT).

Integrative network alignment reveals large regions of global network similarity in yeast and human.

PubMed

Kuchaiev, Oleksii; Przulj, Natasa

2011-05-15

High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology. We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g. proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity and structural similarity. Hence, we resolve the ties in similarity measures and find a combination of similarity measures yielding the largest contiguous (i.e. connected) and biologically sound alignments. MI-GRAAL exposes the largest functional, connected regions of protein-protein interaction (PPI) network similarity to date: surprisingly, it reveals that 77.7% of proteins in the baker's yeast high-confidence PPI network participate in such a subnetwork that is fully contained in the human high-confidence PPI network. This is the first demonstration that species as diverse as yeast and human contain so large, continuous regions of global network similarity. We apply MI-GRAAL's alignments to predict functions of un-annotated proteins in yeast, human and bacteria validating our predictions in the literature. Furthermore, using network alignment scores for PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship. This is the first time that phylogeny is exactly reconstructed from purely topological alignments of PPI networks. Supplementary files and MI-GRAAL executables: http://bio-nets.doc.ic.ac.uk/MI-GRAAL/.

Multiple-Localization and Hub Proteins

PubMed Central

Ota, Motonori; Gonja, Hideki; Koike, Ryotaro; Fukuchi, Satoshi

2016-01-01

Protein-protein interactions are fundamental for all biological phenomena, and protein-protein interaction networks provide a global view of the interactions. The hub proteins, with many interaction partners, play vital roles in the networks. We investigated the subcellular localizations of proteins in the human network, and found that the ones localized in multiple subcellular compartments, especially the nucleus/cytoplasm proteins (NCP), the cytoplasm/cell membrane proteins (CMP), and the nucleus/cytoplasm/cell membrane proteins (NCMP), tend to be hubs. Examinations of keywords suggested that among NCP, those related to post-translational modifications and transcription functions are the major contributors to the large number of interactions. These types of proteins are characterized by a multi-domain architecture and intrinsic disorder. A survey of the typical hub proteins with prominent numbers of interaction partners in the type revealed that most are either transcription factors or co-regulators involved in signaling pathways. They translocate from the cytoplasm to the nucleus, triggered by the phosphorylation and/or ubiquitination of intrinsically disordered regions. Among CMP and NCMP, the contributors to the numerous interactions are related to either kinase or ubiquitin ligase activity. Many of them reside on the cytoplasmic side of the cell membrane, and act as the upstream regulators of signaling pathways. Overall, these hub proteins function to transfer external signals to the nucleus, through the cell membrane and the cytoplasm. Our analysis suggests that multiple-localization is a crucial concept to characterize groups of hub proteins and their biological functions in cellular information processing. PMID:27285823

Mapping Multiplex Hubs in Human Functional Brain Networks

PubMed Central

De Domenico, Manlio; Sasai, Shuntaro; Arenas, Alex

2016-01-01

Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. PMID:27471443

Toward Optimal Transport Networks

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Kincaid, Rex K.; Vargo, Erik P.

2008-01-01

Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.

A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks.

PubMed

Le, Duc-Hau

2015-01-01

Protein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist. In this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes. Comparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer. Our proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.

Alignment and integration of complex networks by hypergraph-based spectral clustering

NASA Astrophysics Data System (ADS)

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Alignment and integration of complex networks by hypergraph-based spectral clustering.

PubMed

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Structural principles within the human-virus protein-protein interaction network

PubMed Central

Franzosa, Eric A.; Xia, Yu

2011-01-01

General properties of the antagonistic biomolecular interactions between viruses and their hosts (exogenous interactions) remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host (endogenous interactions). Systems biology approaches have been applied to study the combined interaction networks of virus and human proteins, but such efforts have so far revealed only low-resolution patterns of host-virus interaction. Here, we layer curated and predicted 3D structural models of human-virus and human-human protein complexes on top of traditional interaction networks to reconstruct the human-virus structural interaction network. This approach reveals atomic resolution, mechanistic patterns of host-virus interaction, and facilitates systematic comparison with the host’s endogenous interactions. We find that exogenous interfaces tend to overlap with and mimic endogenous interfaces, thereby competing with endogenous binding partners. The endogenous interfaces mimicked by viral proteins tend to participate in multiple endogenous interactions which are transient and regulatory in nature. While interface overlap in the endogenous network results largely from gene duplication followed by divergent evolution, viral proteins frequently achieve interface mimicry without any sequence or structural similarity to an endogenous binding partner. Finally, while endogenous interfaces tend to evolve more slowly than the rest of the protein surface, exogenous interfaces—including many sites of endogenous-exogenous overlap—tend to evolve faster, consistent with an evolutionary “arms race” between host and pathogen. These significant biophysical, functional, and evolutionary differences between host-pathogen and within-host protein-protein interactions highlight the distinct consequences of antagonism versus cooperation in biological networks. PMID:21680884

Mixed-method Exploration of Social Network Links to Participation

PubMed Central

Kreider, Consuelo M.; Bendixen, Roxanna M.; Mann, William C.; Young, Mary Ellen; McCarty, Christopher

2015-01-01

The people who regularly interact with an adolescent form that youth's social network, which may impact participation. We investigated the relationship of social networks to participation using personal network analysis and individual interviews. The sample included 36 youth, age 11 – 16 years. Nineteen had diagnoses of learning disability, attention disorder, or high-functioning autism and 17 were typically developing. Network analysis yielded 10 network variables, of which 8 measured network composition and 2 measured network structure, with significant links to at least one measure of participation using the Children's Assessment of Participation and Enjoyment (CAPE). Interviews from youth in the clinical group yielded description of strategies used to negotiate social interactions, as well as processes and reasoning used to remain engaged within social networks. Findings contribute to understanding the ways social networks are linked to youth participation and suggest the potential of social network factors for predicting rehabilitation outcomes. PMID:26594737

Plasticity of genetic interactions in metabolic networks of yeast.

PubMed

Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

2007-02-13

Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.

Experimental evolution of protein–protein interaction networks

PubMed Central

Kaçar, Betül; Gaucher, Eric A.

2013-01-01

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

Aberrant within- and between-network connectivity of the mirror neuron system network and the mentalizing network in first episode psychosis.

PubMed

Choe, Eugenie; Lee, Tae Young; Kim, Minah; Hur, Ji-Won; Yoon, Youngwoo Bryan; Cho, Kang-Ik K; Kwon, Jun Soo

2018-03-26

It has been suggested that the mentalizing network and the mirror neuron system network support important social cognitive processes that are impaired in schizophrenia. However, the integrity and interaction of these two networks have not been sufficiently studied, and their effects on social cognition in schizophrenia remain unclear. Our study included 26 first-episode psychosis (FEP) patients and 26 healthy controls. We utilized resting-state functional connectivity to examine the a priori-defined mirror neuron system network and the mentalizing network and to assess the within- and between-network connectivities of the networks in FEP patients. We also assessed the correlation between resting-state functional connectivity measures and theory of mind performance. FEP patients showed altered within-network connectivity of the mirror neuron system network, and aberrant between-network connectivity between the mirror neuron system network and the mentalizing network. The within-network connectivity of the mirror neuron system network was noticeably correlated with theory of mind task performance in FEP patients. The integrity and interaction of the mirror neuron system network and the mentalizing network may be altered during the early stages of psychosis. Additionally, this study suggests that alterations in the integrity of the mirror neuron system network are highly related to deficient theory of mind in schizophrenia, and this problem would be present from the early stage of psychosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Airport Surface Network Architecture Definition

NASA Technical Reports Server (NTRS)

Nguyen, Thanh C.; Eddy, Wesley M.; Bretmersky, Steven C.; Lawas-Grodek, Fran; Ellis, Brenda L.

2006-01-01

Currently, airport surface communications are fragmented across multiple types of systems. These communication systems for airport operations at most airports today are based dedicated and separate architectures that cannot support system-wide interoperability and information sharing. The requirements placed upon the Communications, Navigation, and Surveillance (CNS) systems in airports are rapidly growing and integration is urgently needed if the future vision of the National Airspace System (NAS) and the Next Generation Air Transportation System (NGATS) 2025 concept are to be realized. To address this and other problems such as airport surface congestion, the Space Based Technologies Project s Surface ICNS Network Architecture team at NASA Glenn Research Center has assessed airport surface communications requirements, analyzed existing and future surface applications, and defined a set of architecture functions that will help design a scalable, reliable and flexible surface network architecture to meet the current and future needs of airport operations. This paper describes the systems approach or methodology to networking that was employed to assess airport surface communications requirements, analyze applications, and to define the surface network architecture functions as the building blocks or components of the network. The systems approach used for defining these functions is relatively new to networking. It is viewing the surface network, along with its environment (everything that the surface network interacts with or impacts), as a system. Associated with this system are sets of services that are offered by the network to the rest of the system. Therefore, the surface network is considered as part of the larger system (such as the NAS), with interactions and dependencies between the surface network and its users, applications, and devices. The surface network architecture includes components such as addressing/routing, network management, network performance and security.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

PubMed

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; Ng, Patrick; Khraiwesh, Basel; Jaiswal, Ashish; Jijakli, Kenan; Koussa, Joseph; Nelson, David R; Cai, Hong; Yang, Xinping; Chang, Roger L; Papin, Jason; Yu, Haiyuan; Balaji, Santhanam; Salehi-Ashtiani, Kourosh

2016-07-19

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE PAGES

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; ...

2016-06-14

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

RAID: a comprehensive resource for human RNA-associated (RNA-RNA/RNA-protein) interaction.

PubMed

Zhang, Xiaomeng; Wu, Deng; Chen, Liqun; Li, Xiang; Yang, Jinxurong; Fan, Dandan; Dong, Tingting; Liu, Mingyue; Tan, Puwen; Xu, Jintian; Yi, Ying; Wang, Yuting; Zou, Hua; Hu, Yongfei; Fan, Kaili; Kang, Juanjuan; Huang, Yan; Miao, Zhengqiang; Bi, Miaoman; Jin, Nana; Li, Kongning; Li, Xia; Xu, Jianzhen; Wang, Dong

2014-07-01

Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA-RNA/RNA-protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA-RNA interactions and 1619 RNA-protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA-RNA/RNA-protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA-RNA/RNA-protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network. © 2014 Zhang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

PubMed

Liu, Lizhen; Sun, Xiaowu; Song, Wei; Du, Chao

2018-06-01

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

Network Compression as a Quality Measure for Protein Interaction Networks

PubMed Central

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

A Collaborative Knowledge Plane for Autonomic Networks

NASA Astrophysics Data System (ADS)

Mbaye, Maïssa; Krief, Francine

Autonomic networking aims to give network components self-managing capabilities. Several autonomic architectures have been proposed. Each of these architectures includes sort of a knowledge plane which is very important to mimic an autonomic behavior. Knowledge plane has a central role for self-functions by providing suitable knowledge to equipment and needs to learn new strategies for more accuracy.However, defining knowledge plane's architecture is still a challenge for researchers. Specially, defining the way cognitive supports interact each other in knowledge plane and implementing them. Decision making process depends on these interactions between reasoning and learning parts of knowledge plane. In this paper we propose a knowledge plane's architecture based on machine learning (inductive logic programming) paradigm and situated view to deal with distributed environment. This architecture is focused on two self-functions that include all other self-functions: self-adaptation and self-organization. Study cases are given and implemented.

The brain's default network: anatomy, function, and relevance to disease.

PubMed

Buckner, Randy L; Andrews-Hanna, Jessica R; Schacter, Daniel L

2008-03-01

Thirty years of brain imaging research has converged to define the brain's default network-a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.

Finding Correlation between Protein Protein Interaction Modules Using Semantic Web Techniques

NASA Astrophysics Data System (ADS)

Kargar, Mehdi; Moaven, Shahrouz; Abolhassani, Hassan

Many complex networks such as social networks and computer show modular structures, where edges between nodes are much denser within modules than between modules. It is strongly believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. In this paper we used a human curated dataset. In this paper we consider each module in the PPI network as ontology. Using techniques in ontology alignment, we compare each pair of modules in the network. We want to see that is there a correlation between the structure of each module or they have totally different structures. Our results show that there is no correlation between proteins in a protein protein interaction network.

System analysis identifies distinct and common functional networks governed by transcription factor ASCL1, in glioma and small cell lung cancer.

PubMed

Donakonda, Sainitin; Sinha, Swati; Dighe, Shrinivas Nivrutti; Rao, Manchanahalli R Satyanarayana

2017-07-25

ASCL1 is a basic Helix-Loop-Helix transcription factor (TF), which is involved in various cellular processes like neuronal development and signaling pathways. Transcriptome profiling has shown that ASCL1 overexpression plays an important role in the development of glioma and Small Cell Lung Carcinoma (SCLC), but distinct and common molecular mechanisms regulated by ASCL1 in these cancers are unknown. In order to understand how it drives the cellular functional network in these two tumors, we generated a gene expression profile in a glioma cell line (U87MG) to identify ASCL1 gene targets by an si RNA silencing approach and then compared this with a publicly available dataset of similarly silenced SCLC (NCI-H1618 cells). We constructed TF-TF and gene-gene interactions, as well as protein interaction networks of ASCL1 regulated genes in glioma and SCLC cells. Detailed network analysis uncovered various biological processes governed by ASCL1 target genes in these two tumor cell lines. We find that novel ASCL1 functions related to mitosis and signaling pathways influencing development and tumor growth are affected in both glioma and SCLC cells. In addition, we also observed ASCL1 governed functional networks that are distinct to glioma and SCLC.

Percolation on shopping and cashback electronic commerce networks

NASA Astrophysics Data System (ADS)

Fu, Tao; Chen, Yini; Qin, Zhen; Guo, Liping

2013-06-01

Many realistic networks live in the form of multiple networks, including interacting networks and interdependent networks. Here we study percolation properties of a special kind of interacting networks, namely Shopping and Cashback Electronic Commerce Networks (SCECNs). We investigate two actual SCECNs to extract their structural properties, and develop a mathematical framework based on generating functions for analyzing directed interacting networks. Then we derive the necessary and sufficient condition for the absence of the system-wide giant in- and out- component, and propose arithmetic to calculate the corresponding structural measures in the sub-critical and supercritical regimes. We apply our mathematical framework and arithmetic to those two actual SCECNs to observe its accuracy, and give some explanations on the discrepancies. We show those structural measures based on our mathematical framework and arithmetic are useful to appraise the status of SCECNs. We also find that the supercritical regime of the whole network is maintained mainly by hyperlinks between different kinds of websites, while those hyperlinks between the same kinds of websites can only enlarge the sizes of in-components and out-components.

GENOME-WIDE GENETIC INTERACTION ANALYSIS OF GLAUCOMA USING EXPERT KNOWLEDGE DERIVED FROM HUMAN PHENOTYPE NETWORKS

PubMed Central

HU, TING; DARABOS, CHRISTIAN; CRICCO, MARIA E.; KONG, EMILY; MOORE, JASON H.

2014-01-01

The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest. In this study, we use the GLAUGEN glaucoma GWAS dataset and apply the HPN as a biological knowledge-based filter to prioritize genetic variants. Then, we use the statistical epistasis network (SEN) to identify a significant connected network of pairwise epistatic interactions among the prioritized SNPs. These clearly highlight the complex genetic basis of glaucoma. Furthermore, we identify key SNPs by quantifying structural network characteristics. Through functional annotation of these key SNPs using Biofilter, a software accessing multiple publicly available human genetic data sources, we find supporting biomedical evidences linking glaucoma to an array of genetic diseases, proving our concept. We conclude by suggesting hypotheses for a better understanding of the disease. PMID:25592582

Hacking the Cell: Network Intrusion and Exploitation by Adenovirus E1A.

PubMed

King, Cason R; Zhang, Ali; Tessier, Tanner M; Gameiro, Steven F; Mymryk, Joe S

2018-05-01

As obligate intracellular parasites, viruses are dependent on their infected hosts for survival. Consequently, viruses are under enormous selective pressure to utilize available cellular components and processes to their own advantage. As most, if not all, cellular activities are regulated at some level via protein interactions, host protein interaction networks are particularly vulnerable to viral exploitation. Indeed, viral proteins frequently target highly connected "hub" proteins to "hack" the cellular network, defining the molecular basis for viral control over the host. This widespread and successful strategy of network intrusion and exploitation has evolved convergently among numerous genetically distinct viruses as a result of the endless evolutionary arms race between pathogens and hosts. Here we examine the means by which a particularly well-connected viral hub protein, human adenovirus E1A, compromises and exploits the vulnerabilities of eukaryotic protein interaction networks. Importantly, these interactions identify critical regulatory hubs in the human proteome and help define the molecular basis of their function. Copyright © 2018 King et al.

Hacking the Cell: Network Intrusion and Exploitation by Adenovirus E1A

PubMed Central

King, Cason R.; Zhang, Ali; Tessier, Tanner M.; Gameiro, Steven F.

2018-01-01

ABSTRACT As obligate intracellular parasites, viruses are dependent on their infected hosts for survival. Consequently, viruses are under enormous selective pressure to utilize available cellular components and processes to their own advantage. As most, if not all, cellular activities are regulated at some level via protein interactions, host protein interaction networks are particularly vulnerable to viral exploitation. Indeed, viral proteins frequently target highly connected “hub” proteins to “hack” the cellular network, defining the molecular basis for viral control over the host. This widespread and successful strategy of network intrusion and exploitation has evolved convergently among numerous genetically distinct viruses as a result of the endless evolutionary arms race between pathogens and hosts. Here we examine the means by which a particularly well-connected viral hub protein, human adenovirus E1A, compromises and exploits the vulnerabilities of eukaryotic protein interaction networks. Importantly, these interactions identify critical regulatory hubs in the human proteome and help define the molecular basis of their function. PMID:29717008

Common and distinct changes of default mode and salience network in schizophrenia and major depression.

PubMed

Shao, Junming; Meng, Chun; Tahmasian, Masoud; Brandl, Felix; Yang, Qinli; Luo, Guangchun; Luo, Cheng; Yao, Dezhong; Gao, Lianli; Riedl, Valentin; Wohlschläger, Afra; Sorg, Christian

2018-02-19

Brain imaging reveals schizophrenia as a disorder of macroscopic brain networks. In particular, default mode and salience network (DMN, SN) show highly consistent alterations in both interacting brain activity and underlying brain structure. However, the same networks are also altered in major depression. This overlap in network alterations induces the question whether DMN and SN changes are different across both disorders, potentially indicating distinct underlying pathophysiological mechanisms. To address this question, we acquired T1-weighted, diffusion-weighted, and resting-state functional MRI in patients with schizophrenia, patients with major depression, and healthy controls. We measured regional gray matter volume, inter-regional structural and intrinsic functional connectivity of DMN and SN, and compared these measures across groups by generalized Wilcoxon rank tests, while controlling for symptoms and medication. When comparing patients with controls, we found in each patient group SN volume loss, impaired DMN structural connectivity, and aberrant DMN and SN functional connectivity. When comparing patient groups, SN gray matter volume loss and DMN structural connectivity reduction did not differ between groups, but in schizophrenic patients, functional hyperconnectivity between DMN and SN was less in comparison to depressed patients. Results provide evidence for distinct functional hyperconnectivity between DMN and SN in schizophrenia and major depression, while structural changes in DMN and SN were similar. Distinct hyperconnectivity suggests different pathophysiological mechanism underlying aberrant DMN-SN interactions in schizophrenia and depression.

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

PubMed

Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

2017-07-15

There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity. Copyright © 2017 Elsevier Inc. All rights reserved.

The Missing Part of Seed Dispersal Networks: Structure and Robustness of Bat-Fruit Interactions

PubMed Central

Mello, Marco Aurelio Ribeiro; Marquitti, Flávia Maria Darcie; Guimarães, Paulo Roberto; Kalko, Elisabeth Klara Viktoria; Jordano, Pedro; de Aguiar, Marcus Aloizio Martinez

2011-01-01

Mutualistic networks are crucial to the maintenance of ecosystem services. Unfortunately, what we know about seed dispersal networks is based only on bird-fruit interactions. Therefore, we aimed at filling part of this gap by investigating bat-fruit networks. It is known from population studies that: (i) some bat species depend more on fruits than others, and (ii) that some specialized frugivorous bats prefer particular plant genera. We tested whether those preferences affected the structure and robustness of the whole network and the functional roles of species. Nine bat-fruit datasets from the literature were analyzed and all networks showed lower complementary specialization (H2' = 0.37±0.10, mean ± SD) and similar nestedness (NODF = 0.56±0.12) than pollination networks. All networks were modular (M = 0.32±0.07), and had on average four cohesive subgroups (modules) of tightly connected bats and plants. The composition of those modules followed the genus-genus associations observed at population level (Artibeus-Ficus, Carollia-Piper, and Sturnira-Solanum), although a few of those plant genera were dispersed also by other bats. Bat-fruit networks showed high robustness to simulated cumulative removals of both bats (R = 0.55±0.10) and plants (R = 0.68±0.09). Primary frugivores interacted with a larger proportion of the plants available and also occupied more central positions; furthermore, their extinction caused larger changes in network structure. We conclude that bat-fruit networks are highly cohesive and robust mutualistic systems, in which redundancy is high within modules, although modules are complementary to each other. Dietary specialization seems to be an important structuring factor that affects the topology, the guild structure and functional roles in bat-fruit networks. PMID:21386981

The missing part of seed dispersal networks: structure and robustness of bat-fruit interactions.

PubMed

Mello, Marco Aurelio Ribeiro; Marquitti, Flávia Maria Darcie; Guimarães, Paulo Roberto; Kalko, Elisabeth Klara Viktoria; Jordano, Pedro; de Aguiar, Marcus Aloizio Martinez

2011-02-28

Mutualistic networks are crucial to the maintenance of ecosystem services. Unfortunately, what we know about seed dispersal networks is based only on bird-fruit interactions. Therefore, we aimed at filling part of this gap by investigating bat-fruit networks. It is known from population studies that: (i) some bat species depend more on fruits than others, and (ii) that some specialized frugivorous bats prefer particular plant genera. We tested whether those preferences affected the structure and robustness of the whole network and the functional roles of species. Nine bat-fruit datasets from the literature were analyzed and all networks showed lower complementary specialization (H(2)' = 0.37±0.10, mean ± SD) and similar nestedness (NODF = 0.56±0.12) than pollination networks. All networks were modular (M = 0.32±0.07), and had on average four cohesive subgroups (modules) of tightly connected bats and plants. The composition of those modules followed the genus-genus associations observed at population level (Artibeus-Ficus, Carollia-Piper, and Sturnira-Solanum), although a few of those plant genera were dispersed also by other bats. Bat-fruit networks showed high robustness to simulated cumulative removals of both bats (R = 0.55±0.10) and plants (R = 0.68±0.09). Primary frugivores interacted with a larger proportion of the plants available and also occupied more central positions; furthermore, their extinction caused larger changes in network structure. We conclude that bat-fruit networks are highly cohesive and robust mutualistic systems, in which redundancy is high within modules, although modules are complementary to each other. Dietary specialization seems to be an important structuring factor that affects the topology, the guild structure and functional roles in bat-fruit networks.

Chromatin interaction networks revealed unique connectivity patterns of broad H3K4me3 domains and super enhancers in 3D chromatin.

PubMed

Thibodeau, Asa; Márquez, Eladio J; Shin, Dong-Guk; Vera-Licona, Paola; Ucar, Duygu

2017-10-31

Broad domain promoters and super enhancers are regulatory elements that govern cell-specific functions and harbor disease-associated sequence variants. These elements are characterized by distinct epigenomic profiles, such as expanded deposition of histone marks H3K27ac for super enhancers and H3K4me3 for broad domains, however little is known about how they interact with each other and the rest of the genome in three-dimensional chromatin space. Using network theory methods, we studied chromatin interactions between broad domains and super enhancers in three ENCODE cell lines (K562, MCF7, GM12878) obtained via ChIA-PET, Hi-C, and Hi-CHIP assays. In these networks, broad domains and super enhancers interact more frequently with each other compared to their typical counterparts. Network measures and graphlets revealed distinct connectivity patterns associated with these regulatory elements that are robust across cell types and alternative assays. Machine learning models showed that these connectivity patterns could effectively discriminate broad domains from typical promoters and super enhancers from typical enhancers. Finally, targets of broad domains in these networks were enriched in disease-causing SNPs of cognate cell types. Taken together these results suggest a robust and unique organization of the chromatin around broad domains and super enhancers: loci critical for pathologies and cell-specific functions.

Network representations of immune system complexity

PubMed Central

Subramanian, Naeha; Torabi-Parizi, Parizad; Gottschalk, Rachel A.; Germain, Ronald N.; Dutta, Bhaskar

2015-01-01

The mammalian immune system is a dynamic multi-scale system composed of a hierarchically organized set of molecular, cellular and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein-protein interactions underlying intracellular signaling pathways and single cell responses to increasingly complex networks of in vivo cellular interaction, positioning and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather non-linear behaviors arising from dynamic, feedback-regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multi-scale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular and organism-level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. PMID:25625853

Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke.

PubMed

Zhao, Zhiyong; Wu, Jie; Fan, Mingxia; Yin, Dazhi; Tang, Chaozheng; Gong, Jiayu; Xu, Guojun; Gao, Xinjie; Yu, Qiurong; Yang, Hao; Sun, Limin; Jia, Jie

2018-04-24

Motor functions are supported through functional integration across the extended motor system network. Individuals following stroke often show deficits on motor performance requiring coordination of multiple brain networks; however, the assessment of connectivity patterns after stroke was still unclear. This study aimed to investigate the changes in intra- and inter-network functional connectivity (FC) of multiple networks following stroke and further correlate FC with motor performance. Thirty-three left subcortical chronic stroke patients and 34 healthy controls underwent resting-state functional magnetic resonance imaging. Eleven resting-state networks were identified via independent component analysis (ICA). Compared with healthy controls, the stroke group showed abnormal FC within the motor network (MN), visual network (VN), dorsal attention network (DAN), and executive control network (ECN). Additionally, the FC values of the ipsilesional inferior parietal lobule (IPL) within the ECN were negatively correlated with the Fugl-Meyer Assessment (FMA) scores (hand + wrist). With respect to inter-network interactions, the ipsilesional frontoparietal network (FPN) decreased FC with the MN and DAN; the contralesional FPN decreased FC with the ECN, but it increased FC with the default mode network (DMN); and the posterior DMN decreased FC with the VN. In sum, this study demonstrated the coexistence of intra- and inter-network alterations associated with motor-visual attention and high-order cognitive control function in chronic stroke, which might provide insights into brain network plasticity following stroke. © 2018 Wiley Periodicals, Inc.

Functional network connectivity underlying food processing: disturbed salience and visual processing in overweight and obese adults.

PubMed

Kullmann, Stephanie; Pape, Anna-Antonia; Heni, Martin; Ketterer, Caroline; Schick, Fritz; Häring, Hans-Ulrich; Fritsche, Andreas; Preissl, Hubert; Veit, Ralf

2013-05-01

In order to adequately explore the neurobiological basis of eating behavior of humans and their changes with body weight, interactions between brain areas or networks need to be investigated. In the current functional magnetic resonance imaging study, we examined the modulating effects of stimulus category (food vs. nonfood), caloric content of food, and body weight on the time course and functional connectivity of 5 brain networks by means of independent component analysis in healthy lean and overweight/obese adults. These functional networks included motor sensory, default-mode, extrastriate visual, temporal visual association, and salience networks. We found an extensive modulation elicited by food stimuli in the 2 visual and salience networks, with a dissociable pattern in the time course and functional connectivity between lean and overweight/obese subjects. Specifically, only in lean subjects, the temporal visual association network was modulated by the stimulus category and the salience network by caloric content, whereas overweight and obese subjects showed a generalized augmented response in the salience network. Furthermore, overweight/obese subjects showed changes in functional connectivity in networks important for object recognition, motivational salience, and executive control. These alterations could potentially lead to top-down deficiencies driving the overconsumption of food in the obese population.

Investigation of the multifunctional gene AOP3 expands the regulatory network fine-tuning glucosinolate production in Arabidopsis

PubMed Central

Jensen, Lea M.; Kliebenstein, Daniel J.; Burow, Meike

2015-01-01

Quantitative trait loci (QTL) mapping studies enable identification of loci that are part of regulatory networks controlling various phenotypes. Detailed investigations of genes within these loci are required to ultimately understand the function of individual genes and how they interact with other players in the network. In this study, we use transgenic plants in combination with natural variation to investigate the regulatory role of the AOP3 gene found in GS-AOP locus previously suggested to contribute to the regulation of glucosinolate defense compounds. Phenotypic analysis and QTL mapping in F2 populations with different AOP3 transgenes support that the enzymatic function and the AOP3 RNA both play a significant role in controlling glucosinolate accumulation. Furthermore, we find different loci interacting with either the enzymatic activity or the RNA of AOP3 and thereby extend the regulatory network controlling glucosinolate accumulation. PMID:26442075

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

PubMed

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-09-07

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

PubMed Central

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-01-01

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

Network mechanisms of intentional learning

PubMed Central

Hampshire, Adam; Hellyer, Peter J.; Parkin, Beth; Hiebert, Nole; MacDonald, Penny; Owen, Adrian M.; Leech, Robert; Rowe, James

2016-01-01

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated. PMID:26658925

Hippocampal Network Modularity Is Associated With Relational Memory Dysfunction in Schizophrenia.

PubMed

Avery, Suzanne N; Rogers, Baxter P; Heckers, Stephan

2018-05-01

Functional dysconnectivity has been proposed as a major pathophysiological mechanism for cognitive dysfunction in schizophrenia. The hippocampus is a focal point of dysconnectivity in schizophrenia, with decreased hippocampal functional connectivity contributing to the marked memory deficits observed in patients. Normal memory function relies on the interaction of complex corticohippocampal networks. However, only recent technological advances have enabled the large-scale exploration of functional networks with accuracy and precision. We investigated the modularity of hippocampal resting-state functional networks in a sample of 45 patients with schizophrenia spectrum disorders and 38 healthy control subjects. Modularity was calculated for two distinct functional networks: a core hippocampal-medial temporal lobe cortex network and an extended hippocampal-cortical network. As hippocampal function differs along its longitudinal axis, follow-up analyses examined anterior and posterior networks separately. To explore effects of resting network function on behavior, we tested associations between modularity and relational memory ability. Age, sex, handedness, and parental education were similar between groups. Network modularity was lower in schizophrenia patients, especially in the posterior hippocampal network. Schizophrenia patients also showed markedly lower relational memory ability compared with control subjects. We found a distinct brain-behavior relationship in schizophrenia that differed from control subjects by network and anterior/posterior division-while relational memory in control subjects was associated with anterior hippocampal-cortical modularity, schizophrenia patients showed an association with posterior hippocampal-medial temporal lobe cortex network modularity. Our findings support a model of abnormal resting-state corticohippocampal network coherence in schizophrenia, which may contribute to relational memory deficits. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Model-free inference of direct network interactions from nonlinear collective dynamics.

PubMed

Casadiego, Jose; Nitzan, Mor; Hallerberg, Sarah; Timme, Marc

2017-12-19

The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

More than a meal: integrating non-feeding interactions into food webs

USGS Publications Warehouse

Kéfi, Sonia; Berlow, Eric L.; Wieters, Evie A.; Navarrete, Sergio A.; Petchey, Owen L.; Wood, Spencer A.; Boit, Alice; Joppa, Lucas N.; Lafferty, Kevin D.; Williams, Richard J.; Martinez, Neo D.; Menge, Bruce A.; Blanchette, Carol A.; Iles, Alison C.; Brose, Ulrich

2012-01-01

Organisms eating each other are only one of many types of well documented and important interactions among species. Other such types include habitat modification, predator interference and facilitation. However, ecological network research has been typically limited to either pure food webs or to networks of only a few (<3) interaction types. The great diversity of non-trophic interactions observed in nature has been poorly addressed by ecologists and largely excluded from network theory. Herein, we propose a conceptual framework that organises this diversity into three main functional classes defined by how they modify specific parameters in a dynamic food web model. This approach provides a path forward for incorporating non-trophic interactions in traditional food web models and offers a new perspective on tackling ecological complexity that should stimulate both theoretical and empirical approaches to understanding the patterns and dynamics of diverse species interactions in nature.

Reorganization of Functional Connectivity as a Correlate of Cognitive Recovery in Acquired Brain Injury

ERIC Educational Resources Information Center

Castellanos, Nazareth P.; Paul, Nuria; Ordonez, Victoria E.; Demuynck, Olivier; Bajo, Ricardo; Campo, Pablo; Bilbao, Alvaro; Ortiz, Tomas; del-Pozo, Francisco; Maestu, Fernando

2010-01-01

Cognitive processes require a functional interaction between specialized multiple, local and remote brain regions. Although these interactions can be strongly altered by an acquired brain injury, brain plasticity allows network reorganization to be principally responsible for recovery. The present work evaluates the impact of brain injury on…

Quantification of Degeneracy in Biological Systems for Characterization of Functional Interactions Between Modules

PubMed Central

Li, Yao; Dwivedi, Gaurav; Huang, Wen; Yi, Yingfei

2012-01-01

There is an evolutionary advantage in having multiple components with overlapping functionality (i.e degeneracy) in organisms. While theoretical considerations of degeneracy have been well established in neural networks using information theory, the same concepts have not been developed for differential systems, which form the basis of many biochemical reaction network descriptions in systems biology. Here we establish mathematical definitions of degeneracy, complexity and robustness that allow for the quantification of these properties in a system. By exciting a dynamical system with noise, the mutual information associated with a selected observable output and the interacting subspaces of input components can be used to define both complexity and degeneracy. The calculation of degeneracy in a biological network is a useful metric for evaluating features such as the sensitivity of a biological network to environmental evolutionary pressure. Using a two-receptor signal transduction network, we find that redundant components will not yield high degeneracy whereas compensatory mechanisms established by pathway crosstalk will. This form of analysis permits interrogation of large-scale differential systems for non-identical, functionally equivalent features that have evolved to maintain homeostasis during disruption of individual components. PMID:22619750

In-silico studies of neutral drift for functional protein interaction networks

NASA Astrophysics Data System (ADS)

Ali, Md Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

We have developed a minimal physically-motivated model of protein-protein interaction networks. Our system consists of two classes of enzymes, activators (e.g. kinases) and deactivators (e.g. phosphatases), and the enzyme-mediated activation/deactivation rates are determined by sequence-dependent binding strengths between enzymes and their targets. The network is evolved by introducing random point mutations in the binding sequences where we assume that each new mutation is either fixed or entirely lost. We apply this model to studies of neutral drift in networks that yield oscillatory dynamics, where we start, for example, with a relatively simple network and allow it to evolve by adding nodes and connections while requiring that dynamics be conserved. Our studies demonstrate both the importance of employing a sequence-based evolutionary scheme and the relative rapidity (in evolutionary time) for the redistribution of function over new nodes via neutral drift. Surprisingly, in addition to this redistribution time we discovered another much slower timescale for network evolution, reflecting hidden order in sequence space that we interpret in terms of sparsely connected domains.

Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function.

PubMed

Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A

2014-05-01

Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. Twenty-four hours of abstinence vs smoking satiety. Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

Modulation of steady state functional connectivity in the default mode and working memory networks by cognitive load.

PubMed

Newton, Allen T; Morgan, Victoria L; Rogers, Baxter P; Gore, John C

2011-10-01

Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady-state verbal identity N-back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. To test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks. Copyright © 2010 Wiley-Liss, Inc.

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

PubMed Central

Shi, Xiao-He; Hu, Le-Le; Kong, Xiangyin; Cai, Yu-Dong; Chou, Kuo-Chen

2010-01-01

Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging. PMID:20300175

Protein complex prediction for large protein protein interaction networks with the Core&Peel method.

PubMed

Pellegrini, Marco; Baglioni, Miriam; Geraci, Filippo

2016-11-08

Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed. We present a new fast algorithm for clustering large sparse networks: Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G (which is roughly proportional to the maximum average degree of any induced subgraph in G). We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms. Our algorithm Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks.

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks

PubMed Central

Ruiz-Rizzo, Adriana L.; Neitzel, Julia; Müller, Hermann J.; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity. PMID:29662444

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks.

PubMed

Ruiz-Rizzo, Adriana L; Neitzel, Julia; Müller, Hermann J; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's "theory of visual attention" (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity.

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network

PubMed Central

Usaj, Matej; Tan, Yizhao; Wang, Wen; VanderSluis, Benjamin; Zou, Albert; Myers, Chad L.; Costanzo, Michael; Andrews, Brenda; Boone, Charles

2017-01-01

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner. PMID:28325812

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network.

PubMed

Usaj, Matej; Tan, Yizhao; Wang, Wen; VanderSluis, Benjamin; Zou, Albert; Myers, Chad L; Costanzo, Michael; Andrews, Brenda; Boone, Charles

2017-05-05

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner. Copyright © 2017 Usaj et al.

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

PubMed

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.

Optimal network alignment with graphlet degree vectors.

PubMed

Milenković, Tijana; Ng, Weng Leong; Hayes, Wayne; Przulj, Natasa

2010-06-30

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.

Emergence of clustering in an acquaintance model without homophily

NASA Astrophysics Data System (ADS)

Bhat, Uttam; Krapivsky, P. L.; Redner, S.

2014-11-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

Organization and hierarchy of the human functional brain network lead to a chain-like core.

PubMed

Mastrandrea, Rossana; Gabrielli, Andrea; Piras, Fabrizio; Spalletta, Gianfranco; Caldarelli, Guido; Gili, Tommaso

2017-07-07

The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming a highly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions.

Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study.

PubMed

Tian, Lixia; Wang, Jinhui; Yan, Chaogan; He, Yong

2011-01-01

We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition. Copyright © 2010 Elsevier Inc. All rights reserved.

SH3 interactome conserves general function over specific form

PubMed Central

Xin, Xiaofeng; Gfeller, David; Cheng, Jackie; Tonikian, Raffi; Sun, Lin; Guo, Ailan; Lopez, Lianet; Pavlenco, Alevtina; Akintobi, Adenrele; Zhang, Yingnan; Rual, Jean-François; Currell, Bridget; Seshagiri, Somasekar; Hao, Tong; Yang, Xinping; Shen, Yun A; Salehi-Ashtiani, Kourosh; Li, Jingjing; Cheng, Aaron T; Bouamalay, Dryden; Lugari, Adrien; Hill, David E; Grimes, Mark L; Drubin, David G; Grant, Barth D; Vidal, Marc; Boone, Charles; Sidhu, Sachdev S; Bader, Gary D

2013-01-01

Src homology 3 (SH3) domains bind peptides to mediate protein–protein interactions that assemble and regulate dynamic biological processes. We surveyed the repertoire of SH3 binding specificity using peptide phage display in a metazoan, the worm Caenorhabditis elegans, and discovered that it structurally mirrors that of the budding yeast Saccharomyces cerevisiae. We then mapped the worm SH3 interactome using stringent yeast two-hybrid and compared it with the equivalent map for yeast. We found that the worm SH3 interactome resembles the analogous yeast network because it is significantly enriched for proteins with roles in endocytosis. Nevertheless, orthologous SH3 domain-mediated interactions are highly rewired. Our results suggest a model of network evolution where general function of the SH3 domain network is conserved over its specific form. PMID:23549480

Bacterial Unculturability and the Formation of Intercellular Metabolic Networks.

PubMed

Pande, Samay; Kost, Christian

2017-05-01

The majority of known bacterial species cannot be cultivated under laboratory conditions. Here we argue that the adaptive emergence of obligate metabolic interactions in natural bacterial communities can explain this pattern. Bacteria commonly release metabolites into the external environment. Accumulating pools of extracellular metabolites create an ecological niche that benefits auxotrophic mutants, which have lost the ability to autonomously produce the corresponding metabolites. In addition to a diffusion-based metabolite transfer, auxotrophic cells can use contact-dependent means to obtain nutrients from other co-occurring cells. Spatial colocalisation and a continuous coevolution further increase the nutritional dependency and optimise fluxes through combined metabolic networks. Thus, bacteria likely function as networks of interacting cells that reciprocally exchange nutrients and biochemical functions rather than as physiologically autonomous units. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural Correlates of Personal Space Intrusion

PubMed Central

Cassidy, Brittany S.; Yue, Xiaomin; Rauch, Scott L.; Boeke, Emily A.; Nasr, Shahin; Tootell, Roger B. H.; Coombs, Garth

2014-01-01

A parietal-frontal network in primates is thought to support many behaviors occurring in the space around the body, including interpersonal interactions and maintenance of a particular “comfort zone” or distance from other people (“personal space”). To better understand this network in humans, we used functional MRI to measure the responses to moving objects (faces, cars, simple spheres) and the functional connectivity of two regions in this network, the dorsal intraparietal sulcus (DIPS) and the ventral premotor cortex (PMv). We found that both areas responded more strongly to faces that were moving toward (vs away from) subjects, but did not show this bias in response to comparable motion in control stimuli (cars or spheres). Moreover, these two regions were functionally interconnected. Tests of activity-behavior associations revealed that the strength of DIPS-PMv connectivity was correlated with the preferred distance that subjects chose to stand from an unfamiliar person (personal space size). In addition, the magnitude of DIPS and PMv responses was correlated with the preferred level of social activity. Together, these findings suggest that this parietal-frontal network plays a role in everyday interactions with others. PMID:24647934

Analyzing and interpreting genome data at the network level with ConsensusPathDB.

PubMed

Herwig, Ralf; Hardt, Christopher; Lienhard, Matthias; Kamburov, Atanas

2016-10-01

ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.

Graph-theoretic quantum system modelling for neuronal microtubules as hierarchical clustered quantum Hopfield networks

NASA Astrophysics Data System (ADS)

Srivastava, D. P.; Sahni, V.; Satsangi, P. S.

2014-08-01

Graph-theoretic quantum system modelling (GTQSM) is facilitated by considering the fundamental unit of quantum computation and information, viz. a quantum bit or qubit as a basic building block. Unit directional vectors "ket 0" and "ket 1" constitute two distinct fundamental quantum across variable orthonormal basis vectors, for the Hilbert space, specifying the direction of propagation of information, or computation data, while complementary fundamental quantum through, or flow rate, variables specify probability parameters, or amplitudes, as surrogates for scalar quantum information measure (von Neumann entropy). This paper applies GTQSM in continuum of protein heterodimer tubulin molecules of self-assembling polymers, viz. microtubules in the brain as a holistic system of interacting components representing hierarchical clustered quantum Hopfield network, hQHN, of networks. The quantum input/output ports of the constituent elemental interaction components, or processes, of tunnelling interactions and Coulombic bidirectional interactions are in cascade and parallel interconnections with each other, while the classical output ports of all elemental components are interconnected in parallel to accumulate micro-energy functions generated in the system as Hamiltonian, or Lyapunov, energy function. The paper presents an insight, otherwise difficult to gain, for the complex system of systems represented by clustered quantum Hopfield network, hQHN, through the application of GTQSM construct.

Prediction of cassava protein interactome based on interolog method.

PubMed

Thanasomboon, Ratana; Kalapanulak, Saowalak; Netrphan, Supatcharee; Saithong, Treenut

2017-12-08

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi ). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis.

PubMed

Ding, Xiao Pan; Wu, Si Jia; Liu, Jiangang; Fu, Genyue; Lee, Kang

2017-09-21

The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children's functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children's cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception.

Intrinsic protective mechanisms of the neuron-glia network against glioma invasion.

PubMed

Iwadate, Yasuo; Fukuda, Kazumasa; Matsutani, Tomoo; Saeki, Naokatsu

2016-04-01

Gliomas arising in the brain parenchyma infiltrate into the surrounding brain and break down established complex neuron-glia networks. However, mounting evidence suggests that initially the network microenvironment of the adult central nervous system (CNS) is innately non-permissive to glioma cell invasion. The main players are inhibitory molecules in CNS myelin, as well as proteoglycans associated with astrocytes. Neural stem cells, and neurons themselves, possess inhibitory functions against neighboring tumor cells. These mechanisms have evolved to protect the established neuron-glia network, which is necessary for brain function. Greater insight into the interaction between glioma cells and the surrounding neuron-glia network is crucial for developing new therapies for treating these devastating tumors while preserving the important and complex neural functions of patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Remote Synchronization Reveals Network Symmetries and Functional Modules

NASA Astrophysics Data System (ADS)

Nicosia, Vincenzo; Valencia, Miguel; Chavez, Mario; Díaz-Guilera, Albert; Latora, Vito

2013-04-01

We study a Kuramoto model in which the oscillators are associated with the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result, and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

Functionally specialised birds respond flexibly to seasonal changes in fruit availability.

PubMed

Bender, Irene M A; Kissling, W Daniel; Böhning-Gaese, Katrin; Hensen, Isabell; Kühn, Ingolf; Wiegand, Thorsten; Dehling, D Matthias; Schleuning, Matthias

2017-07-01

Interactions between resource and consumer species result in complex ecological networks. The overall structure of these networks is often stable in space and time, but little is known about the temporal stability of the functional roles of consumer species in these networks. We used a trait-based approach to investigate whether consumers (frugivorous birds) show similar degrees of functional specialisation on resources (plants) in ecological networks across seasons. We additionally tested whether closely related bird species have similar degrees of functional specialisation and whether birds that are functionally specialised on specific resource types within a season are flexible in switching to other resource types in other seasons. We analysed four seasonal replicates of two species-rich plant-frugivore networks from the tropical Andes. To quantify fruit preferences of frugivorous birds, we projected their interactions with plants into a multidimensional plant trait space. To measure functional specialisation of birds, we calculated a species' functional niche breadth (the extent of seasonal plant trait space utilised by a particular bird) and functional originality (the extent to which a bird species' fruit preference functionally differs from those of other species in a seasonal network). We additionally calculated functional flexibility, i.e. the ability of bird species to change their fruit preference across seasons in response to variation in plant resources. Functional specialisation of bird species varied more among species than across seasons, and phylogenetically similar bird species showed similar degrees of functional niche breadth (phylogenetic signal λ = 0·81) and functional originality (λ = 0·89). Additionally, we found that birds with high functional flexibility across seasons had narrow functional niche breadth and high functional originality per season, suggesting that birds that are seasonally specialised on particular resources are most flexible in switching to other fruit resources across seasons. The high flexibility of functionally specialised bird species to switch seasonally to other resources challenges the view that consumer species rely on functionally similar resources throughout the year. This flexibility of consumer species may be an important, but widely neglected mechanism that could potentially stabilise consumer-resource networks in response to human disturbance and environmental change. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Collapse of resilience patterns in generalized Lotka-Volterra dynamics and beyond.

PubMed

Tu, Chengyi; Grilli, Jacopo; Schuessler, Friedrich; Suweis, Samir

2017-06-01

Recently, a theoretical framework aimed at separating the roles of dynamics and topology in multidimensional systems has been developed [Gao et al., Nature (London) 530, 307 (2016)10.1038/nature16948]. The validity of their method is assumed to hold depending on two main hypotheses: (i) The network determined by the the interaction between pairs of nodes has negligible degree correlations; (ii) the node activities are uniform across nodes on both the drift and the pairwise interaction functions. Moreover, the authors consider only positive (mutualistic) interactions. Here we show the conditions proposed by Gao and collaborators [Nature (London) 530, 307 (2016)10.1038/nature16948] are neither sufficient nor necessary to guarantee that their method works in general and validity of their results are not independent of the model chosen within the class of dynamics they considered. Indeed we find that a new condition poses effective limitations to their framework and we provide quantitative predictions of the quality of the one-dimensional collapse as a function of the properties of interaction networks and stable dynamics using results from random matrix theory. We also find that multidimensional reduction may work also for an interaction matrix with a mixture of positive and negative signs, opening up an application of the framework to food webs, neuronal networks, and social and economic interactions.

Collapse of resilience patterns in generalized Lotka-Volterra dynamics and beyond

NASA Astrophysics Data System (ADS)

Tu, Chengyi; Grilli, Jacopo; Schuessler, Friedrich; Suweis, Samir

2017-06-01

Recently, a theoretical framework aimed at separating the roles of dynamics and topology in multidimensional systems has been developed [Gao et al., Nature (London) 530, 307 (2016), 10.1038/nature16948]. The validity of their method is assumed to hold depending on two main hypotheses: (i) The network determined by the the interaction between pairs of nodes has negligible degree correlations; (ii) the node activities are uniform across nodes on both the drift and the pairwise interaction functions. Moreover, the authors consider only positive (mutualistic) interactions. Here we show the conditions proposed by Gao and collaborators [Nature (London) 530, 307 (2016), 10.1038/nature16948] are neither sufficient nor necessary to guarantee that their method works in general and validity of their results are not independent of the model chosen within the class of dynamics they considered. Indeed we find that a new condition poses effective limitations to their framework and we provide quantitative predictions of the quality of the one-dimensional collapse as a function of the properties of interaction networks and stable dynamics using results from random matrix theory. We also find that multidimensional reduction may work also for an interaction matrix with a mixture of positive and negative signs, opening up an application of the framework to food webs, neuronal networks, and social and economic interactions.

pH-Responsive Shape Memory Poly(ethylene glycol)-Poly(ε-caprolactone)-based Polyurethane/Cellulose Nanocrystals Nanocomposite.

PubMed

Li, Ying; Chen, Hongmei; Liu, Dian; Wang, Wenxi; Liu, Ye; Zhou, Shaobing

2015-06-17

In this study, we developed a pH-responsive shape-memory polymer nanocomposite by blending poly(ethylene glycol)-poly(ε-caprolactone)-based polyurethane (PECU) with functionalized cellulose nanocrystals (CNCs). CNCs were functionalized with pyridine moieties (CNC-C6H4NO2) through hydroxyl substitution of CNCs with pyridine-4-carbonyl chloride and with carboxyl groups (CNC-CO2H) via 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) mediated surface oxidation, respectively. At a high pH value, the CNC-C6H4NO2 had attractive interactions from the hydrogen bonding between pyridine groups and hydroxyl moieties; at a low pH value, the interactions reduced or disappeared due to the protonation of pyridine groups, which are a Lewis base. The CNC-CO2H responded to pH variation in an opposite manner. The hydrogen bonding interactions of both CNC-C6H4NO2 and CNC-CO2H can be readily disassociated by altering pH values, endowing the pH-responsiveness of CNCs. When these functionalized CNCs were added in PECU polymer matrix to form nanocomposite network which was confirmed with rheological measurements, the mechanical properties of PECU were not only obviously improved but also the pH-responsiveness of CNCs could be transferred to the nanocomposite network. The pH-sensitive CNC percolation network in polymer matrix served as the switch units of shape-memory polymers (SMPs). Furthermore, the modified CNC percolation network and polymer molecular chains also had strong hydrogen bonding interactions among hydroxyl, carboxyl, pyridine moieties, and isocyanate groups, which could be formed or destroyed through changing pH value. The shape memory function of the nanocomposite network was only dependent on the pH variation of the environment. Therefore, this pH-responsive shape-memory nancomposite could be potentially developed into a new smart polymer material.

A Strategy Based on Protein-Protein Interface Motifs May Help in Identifying Drug Off-Targets

PubMed Central

Engin, H. Billur; Keskin, Ozlem; Nussinov, Ruth; Gursoy, Attila

2014-01-01

Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can ‘attack’ nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, “The Interface Attack”, based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model which we call “Protein Interface and Interaction Network (P2IN)”, which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces, and which proteins may compete to bind the same surface region. We built the P2IN of p53 signaling network and performed network robustness analysis. We show that (1) ‘hitting’ frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes); (2) frequent interfaces are not always topologically critical elements in the network; and (3) interface attack may reveal functional changes in the system better than attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D. PMID:22817115

Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns

NASA Astrophysics Data System (ADS)

Burioni, Raffaella; Ubaldi, Enrico; Vezzani, Alessandro

2017-05-01

The recent availability of large-scale, time-resolved and high quality digital datasets has allowed for a deeper understanding of the structure and properties of many real-world networks. The empirical evidence of a temporal dimension prompted the switch of paradigm from a static representation of networks to a time varying one. In this work we briefly review the framework of time-varying-networks in real world social systems, especially focusing on the activity-driven paradigm. We develop a framework that allows for the encoding of three generative mechanisms that seem to play a central role in the social networks’ evolution: the individual’s propensity to engage in social interactions, its strategy in allocate these interactions among its alters and the burstiness of interactions amongst social actors. The functional forms and probability distributions encoding these mechanisms are typically data driven. A natural question arises if different classes of strategies and burstiness distributions, with different local scale behavior and analogous asymptotics can lead to the same long time and large scale structure of the evolving networks. We consider the problem in its full generality, by investigating and solving the system dynamics in the asymptotic limit, for general classes of ties allocation mechanisms and waiting time probability distributions. We show that the asymptotic network evolution is driven by a few characteristics of these functional forms, that can be extracted from direct measurements on large datasets.

Performance verification of network function virtualization in software defined optical transport networks

NASA Astrophysics Data System (ADS)

Zhao, Yongli; Hu, Liyazhou; Wang, Wei; Li, Yajie; Zhang, Jie

2017-01-01

With the continuous opening of resource acquisition and application, there are a large variety of network hardware appliances deployed as the communication infrastructure. To lunch a new network application always implies to replace the obsolete devices and needs the related space and power to accommodate it, which will increase the energy and capital investment. Network function virtualization1 (NFV) aims to address these problems by consolidating many network equipment onto industry standard elements such as servers, switches and storage. Many types of IT resources have been deployed to run Virtual Network Functions (vNFs), such as virtual switches and routers. Then how to deploy NFV in optical transport networks is a of great importance problem. This paper focuses on this problem, and gives an implementation architecture of NFV-enabled optical transport networks based on Software Defined Optical Networking (SDON) with the procedure of vNFs call and return. Especially, an implementation solution of NFV-enabled optical transport node is designed, and a parallel processing method for NFV-enabled OTN nodes is proposed. To verify the performance of NFV-enabled SDON, the protocol interaction procedures of control function virtualization and node function virtualization are demonstrated on SDON testbed. Finally, the benefits and challenges of the parallel processing method for NFV-enabled OTN nodes are simulated and analyzed.

Dynamic Changes in Amygdala Psychophysiological Connectivity Reveal Distinct Neural Networks for Facial Expressions of Basic Emotions.

PubMed

Diano, Matteo; Tamietto, Marco; Celeghin, Alessia; Weiskrantz, Lawrence; Tatu, Mona-Karina; Bagnis, Arianna; Duca, Sergio; Geminiani, Giuliano; Cauda, Franco; Costa, Tommaso

2017-03-27

The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala's psychophysiological interaction (PPI). In fact, PPI can reveal how inter-regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic signature prototypical of individual emotion processing, and seemingly represent a neural mechanism that serves to implement the distinctive influence that each emotion exerts on perceptual, cognitive, and motor responses. Besides these differences, all emotions enhanced amygdala functional integration with premotor cortices compared to neutral faces. The present findings thus concur to reconceptualise the structure-function relation between brain-emotion from the traditional one-to-one mapping toward a network-based and dynamic perspective.

A Model of Amygdala-Hippocampal-Prefrontal Interaction in Fear Conditioning and Extinction in Animals

ERIC Educational Resources Information Center

Moustafa, Ahmed A.; Gilbertson, Mark W.; Orr, Scott P.; Herzallah, Mohammad M.; Servatius, Richard J.; Myers, Catherine E.

2013-01-01

Empirical research has shown that the amygdala, hippocampus, and ventromedial prefrontal cortex (vmPFC) are involved in fear conditioning. However, the functional contribution of each brain area and the nature of their interactions are not clearly understood. Here, we extend existing neural network models of the functional roles of the hippocampus…

A decentralized mechanism for improving the functional robustness of distribution networks.

PubMed

Shi, Benyun; Liu, Jiming

2012-10-01

Most real-world distribution systems can be modeled as distribution networks, where a commodity can flow from source nodes to sink nodes through junction nodes. One of the fundamental characteristics of distribution networks is the functional robustness, which reflects the ability of maintaining its function in the face of internal or external disruptions. In view of the fact that most distribution networks do not have any centralized control mechanisms, we consider the problem of how to improve the functional robustness in a decentralized way. To achieve this goal, we study two important problems: 1) how to formally measure the functional robustness, and 2) how to improve the functional robustness of a network based on the local interaction of its nodes. First, we derive a utility function in terms of network entropy to characterize the functional robustness of a distribution network. Second, we propose a decentralized network pricing mechanism, where each node need only communicate with its distribution neighbors by sending a "price" signal to its upstream neighbors and receiving "price" signals from its downstream neighbors. By doing so, each node can determine its outflows by maximizing its own payoff function. Our mathematical analysis shows that the decentralized pricing mechanism can produce results equivalent to those of an ideal centralized maximization with complete information. Finally, to demonstrate the properties of our mechanism, we carry out a case study on the U.S. natural gas distribution network. The results validate the convergence and effectiveness of our mechanism when comparing it with an existing algorithm.

Functional brain networks reconstruction using group sparsity-regularized learning.

PubMed

Zhao, Qinghua; Li, Will X Y; Jiang, Xi; Lv, Jinglei; Lu, Jianfeng; Liu, Tianming

2018-06-01

Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks utilizing the structure guided group sparse regression (S2GSR) in which 116 anatomical regions from the AAL template, as prior knowledge, are employed to guide the network reconstruction when performing sparse representation of whole-brain fMRI data. Specifically, we extract fMRI signals from standard space aligned with the AAL template. Then by learning a global over-complete dictionary, with the learned dictionary as a set of features (regressors), the group structured regression employs anatomical structures as group information to regress whole brain signals. Finally, the decomposition coefficients matrix is mapped back to the brain volume to represent functional brain networks and patterns. We use the publicly available Human Connectome Project (HCP) Q1 dataset as the test bed, and the experimental results indicate that the proposed anatomically guided structure sparse representation is effective in reconstructing concurrent functional brain networks.

White-matter functional networks changes in patients with schizophrenia.

PubMed

Jiang, Yuchao; Luo, Cheng; Li, Xuan; Li, Yingjia; Yang, Hang; Li, Jianfu; Chang, Xin; Li, Hechun; Yang, Huanghao; Wang, Jijun; Duan, Mingjun; Yao, Dezhong

2018-04-13

Resting-state functional MRI (rsfMRI) is a useful technique for investigating the functional organization of human gray-matter in neuroscience and neuropsychiatry. Nevertheless, most studies have demonstrated the functional connectivity and/or task-related functional activity in the gray-matter. White-matter functional networks have been investigated in healthy subjects. Schizophrenia has been hypothesized to be a brain disorder involving insufficient or ineffective communication associated with white-matter abnormalities. However, previous studies have mainly examined the structural architecture of white-matter using MRI or diffusion tensor imaging and failed to uncover any dysfunctional connectivity within the white-matter on rsfMRI. The current study used rsfMRI to evaluate white-matter functional connectivity in a large cohort of ninety-seven schizophrenia patients and 126 healthy controls. Ten large-scale white-matter networks were identified by a cluster analysis of voxel-based white-matter functional connectivity and classified into superficial, middle and deep layers of networks. Evaluation of the spontaneous oscillation of white-matter networks and the functional connectivity between them showed that patients with schizophrenia had decreased amplitudes of low-frequency oscillation and increased functional connectivity in the superficial perception-motor networks. Additionally, we examined the interactions between white-matter and gray-matter networks. The superficial perception-motor white-matter network had decreased functional connectivity with the cortical perception-motor gray-matter networks. In contrast, the middle and deep white-matter networks had increased functional connectivity with the superficial perception-motor white-matter network and the cortical perception-motor gray-matter network. Thus, we presumed that the disrupted association between the gray-matter and white-matter networks in the perception-motor system may be compensated for through the middle-deep white-matter networks, which may be the foundation of the extensively disrupted connections in schizophrenia. Copyright © 2018 Elsevier Inc. All rights reserved.

Progression of Brain Network Alterations in Cerebral Amyloid Angiopathy.

PubMed

Reijmer, Yael D; Fotiadis, Panagiotis; Riley, Grace A; Xiong, Li; Charidimou, Andreas; Boulouis, Gregoire; Ayres, Alison M; Schwab, Kristin; Rosand, Jonathan; Gurol, M Edip; Viswanathan, Anand; Greenberg, Steven M

2016-10-01

We recently showed that cerebral amyloid angiopathy (CAA) is associated with functionally relevant brain network impairments, in particular affecting posterior white matter connections. Here we examined how these brain network impairments progress over time. Thirty-three patients with probable CAA underwent multimodal brain magnetic resonance imaging at 2 time points (mean follow-up time: 1.3±0.4 years). Brain networks of the hemisphere free of intracerebral hemorrhages were reconstructed using fiber tractography and graph theory. The global efficiency of the network and mean fractional anisotropies of posterior-posterior, frontal-frontal, and posterior-frontal network connections were calculated. Patients with moderate versus severe CAA were defined based on microbleed count, dichotomized at the median (median=35). Global efficiency of the intracerebral hemorrhage-free hemispheric network declined from baseline to follow-up (-0.008±0.003; P=0.029). The decline in global efficiency was most pronounced for patients with severe CAA (group×time interaction P=0.03). The decline in global network efficiency was associated with worse executive functioning (β=0.46; P=0.03). Examination of subgroups of network connections revealed a decline in fractional anisotropies of posterior-posterior connections at both levels of CAA severity (-0.006±0.002; P=0.017; group×time interaction P=0.16). The fractional anisotropies of posterior-frontal and frontal-frontal connections declined in patients with severe but not moderate CAA (group×time interaction P=0.007 and P=0.005). Associations were independent of change in white matter hyperintensity volume. Brain network impairment in patients with CAA worsens measurably over just 1.3-year follow-up and seem to progress from posterior to frontal connections with increasing disease severity. © 2016 American Heart Association, Inc.

Systematic identification of an integrative network module during senescence from time-series gene expression.

PubMed

Park, Chihyun; Yun, So Jeong; Ryu, Sung Jin; Lee, Soyoung; Lee, Young-Sam; Yoon, Youngmi; Park, Sang Chul

2017-03-15

Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator. Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

PubMed

Huang, Huiyuan; Wang, Junjing; Seger, Carol; Lu, Min; Deng, Feng; Wu, Xiaoyan; He, Yuan; Niu, Chen; Wang, Jun; Huang, Ruiwang

2018-01-01

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

An application programming interface for CellNetAnalyzer.

PubMed

Klamt, Steffen; von Kamp, Axel

2011-08-01

CellNetAnalyzer (CNA) is a MATLAB toolbox providing computational methods for studying structure and function of metabolic and cellular signaling networks. In order to allow non-experts to use these methods easily, CNA provides GUI-based interactive network maps as a means of parameter input and result visualization. However, with the availability of high-throughput data, there is a need to make CNA's functionality also accessible in batch mode for automatic data processing. Furthermore, as some algorithms of CNA are of general relevance for network analysis it would be desirable if they could be called as sub-routines by other applications. For this purpose, we developed an API (application programming interface) for CNA allowing users (i) to access the content of network models in CNA, (ii) to use CNA's network analysis capabilities independent of the GUI, and (iii) to interact with the GUI to facilitate the development of graphical plugins. Here we describe the organization of network projects in CNA and the application of the new API functions to these projects. This includes the creation of network projects from scratch, loading and saving of projects and scenarios, and the application of the actual analysis methods. Furthermore, API functions for the import/export of metabolic models in SBML format and for accessing the GUI are described. Lastly, two example applications demonstrate the use and versatile applicability of CNA's API. CNA is freely available for academic use and can be downloaded from http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Disease gene classification with metagraph representations.

PubMed

Kircali Ata, Sezin; Fang, Yuan; Wu, Min; Li, Xiao-Li; Xiao, Xiaokui

2017-12-01

Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes. As disease proteins tend to have a similar topological characteristics on the PPIK network, we further propose to represent proteins with metagraphs. Different from a traditional network motif or subgraph, a metagraph can capture a particular topological arrangement involving the interactions/associations between both proteins and keywords. Based on the novel metagraph representations for proteins, we further build classifiers for disease protein classification through supervised learning. Our experiments on three different PPI databases demonstrate that the proposed method consistently improves disease protein prediction across various classifiers, by 15.3% in AUC on average. It outperforms the baselines including the diffusion-based methods (e.g., RWR) and the module-based methods by 13.8-32.9% for overall disease protein prediction. For predicting breast cancer genes, it outperforms RWR, PRINCE and the module-based baselines by 6.6-14.2%. Finally, our predictions also turn out to have better correlations with literature findings from PubMed. Copyright © 2017 Elsevier Inc. All rights reserved.

Network of Spaces and Interaction-Related Behaviors in Adult Intensive Care Units

PubMed Central

Rashid, Mahbub; Boyle, Diane K.; Crosser, Michael

2014-01-01

Using three spatial network measures of “space syntax”, this correlational study describes four interaction-related behaviors among three groups of users in relation to visibility and accessibility of spaces in four adult intensive care units (ICUs) of different size, geometry, and specialty. Systematic field observations of interaction-related behaviors show significant differences in spatial distribution of interaction-related behaviors in the ICUs. Despite differences in unit characteristics and interaction-related behaviors, the study finds that when nurses and physicians “interact while sitting” they prefer spaces that help maintain a high level of environmental awareness; that when nurses “walk” and “interact while walking” they avoid spaces with better global access and visibility; and that everyone in ICUs “walk” more in spaces with higher control over neighboring spaces. It is argued that such consistent behavioral patterns occur due to the structural similarities of spatial networks over and above the more general functional similarities of ICUs. PMID:25469838

Functional network analysis of genes differentially expressed during xylogenesis in soc1ful woody Arabidopsis plants.

PubMed

Davin, Nicolas; Edger, Patrick P; Hefer, Charles A; Mizrachi, Eshchar; Schuetz, Mathias; Smets, Erik; Myburg, Alexander A; Douglas, Carl J; Schranz, Michael E; Lens, Frederic

2016-06-01

Many plant genes are known to be involved in the development of cambium and wood, but how the expression and functional interaction of these genes determine the unique biology of wood remains largely unknown. We used the soc1ful loss of function mutant - the woodiest genotype known in the otherwise herbaceous model plant Arabidopsis - to investigate the expression and interactions of genes involved in secondary growth (wood formation). Detailed anatomical observations of the stem in combination with mRNA sequencing were used to assess transcriptome remodeling during xylogenesis in wild-type and woody soc1ful plants. To interpret the transcriptome changes, we constructed functional gene association networks of differentially expressed genes using the STRING database. This analysis revealed functionally enriched gene association hubs that are differentially expressed in herbaceous and woody tissues. In particular, we observed the differential expression of genes related to mechanical stress and jasmonate biosynthesis/signaling during wood formation in soc1ful plants that may be an effect of greater tension within woody tissues. Our results suggest that habit shifts from herbaceous to woody life forms observed in many angiosperm lineages could have evolved convergently by genetic changes that modulate the gene expression and interaction network, and thereby redeploy the conserved wood developmental program. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks.

PubMed

Li, Hongdong; Zhang, Yang; Guan, Yuanfang; Menon, Rajasree; Omenn, Gilbert S

2017-01-01

Tens of thousands of splice isoforms of proteins have been catalogued as predicted sequences from transcripts in humans and other species. Relatively few have been characterized biochemically or structurally. With the extensive development of protein bioinformatics, the characterization and modeling of isoform features, isoform functions, and isoform-level networks have advanced notably. Here we present applications of the I-TASSER family of algorithms for folding and functional predictions and the IsoFunc, MIsoMine, and Hisonet data resources for isoform-level analyses of network and pathway-based functional predictions and protein-protein interactions. Hopefully, predictions and insights from protein bioinformatics will stimulate many experimental validation studies.

Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints.

PubMed

Zilles, Karl; Bacha-Trams, Maraike; Palomero-Gallagher, Nicola; Amunts, Katrin; Friederici, Angela D

2015-02-01

The language network is a well-defined large-scale neural network of anatomically and functionally interacting cortical areas. The successful language process requires the transmission of information between these areas. Since neurotransmitter receptors are key molecules of information processing, we hypothesized that cortical areas which are part of the same functional language network may show highly similar multireceptor expression pattern ("receptor fingerprint"), whereas those that are not part of this network should have different fingerprints. Here we demonstrate that the relation between the densities of 15 different excitatory, inhibitory and modulatory receptors in eight language-related areas are highly similar and differ considerably from those of 18 other brain regions not directly involved in language processing. Thus, the fingerprints of all cortical areas underlying a large-scale cognitive domain such as language is a characteristic, functionally relevant feature of this network and an important prerequisite for the underlying neuronal processes of language functions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints

PubMed Central

Zilles, Karl; Bacha-Trams, Maraike; Palomero-Gallagher, Nicola; Amunts, Katrin; Friederici, Angela D.

2015-01-01

The language network is a well-defined large-scale neural network of anatomically and functionally interacting cortical areas. The successful language process requires the transmission of information between these areas. Since neurotransmitter receptors are key molecules of information processing, we hypothesized that cortical areas which are part of the same functional language network may show highly similar multireceptor expression pattern (“receptor fingerprint”), whereas those that are not part of this network should have different fingerprints. Here we demonstrate that the relation between the densities of 15 different excitatory, inhibitory and modulatory receptors in eight language-related areas are highly similar and differ considerably from those of 18 other brain regions not directly involved in language processing. Thus, the fingerprints of all cortical areas underlying a large-scale cognitive domain such as language is a characteristic, functionally relevant feature of this network and an important prerequisite for the underlying neuronal processes of language functions. PMID:25243991

Redesigning metabolism based on orthogonality principles

PubMed Central

Pandit, Aditya Vikram; Srinivasan, Shyam; Mahadevan, Radhakrishnan

2017-01-01

Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering. PMID:28555623

Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

PubMed

Rezende, Antonio M; Folador, Edson L; Resende, Daniela de M; Ruiz, Jeronimo C

2012-01-01

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.

Disrupted intrinsic and remote functional connectivity in heterotopia-related epilepsy.

PubMed

Liu, W; Hu, X; An, D; Gong, Q; Zhou, D

2018-01-01

Several neuroimaging studies have examined neural interactions in patients with periventricular nodular heterotopia (PNH). However, features of the underlying functional network remain poorly understood. In this study, we examined alterations in the local (regional) and remote (interregional) cerebral networks in this disorder. Twenty-eight subjects all having suffered from PNH with epilepsy, as well as 28 age- and sex- matched healthy controls, were enrolled in this study. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC) were calculated to detect regional neural function and functional network integration, respectively. Compared with healthy controls, patients with PNH-related epilepsy showed decreased ALFF in the ventromedial prefrontal cortex (vmPFC) and precuneus areas. ALFF values in both areas were negative correlated with epilepsy duration (P < .05, Bonferroni-corrected). Furthermore, patients with PNH-related epilepsy had increased remote interregional FC mainly in bilateral prefrontal and parietal cortices, supramarginal gyrus, dorsal cingulate gyrus, and right insula; lower FC was found in posterior brain regions including bilateral parahippocampal gyrus and inferior temporal gyrus. Focal spontaneous hypofunction, as assessed by ALFF, correlates with epilepsy duration in patients with PNH-related epilepsy. Abnormalities existed both within the default-mode network and then across the whole brain, demonstrating that intrinsic brain dysfunction may be related to specific network interactions. Our findings provide novel understanding of the connectivity-based pathophysiological mechanisms of PNH. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Temporal variation in bat-fruit interactions: Foraging strategies influence network structure over time

NASA Astrophysics Data System (ADS)

Zapata-Mesa, Natalya; Montoya-Bustamante, Sebastián; Murillo-García, Oscar E.

2017-11-01

Mutualistic interactions, such as seed dispersal, are important for the maintenance of structure and stability of tropical communities. However, there is a lack of information about spatial and temporal variation in plant-animal interaction networks. Thus, our goal was to assess the effect of bat's foraging strategies on temporal variation in the structure and robustness of bat-fruit networks in both a dry and a rain tropical forest. We evaluated monthly variation in bat-fruit networks by using seven structure metrics: network size, average path length, nestedness, modularity, complementary specialization, normalized degree and betweenness centrality. Seed dispersal networks showed variations in size, species composition and modularity; did not present nested structures and their complementary specialization was high compared to other studies. Both networks presented short path lengths, and a constantly high robustness, despite their monthly variations. Sedentary bat species were recorded during all the study periods and occupied more central positions than nomadic species. We conclude that foraging strategies are important structuring factors that affect the dynamic of networks by determining the functional roles of frugivorous bats over time; thus sedentary bats are more important than nomadic species for the maintenance of the network structure, and their conservation is a must.

Alterations of Brain Functional Architecture Associated with Psychopathic Traits in Male Adolescents with Conduct Disorder.

PubMed

Pu, Weidan; Luo, Qiang; Jiang, Yali; Gao, Yidian; Ming, Qingsen; Yao, Shuqiao

2017-09-12

Psychopathic traits of conduct disorder (CD) have a core callous-unemotional (CU) component and an impulsive-antisocial component. Previous task-driven fMRI studies have suggested that psychopathic traits are associated with dysfunction of several brain areas involved in different cognitive functions (e.g., empathy, reward, and response inhibition etc.), but the relationship between psychopathic traits and intrinsic brain functional architecture has not yet been explored in CD. Using a holistic brain-wide functional connectivity analysis, this study delineated the alterations in brain functional networks in patients with conduct disorder. Compared with matched healthy controls, we found decreased anti-synchronization between the fronto-parietal network (FPN) and default mode network (DMN), and increased intra-network synchronization within the frontothalamic-basal ganglia, right frontoparietal, and temporal/limbic/visual networks in CD patients. Correlation analysis showed that the weakened FPN-DMN interaction was associated with CU traits, while the heightened intra-network functional connectivity was related to impulsivity traits in CD patients. Our findings suggest that decoupling of cognitive control (FPN) with social understanding of others (DMN) is associated with the CU traits, and hyper-functions of the reward and motor inhibition systems elevate impulsiveness in CD.

Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients.

PubMed

Li, Lianshuo; Wang, Zicheng; He, Peng; Ma, Shining; Du, Jie; Jiang, Rui

2016-10-01

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D. Copyright © 2016. Production and hosting by Elsevier Ltd.

The protein interaction map of bacteriophage lambda

PubMed Central

2011-01-01

Background Bacteriophage lambda is a model phage for most other dsDNA phages and has been studied for over 60 years. Although it is probably the best-characterized phage there are still about 20 poorly understood open reading frames in its 48-kb genome. For a complete understanding we need to know all interactions among its proteins. We have manually curated the lambda literature and compiled a total of 33 interactions that have been found among lambda proteins. We set out to find out how many protein-protein interactions remain to be found in this phage. Results In order to map lambda's interactions, we have cloned 68 out of 73 lambda open reading frames (the "ORFeome") into Gateway vectors and systematically tested all proteins for interactions using exhaustive array-based yeast two-hybrid screens. These screens identified 97 interactions. We found 16 out of 30 previously published interactions (53%). We have also found at least 18 new plausible interactions among functionally related proteins. All previously found and new interactions are combined into structural and network models of phage lambda. Conclusions Phage lambda serves as a benchmark for future studies of protein interactions among phage, viruses in general, or large protein assemblies. We conclude that we could not find all the known interactions because they require chaperones, post-translational modifications, or multiple proteins for their interactions. The lambda protein network connects 12 proteins of unknown function with well characterized proteins, which should shed light on the functional associations of these uncharacterized proteins. PMID:21943085

PodNet, a protein-protein interaction network of the podocyte.

PubMed

Warsow, Gregor; Endlich, Nicole; Schordan, Eric; Schordan, Sandra; Chilukoti, Ravi K; Homuth, Georg; Moeller, Marcus J; Fuellen, Georg; Endlich, Karlhans

2013-07-01

Interactions between proteins crucially determine cellular structure and function. Differential analysis of the interactome may help elucidate molecular mechanisms during disease development; however, this analysis necessitates mapping of expression data on protein-protein interaction networks. These networks do not exist for the podocyte; therefore, we built PodNet, a literature-based mouse podocyte network in Cytoscape format. Using database protein-protein interactions, we expanded PodNet to XPodNet with enhanced connectivity. In order to test the performance of XPodNet in differential interactome analysis, we examined podocyte developmental differentiation and the effect of cell culture. Transcriptomes of podocytes in 10 different states were mapped on XPodNet and analyzed with the Cytoscape plugin ExprEssence, based on the law of mass action. Interactions between slit diaphragm proteins are most significantly upregulated during podocyte development and most significantly downregulated in culture. On the other hand, our analysis revealed that interactions lost during podocyte differentiation are not regained in culture, suggesting a loss rather than a reversal of differentiation for podocytes in culture. Thus, we have developed PodNet as a valuable tool for differential interactome analysis in podocytes, and we have identified established and unexplored regulated interactions in developing and cultured podocytes.

Comparing species interaction networks along environmental gradients.

PubMed

Pellissier, Loïc; Albouy, Camille; Bascompte, Jordi; Farwig, Nina; Graham, Catherine; Loreau, Michel; Maglianesi, Maria Alejandra; Melián, Carlos J; Pitteloud, Camille; Roslin, Tomas; Rohr, Rudolf; Saavedra, Serguei; Thuiller, Wilfried; Woodward, Guy; Zimmermann, Niklaus E; Gravel, Dominique

2018-05-01

Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co-variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant-hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis-based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients. © 2017 Cambridge Philosophical Society.

BioLayout(Java): versatile network visualisation of structural and functional relationships.

PubMed

Goldovsky, Leon; Cases, Ildefonso; Enright, Anton J; Ouzounis, Christos A

2005-01-01

Visualisation of biological networks is becoming a common task for the analysis of high-throughput data. These networks correspond to a wide variety of biological relationships, such as sequence similarity, metabolic pathways, gene regulatory cascades and protein interactions. We present a general approach for the representation and analysis of networks of variable type, size and complexity. The application is based on the original BioLayout program (C-language implementation of the Fruchterman-Rheingold layout algorithm), entirely re-written in Java to guarantee portability across platforms. BioLayout(Java) provides broader functionality, various analysis techniques, extensions for better visualisation and a new user interface. Examples of analysis of biological networks using BioLayout(Java) are presented.

Optical protocols for advanced spacecraft networks

NASA Technical Reports Server (NTRS)

Bergman, Larry A.

1991-01-01

Most present day fiber optic networks are in fact extensions of copper wire networks. As a result, their speed is still limited by electronics even though optics is capable of running three orders of magnitude faster. Also, the fact that photons do not interact with one another (as electrons do) provides optical communication systems with some unique properties or new functionality that is not readily taken advantage of with conventional approaches. Some of the motivation for implementing network protocols in the optical domain, a few possible approaches including optical code-division multiple-access (CDMA), and how this class of networks can extend the technology life cycle of the Space Station Freedom (SSF) with increased performance and functionality are described.

Protein annotation from protein interaction networks and Gene Ontology.

PubMed

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.

Diversity of key players in the microbial ecosystems of the human body

PubMed Central

Jordán, Ferenc; Lauria, Mario; Scotti, Marco; Nguyen, Thanh-Phuong; Praveen, Paurush; Morine, Melissa; Priami, Corrado

2015-01-01

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different. PMID:26514870

Diversity of key players in the microbial ecosystems of the human body.

PubMed

Jordán, Ferenc; Lauria, Mario; Scotti, Marco; Nguyen, Thanh-Phuong; Praveen, Paurush; Morine, Melissa; Priami, Corrado

2015-10-30

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different.

Networks Depicting the Fine-Scale Co-Occurrences of Fungi in Soil Horizons.

PubMed

Toju, Hirokazu; Kishida, Osamu; Katayama, Noboru; Takagi, Kentaro

2016-01-01

Fungi in soil play pivotal roles in nutrient cycling, pest controls, and plant community succession in terrestrial ecosystems. Despite the ecosystem functions provided by soil fungi, our knowledge of the assembly processes of belowground fungi has been limited. In particular, we still have limited knowledge of how diverse functional groups of fungi interact with each other in facilitative and competitive ways in soil. Based on the high-throughput sequencing data of fungi in a cool-temperate forest in northern Japan, we analyzed how taxonomically and functionally diverse fungi showed correlated fine-scale distributions in soil. By uncovering pairs of fungi that frequently co-occurred in the same soil samples, networks depicting fine-scale co-occurrences of fungi were inferred at the O (organic matter) and A (surface soil) horizons. The results then led to the working hypothesis that mycorrhizal, endophytic, saprotrophic, and pathogenic fungi could form compartmentalized (modular) networks of facilitative, antagonistic, and/or competitive interactions in belowground ecosystems. Overall, this study provides a research basis for further understanding how interspecific interactions, along with sharing of niches among fungi, drive the dynamics of poorly explored biospheres in soil.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria.

PubMed

Ibarra-Arellano, Miguel A; Campos-González, Adrián I; Treviño-Quintanilla, Luis G; Tauch, Andreas; Freyre-González, Julio A

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy ( A: cross- BA: cteria SY: stems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.Database URL: http://abasy.ccg.unam.mx. © The Author(s) 2016. Published by Oxford University Press.

Cellular automata with object-oriented features for parallel molecular network modeling.

PubMed

Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

2005-06-01

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

Decay of interspecific avian flock networks along a disturbance gradient in Amazonia.

PubMed

Mokross, Karl; Ryder, Thomas B; Côrtes, Marina Corrêa; Wolfe, Jared D; Stouffer, Philip C

2014-02-07

Our understanding of how anthropogenic habitat change shapes species interactions is in its infancy. This is in large part because analytical approaches such as network theory have only recently been applied to characterize complex community dynamics. Network models are a powerful tool for quantifying how ecological interactions are affected by habitat modification because they provide metrics that quantify community structure and function. Here, we examine how large-scale habitat alteration has affected ecological interactions among mixed-species flocking birds in Amazonian rainforest. These flocks provide a model system for investigating how habitat heterogeneity influences non-trophic interactions and the subsequent social structure of forest-dependent mixed-species bird flocks. We analyse 21 flock interaction networks throughout a mosaic of primary forest, fragments of varying sizes and secondary forest (SF) at the Biological Dynamics of Forest Fragments Project in central Amazonian Brazil. Habitat type had a strong effect on network structure at the levels of both species and flock. Frequency of associations among species, as summarized by weighted degree, declined with increasing levels of forest fragmentation and SF. At the flock level, clustering coefficients and overall attendance positively correlated with mean vegetation height, indicating a strong effect of habitat structure on flock cohesion and stability. Prior research has shown that trophic interactions are often resilient to large-scale changes in habitat structure because species are ecologically redundant. By contrast, our results suggest that behavioural interactions and the structure of non-trophic networks are highly sensitive to environmental change. Thus, a more nuanced, system-by-system approach may be needed when thinking about the resiliency of ecological networks.

Fruit traits and temporal abundance shape plant-frugivore interaction networks in a seasonal tropical forest.

PubMed

Ramos-Robles, Michelle; Dáttilo, Wesley; Díaz-Castelazo, Cecilia; Andresen, Ellen

2018-04-02

Interactions between fleshy fruited plants and frugivores are crucial for the structuring and functioning of biotic communities, particularly in tropical forests where both groups are diverse and play different roles in network organization. However, it remains poorly understood how different groups of frugivore species and fruit traits contribute to network structure. We recorded interactions among 28 plant species and three groups of frugivores (birds, bats, and non-flying mammals) in a seasonal forest in Mexico to determine which species contribute more to network structure and evaluate the importance of each species. We also determined whether fruit abundance, water content, morphology traits, and fruiting phenology are related to network parameters: the number of interactions, species contribution to nestedness, and species strength. We found that plants did not depend on a single group of frugivores, but rather on one species of each group: the bird Pitangus sulphuratus, the bat Sturnira parvidens, and the non-flying mammal Procyon lotor. The abundance, size, and water content of the fruits were significantly related to the contribution to nestedness, number of interactions, and species strength index of plant species. Tree species and birds contributed mainly to the nested structure of the network. We show that the structure of plant-frugivore networks in this seasonal forest is non-random and that fruit traits (i.e., abundance, phenology, size, and water content) are important factors shaping plant-frugivore networks. Identification of the key species and their traits that maintain the complex structure of species interactions is therefore fundamental for the integral conservation of tropical forests.

Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks.

PubMed

Li, Wan; Zhu, Lina; Huang, Hao; He, Yuehan; Lv, Junjie; Li, Weimin; Chen, Lina; He, Weiming

2017-10-01

Complex chronic diseases are caused by the effects of genetic and environmental factors. Single nucleotide polymorphisms (SNPs), one common type of genetic variations, played vital roles in diseases. We hypothesized that disease risk functional SNPs in coding regions and protein interaction network modules were more likely to contribute to the identification of disease susceptible genes for complex chronic diseases. This could help to further reveal the pathogenesis of complex chronic diseases. Disease risk SNPs were first recognized from public SNP data for coronary heart disease (CHD), hypertension (HT) and type 2 diabetes (T2D). SNPs in coding regions that were classified into nonsense and missense by integrating several SNP functional annotation databases were treated as functional SNPs. Then, regions significantly associated with each disease were screened using random permutations for disease risk functional SNPs. Corresponding to these regions, 155, 169 and 173 potential disease susceptible genes were identified for CHD, HT and T2D, respectively. A disease-related gene product interaction network in environmental context was constructed for interacting gene products of both disease genes and potential disease susceptible genes for these diseases. After functional enrichment analysis for disease associated modules, 5 CHD susceptible genes, 7 HT susceptible genes and 3 T2D susceptible genes were finally identified, some of which had pleiotropic effects. Most of these genes were verified to be related to these diseases in literature. This was similar for disease genes identified from another method proposed by Lee et al. from a different aspect. This research could provide novel perspectives for diagnosis and treatment of complex chronic diseases and susceptible genes identification for other diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish

PubMed Central

Teles, Magda C.; Almeida, Olinda; Lopes, João S.; Oliveira, Rui F.

2015-01-01

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance. PMID:26423839

Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish.

PubMed

Teles, Magda C; Almeida, Olinda; Lopes, João S; Oliveira, Rui F

2015-10-07

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance. © 2015 The Author(s).

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

PubMed

Chu, Shu-Hsien; Parhi, Keshab K; Lenglet, Christophe

2018-03-16

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.

Functional Connectivity of Child and Adolescent Attention Deficit Hyperactivity Disorder Patients: Correlation with IQ.

PubMed

Park, Bo-Yong; Hong, Jisu; Lee, Seung-Hak; Park, Hyunjin

2016-01-01

Attention deficit hyperactivity disorder (ADHD) is a pervasive neuropsychological disorder that affects both children and adolescents. Child and adolescent ADHD patients exhibit different behavioral symptoms such as hyperactivity and impulsivity, but not much connectivity research exists to help explain these differences. We analyzed openly accessible resting-state functional magnetic resonance imaging (rs-fMRI) data on 112 patients (28 child ADHD, 28 adolescent ADHD, 28 child normal control (NC), and 28 adolescent NC). We used group independent component analysis (ICA) and weighted degree values to identify interaction effects of age (child and adolescent) and symptom (ADHD and NC) in brain networks. The frontoparietal network showed significant interaction effects ( p = 0.0068). The frontoparietal network is known to be related to hyperactive and impulsive behaviors. Intelligence quotient (IQ) is an important factor in ADHD, and we predicted IQ scores using the results of our connectivity analysis. IQ was predicted using degree centrality values of networks with significant interaction effects of age and symptom. Actual and predicted IQ scores demonstrated significant correlation values, with an error of about 10%. Our study might provide imaging biomarkers for future ADHD and intelligence studies.

Controllability of structural brain networks

NASA Astrophysics Data System (ADS)

Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Telesford, Qawi K.; Yu, Alfred B.; Kahn, Ari E.; Medaglia, John D.; Vettel, Jean M.; Miller, Michael B.; Grafton, Scott T.; Bassett, Danielle S.

2015-10-01

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

Coevolutionary diversification creates nested-modular structure in phage–bacteria interaction networks

PubMed Central

Beckett, Stephen J.; Williams, Hywel T. P.

2013-01-01

Phage and their bacterial hosts are the most diverse and abundant biological entities in the oceans, where their interactions have a major impact on marine ecology and ecosystem function. The structure of interaction networks for natural phage–bacteria communities offers insight into their coevolutionary origin. At small phylogenetic scales, observed communities typically show a nested structure, in which both hosts and phages can be ranked by their range of resistance and infectivity, respectively. A qualitatively different multi-scale structure is seen at larger phylogenetic scales; a natural assemblage sampled from the Atlantic Ocean displays large-scale modularity and local nestedness within each module. Here, we show that such ‘nested-modular’ interaction networks can be produced by a simple model of host–phage coevolution in which infection depends on genetic matching. Negative frequency-dependent selection causes diversification of hosts (to escape phages) and phages (to track their evolving hosts). This creates a diverse community of bacteria and phage, maintained by kill-the-winner ecological dynamics. When the resulting communities are visualized as bipartite networks of who infects whom, they show the nested-modular structure characteristic of the Atlantic sample. The statistical significance and strength of this observation varies depending on whether the interaction networks take into account the density of the interacting strains, with implications for interpretation of interaction networks constructed by different methods. Our results suggest that the apparently complex community structures associated with marine bacteria and phage may arise from relatively simple coevolutionary origins. PMID:24516719

High-confidence prediction of global interactomes based on genome-wide coevolutionary networks

PubMed Central

Juan, David; Pazos, Florencio; Valencia, Alfonso

2008-01-01

Interacting or functionally related protein families tend to have similar phylogenetic trees. Based on this observation, techniques have been developed to predict interaction partners. The observed degree of similarity between the phylogenetic trees of two proteins is the result of many different factors besides the actual interaction or functional relationship between them. Such factors influence the performance of interaction predictions. One aspect that can influence this similarity is related to the fact that a given protein interacts with many others, and hence it must adapt to all of them. Accordingly, the interaction or coadaptation signal within its tree is a composite of the influence of all of the interactors. Here, we introduce a new estimator of coevolution to overcome this and other problems. Instead of relying on the individual value of tree similarity between two proteins, we use the whole network of similarities between all of the pairs of proteins within a genome to reassess the similarity of that pair, thereby taking into account its coevolutionary context. We show that this approach offers a substantial improvement in interaction prediction performance, providing a degree of accuracy/coverage comparable with, or in some cases better than, that of experimental techniques. Moreover, important information on the structure, function, and evolution of macromolecular complexes can be inferred with this methodology. PMID:18199838

High-confidence prediction of global interactomes based on genome-wide coevolutionary networks.

PubMed

Juan, David; Pazos, Florencio; Valencia, Alfonso

2008-01-22

Interacting or functionally related protein families tend to have similar phylogenetic trees. Based on this observation, techniques have been developed to predict interaction partners. The observed degree of similarity between the phylogenetic trees of two proteins is the result of many different factors besides the actual interaction or functional relationship between them. Such factors influence the performance of interaction predictions. One aspect that can influence this similarity is related to the fact that a given protein interacts with many others, and hence it must adapt to all of them. Accordingly, the interaction or coadaptation signal within its tree is a composite of the influence of all of the interactors. Here, we introduce a new estimator of coevolution to overcome this and other problems. Instead of relying on the individual value of tree similarity between two proteins, we use the whole network of similarities between all of the pairs of proteins within a genome to reassess the similarity of that pair, thereby taking into account its coevolutionary context. We show that this approach offers a substantial improvement in interaction prediction performance, providing a degree of accuracy/coverage comparable with, or in some cases better than, that of experimental techniques. Moreover, important information on the structure, function, and evolution of macromolecular complexes can be inferred with this methodology.

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

PubMed Central

Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Singh, Ambuj K.

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in “resting state” employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions. PMID:29016686

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

PubMed

Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

PubMed

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Fault Tolerance in Protein Interaction Networks: Stable Bipartite Subgraphs and Redundant Pathways

PubMed Central

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J.

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair. PMID:19399174

iELM—a web server to explore short linear motif-mediated interactions

PubMed Central

Weatheritt, Robert J.; Jehl, Peter; Dinkel, Holger; Gibson, Toby J.

2012-01-01

The recent expansion in our knowledge of protein–protein interactions (PPIs) has allowed the annotation and prediction of hundreds of thousands of interactions. However, the function of many of these interactions remains elusive. The interactions of Eukaryotic Linear Motif (iELM) web server provides a resource for predicting the function and positional interface for a subset of interactions mediated by short linear motifs (SLiMs). The iELM prediction algorithm is based on the annotated SLiM classes from the Eukaryotic Linear Motif (ELM) resource and allows users to explore both annotated and user-generated PPI networks for SLiM-mediated interactions. By incorporating the annotated information from the ELM resource, iELM provides functional details of PPIs. This can be used in proteomic analysis, for example, to infer whether an interaction promotes complex formation or degradation. Furthermore, details of the molecular interface of the SLiM-mediated interactions are also predicted. This information is displayed in a fully searchable table, as well as graphically with the modular architecture of the participating proteins extracted from the UniProt and Phospho.ELM resources. A network figure is also presented to aid the interpretation of results. The iELM server supports single protein queries as well as large-scale proteomic submissions and is freely available at http://i.elm.eu.org. PMID:22638578

Retinotopic patterns of functional connectivity between V1 and large-scale brain networks during resting fixation

PubMed Central

Griffis, Joseph C.; Elkhetali, Abdurahman S.; Burge, Wesley K.; Chen, Richard H.; Bowman, Anthony D.; Szaflarski, Jerzy P.; Visscher, Kristina M.

2016-01-01

Psychophysical and neurobiological evidence suggests that central and peripheral vision are specialized for different functions. This specialization of function might be expected to lead to differences in the large-scale functional interactions of early cortical areas that represent central and peripheral visual space. Here, we characterize differences in whole-brain functional connectivity among sectors in primary visual cortex (V1) corresponding to central, near-peripheral, and far-peripheral vision during resting fixation. Importantly, our analyses reveal that eccentricity sectors in V1 have different functional connectivity with non-visual areas associated with large-scale brain networks. Regions associated with the fronto-parietal control network are most strongly connected with central sectors of V1, regions associated with the cingulo-opercular control network are most strongly connected with near-peripheral sectors of V1, and regions associated with the default mode and auditory networks are most strongly connected with far-peripheral sectors of V1. Additional analyses suggest that similar patterns are present during eyes-closed rest. These results suggest that different types of visual information may be prioritized by large-scale brain networks with distinct functional profiles, and provide insights into how the small-scale functional specialization within early visual regions such as V1 relates to the large-scale organization of functionally distinct whole-brain networks. PMID:27554527

Functional Stem Cell Integration into Neural Networks Assessed by Organotypic Slice Cultures.

PubMed

Forsberg, David; Thonabulsombat, Charoensri; Jäderstad, Johan; Jäderstad, Linda Maria; Olivius, Petri; Herlenius, Eric

2017-08-14

Re-formation or preservation of functional, electrically active neural networks has been proffered as one of the goals of stem cell-mediated neural therapeutics. A primary issue for a cell therapy approach is the formation of functional contacts between the implanted cells and the host tissue. Therefore, it is of fundamental interest to establish protocols that allow us to delineate a detailed time course of grafted stem cell survival, migration, differentiation, integration, and functional interaction with the host. One option for in vitro studies is to examine the integration of exogenous stem cells into an existing active neural network in ex vivo organotypic cultures. Organotypic cultures leave the structural integrity essentially intact while still allowing the microenvironment to be carefully controlled. This allows detailed studies over time of cellular responses and cell-cell interactions, which are not readily performed in vivo. This unit describes procedures for using organotypic slice cultures as ex vivo model systems for studying neural stem cell and embryonic stem cell engraftment and communication with CNS host tissue. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Probing Allosteric Inhibition Mechanisms of the Hsp70 Chaperone Proteins Using Molecular Dynamics Simulations and Analysis of the Residue Interaction Networks.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2016-08-22

Although molecular mechanisms of allosteric regulation in the Hsp70 chaperones have been extensively studied at both structural and functional levels, the current understanding of allosteric inhibition of chaperone activities by small molecules is still lacking. In the current study, using a battery of computational approaches, we probed allosteric inhibition mechanisms of E. coli Hsp70 (DnaK) and human Hsp70 proteins by small molecule inhibitors PET-16 and novolactone. Molecular dynamics simulations and binding free energy analysis were combined with network-based modeling of residue interactions and allosteric communications to systematically characterize and compare molecular signatures of the apo form, substrate-bound, and inhibitor-bound chaperone complexes. The results suggested a mechanism by which the allosteric inhibitors may leverage binding energy hotspots in the interaction networks to stabilize a specific conformational state and impair the interdomain allosteric control. Using the network-based centrality analysis and community detection, we demonstrated that substrate binding may strengthen the connectivity of local interaction communities, leading to a dense interaction network that can promote an efficient allosteric communication. In contrast, binding of PET-16 to DnaK may induce significant dynamic changes and lead to a fractured interaction network and impaired allosteric communications in the DnaK complex. By using a mechanistic-based analysis of distance fluctuation maps and allosteric propensities of protein residues, we determined that the allosteric network in the PET-16 complex may be small and localized due to the reduced communication and low cooperativity of the substrate binding loops, which may promote the higher rates of substrate dissociation and the decreased substrate affinity. In comparison with the significant effect of PET-16, binding of novolactone to HSPA1A may cause only moderate network changes and preserve allosteric coupling between the allosteric pocket and the substrate binding region. The impact of novolactone on the conformational dynamics and allosteric communications in the HSPA1A complex was comparable to the substrate effect, which is consistent with the experimental evidence that PET-16, but not novolactone binding, can significantly decrease substrate affinity. We argue that the unique dynamic and network signatures of PET-16 and novolactone may be linked with the experimentally observed functional effects of these inhibitors on allosteric regulation and substrate binding.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.

PubMed

Babaei, Sepideh; Hulsman, Marc; Reinders, Marcel; de Ridder, Jeroen

2013-01-23

Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

An Adaptive Complex Network Model for Brain Functional Networks

PubMed Central

Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

2009-01-01

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms.

PubMed

Wang, Jia-Hong; Zhao, Ling-Feng; Lin, Pei; Su, Xiao-Rong; Chen, Shi-Jun; Huang, Li-Qiang; Wang, Hua-Feng; Zhang, Hai; Hu, Zhen-Fu; Yao, Kai-Tai; Huang, Zhong-Xi

2014-09-01

Identifying biological functions and molecular networks in a gene list and how the genes may relate to various topics is of considerable value to biomedical researchers. Here, we present a web-based text-mining server, GenCLiP 2.0, which can analyze human genes with enriched keywords and molecular interactions. Compared with other similar tools, GenCLiP 2.0 offers two unique features: (i) analysis of gene functions with free terms (i.e. any terms in the literature) generated by literature mining or provided by the user and (ii) accurate identification and integration of comprehensive molecular interactions from Medline abstracts, to construct molecular networks and subnetworks related to the free terms. http://ci.smu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Linking disease-associated genes to regulatory networks via promoter organization

PubMed Central

Döhr, S.; Klingenhoff, A.; Maier, H.; de Angelis, M. Hrabé; Werner, T.; Schneider, R.

2005-01-01

Pathway- or disease-associated genes may participate in more than one transcriptional co-regulation network. Such gene groups can be readily obtained by literature analysis or by high-throughput techniques such as microarrays or protein-interaction mapping. We developed a strategy that defines regulatory networks by in silico promoter analysis, finding potentially co-regulated subgroups without a priori knowledge. Pairs of transcription factor binding sites conserved in orthologous genes (vertically) as well as in promoter sequences of co-regulated genes (horizontally) were used as seeds for the development of promoter models representing potential co-regulation. This approach was applied to a Maturity Onset Diabetes of the Young (MODY)-associated gene list, which yielded two models connecting functionally interacting genes within MODY-related insulin/glucose signaling pathways. Additional genes functionally connected to our initial gene list were identified by database searches with these promoter models. Thus, data-driven in silico promoter analysis allowed integrating molecular mechanisms with biological functions of the cell. PMID:15701758

The Functional Landscape of Hsp27 Reveals New Cellular Processes such as DNA Repair and Alternative Splicing and Proposes Novel Anticancer Targets*

PubMed Central

Katsogiannou, Maria; Andrieu, Claudia; Baylot, Virginie; Baudot, Anaïs; Dusetti, Nelson J.; Gayet, Odile; Finetti, Pascal; Garrido, Carmen; Birnbaum, Daniel; Bertucci, François; Brun, Christine; Rocchi, Palma

2014-01-01

Previously, we identified the stress-induced chaperone, Hsp27, as highly overexpressed in castration-resistant prostate cancer and developed an Hsp27 inhibitor (OGX-427) currently tested in phase I/II clinical trials as a chemosensitizing agent in different cancers. To better understand the Hsp27 poorly-defined cytoprotective functions in cancers and increase the OGX-427 pharmacological safety, we established the Hsp27-protein interaction network using a yeast two-hybrid approach and identified 226 interaction partners. As an example, we showed that targeting Hsp27 interaction with TCTP, a partner protein identified in our screen increases therapy sensitivity, opening a new promising field of research for therapeutic approaches that could decrease or abolish toxicity for normal cells. Results of an in-depth bioinformatics network analysis allying the Hsp27 interaction map into the human interactome underlined the multifunctional character of this protein. We identified interactions of Hsp27 with proteins involved in eight well known functions previously related to Hsp27 and uncovered 17 potential new ones, such as DNA repair and RNA splicing. Validation of Hsp27 involvement in both processes in human prostate cancer cells supports our system biology-predicted functions and provides new insights into Hsp27 roles in cancer cells. PMID:25277244

Inborn errors of metabolism and the human interactome: a systems medicine approach.

PubMed

Woidy, Mathias; Muntau, Ania C; Gersting, Søren W

2018-02-05

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.

Altered Network Oscillations and Functional Connectivity Dynamics in Children Born Very Preterm.

PubMed

Moiseev, Alexander; Doesburg, Sam M; Herdman, Anthony T; Ribary, Urs; Grunau, Ruth E

2015-09-01

Structural brain connections develop atypically in very preterm children, and altered functional connectivity is also evident in fMRI studies. Such alterations in brain network connectivity are associated with cognitive difficulties in this population. Little is known, however, about electrophysiological interactions among specific brain networks in children born very preterm. In the present study, we recorded magnetoencephalography while very preterm children and full-term controls performed a visual short-term memory task. Regions expressing task-dependent activity changes were identified using beamformer analysis, and inter-regional phase synchrony was calculated. Very preterm children expressed altered regional recruitment in distributed networks of brain areas, across standard physiological frequency ranges including the theta, alpha, beta and gamma bands. Reduced oscillatory synchrony was observed among task-activated brain regions in very preterm children, particularly for connections involving areas critical for executive abilities, including middle frontal gyrus. These findings suggest that inability to recruit neurophysiological activity and interactions in distributed networks including frontal regions may contribute to difficulties in cognitive development in children born very preterm.

Structure and energetics of hydrogen-bonded networks of methanol on close packed transition metal surfaces

NASA Astrophysics Data System (ADS)

Murphy, Colin J.; Carrasco, Javier; Lawton, Timothy J.; Liriano, Melissa L.; Baber, Ashleigh E.; Lewis, Emily A.; Michaelides, Angelos; Sykes, E. Charles H.

2014-07-01

Methanol is a versatile chemical feedstock, fuel source, and energy storage material. Many reactions involving methanol are catalyzed by transition metal surfaces, on which hydrogen-bonded methanol overlayers form. As with water, the structure of these overlayers is expected to depend on a delicate balance of hydrogen bonding and adsorbate-substrate bonding. In contrast to water, however, relatively little is known about the structures methanol overlayers form and how these vary from one substrate to another. To address this issue, herein we analyze the hydrogen bonded networks that methanol forms as a function of coverage on three catalytically important surfaces, Au(111), Cu(111), and Pt(111), using a combination of scanning tunneling microscopy and density functional theory. We investigate the effect of intermolecular interactions, surface coverage, and adsorption energies on molecular assembly and compare the results to more widely studied water networks on the same surfaces. Two main factors are shown to direct the structure of methanol on the surfaces studied: the surface coverage and the competition between the methanol-methanol and methanol-surface interactions. Additionally, we report a new chiral form of buckled hexamer formed by surface bound methanol that maximizes the interactions between methanol monomers by sacrificing interactions with the surface. These results serve as a direct comparison of interaction strength, assembly, and chirality of methanol networks on Au(111), Cu(111), and Pt(111) which are catalytically relevant for methanol oxidation, steam reforming, and direct methanol fuel cells.

Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species.

PubMed

Magrach, Ainhoa; Senior, Rebecca A; Rogers, Andrew; Nurdin, Deddy; Benedick, Suzan; Laurance, William F; Santamaria, Luis; Edwards, David P

2016-03-16

Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the 'health' and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness, and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growing tree species, which could exacerbate the loss of timber value and carbon storage already associated with logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana-tree interaction networks if heavily infested trees (i.e. the most connected ones) were more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of 'extinction debt'. © 2016 The Author(s).

Selective logging in tropical forests decreases the robustness of liana–tree interaction networks to the loss of host tree species

PubMed Central

Magrach, Ainhoa; Senior, Rebecca A.; Rogers, Andrew; Nurdin, Deddy; Benedick, Suzan; Laurance, William F.; Santamaria, Luis; Edwards, David P.

2016-01-01

Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the ‘health’ and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness, and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growing tree species, which could exacerbate the loss of timber value and carbon storage already associated with logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana–tree interaction networks if heavily infested trees (i.e. the most connected ones) were more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of ‘extinction debt’. PMID:26936241

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

NASA Astrophysics Data System (ADS)

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.

Mapping Functional Brain Development: Building a Social Brain through Interactive Specialization

ERIC Educational Resources Information Center

Johnson, Mark H.; Grossmann, Tobias; Kadosh, Kathrin Cohen

2009-01-01

The authors review a viewpoint on human functional brain development, interactive specialization (IS), and its application to the emerging network of cortical regions referred to as the "social brain." They advance the IS view in 2 new ways. First, they extend IS into a domain to which it has not previously been applied--the emergence of social…

A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging

PubMed Central

Nakae, Ken; Ikegaya, Yuji; Ishikawa, Tomoe; Oba, Shigeyuki; Urakubo, Hidetoshi; Koyama, Masanori; Ishii, Shin

2014-01-01

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems. PMID:25393874

Randomizing Genome-Scale Metabolic Networks

PubMed Central

Samal, Areejit; Martin, Olivier C.

2011-01-01

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints. PMID:21779409

Network neuroscience

PubMed Central

Bassett, Danielle S; Sporns, Olaf

2017-01-01

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

Modelling protein functional domains in signal transduction using Maude

NASA Technical Reports Server (NTRS)

Sriram, M. G.

2003-01-01

Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.

The neural basis of hand gesture comprehension: A meta-analysis of functional magnetic resonance imaging studies.

PubMed

Yang, Jie; Andric, Michael; Mathew, Mili M

2015-10-01

Gestures play an important role in face-to-face communication and have been increasingly studied via functional magnetic resonance imaging. Although a large amount of data has been provided to describe the neural substrates of gesture comprehension, these findings have never been quantitatively summarized and the conclusion is still unclear. This activation likelihood estimation meta-analysis investigated the brain networks underpinning gesture comprehension while considering the impact of gesture type (co-speech gestures vs. speech-independent gestures) and task demand (implicit vs. explicit) on the brain activation of gesture comprehension. The meta-analysis of 31 papers showed that as hand actions, gestures involve a perceptual-motor network important for action recognition. As meaningful symbols, gestures involve a semantic network for conceptual processing. Finally, during face-to-face interactions, gestures involve a network for social emotive processes. Our finding also indicated that gesture type and task demand influence the involvement of the brain networks during gesture comprehension. The results highlight the complexity of gesture comprehension, and suggest that future research is necessary to clarify the dynamic interactions among these networks. Copyright © 2015 Elsevier Ltd. All rights reserved.