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Sample records for protein network coupled

  1. Contact rearrangements form coupled networks from local motions in allosteric proteins.

    PubMed

    Daily, Michael D; Upadhyaya, Tarak J; Gray, Jeffrey J

    2008-04-01

    Allosteric proteins bind an effector molecule at one site resulting in a functional change at a second site. We hypothesize that networks of contacts altered, formed, or broken are a significant contributor to allosteric communication in proteins. In this work, we identify which interactions change significantly between the residue-residue contact networks of two allosteric structures, and then organize these changes into graphs. We perform the analysis on 15 pairs of allosteric structures with effector and substrate each present in at least one of the two structures. Most proteins exhibit large, dense regions of contact rearrangement, and the graphs form connected paths between allosteric effector and substrate sites in five of these proteins. In the remaining 10 proteins, large-scale conformational changes such as rigid-body motions are likely required in addition to contact rearrangement networks to account for substrate-effector communication. On average, clusters which contain at least one substrate or effector molecule comprise 20% of the protein. These allosteric graphs are small worlds; that is, they typically have mean shortest path lengths comparable to those of corresponding random graphs and average clustering coefficients enhanced relative to those of random graphs. The networks capture 60-80% of known allostery-perturbing mutants in three proteins, and the metrics degree and closeness are statistically good discriminators of mutant residues from nonmutant residues within the networks in two of these three proteins. For two proteins, coevolving clusters of residues which have been hypothesized to be allosterically important differ from the regions with the most contact rearrangement. Residues and contacts which modulate normal mode fluctuations also often participate in the contact rearrangement networks. In summary, residue-residue contact rearrangement networks provide useful representations of the portions of allosteric pathways resulting from

  2. A Hybrid Model for Erythrocyte Membrane: A Single Unit of Protein Network Coupled with Lipid Bilayer

    PubMed Central

    Zhu, Qiang; Vera, Carlos; Asaro, Robert J.; Sche, Paul; Sung, L. Amy

    2007-01-01

    To investigate the nanomechanics of the erythrocyte membrane we developed a hybrid model that couples the actin-spectrin network to the lipid bilayer. This model features a Fourier space Brownian dynamics model of the bilayer, a Brownian dynamics model of the actin protofilament, and a modified wormlike-chain model of the spectrin (including a cable-dynamics model to predict the oscillation in tension). This model enables us to predict the nanomechanics of single or multiple units of the protein network, the lipid bilayer, and the effect of their interactions. The present work is focused on the attitude of the actin protofilament at the equilibrium states coupled with the elevations of the lipid bilayer through their primary linkage at the suspension complex in deformations. Two different actin-spectrin junctions are considered at the junctional complex. With a point-attachment junction, large pitch angles and bifurcation of yaw angles are predicted. Thermal fluctuations at bifurcation may lead to mode-switching, which may affect the network and the physiological performance of the membrane. In contrast, with a wrap-around junction, pitch angles remain small, and the occurrence of bifurcation is greatly reduced. These simulations suggest the importance of three-dimensional molecular junctions and the lipid bilayer/protein network coupling on cell membrane mechanics. PMID:17449663

  3. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

    PubMed Central

    Di Roberto, Raphaël B.; Chang, Belinda; Trusina, Ala; Peisajovich, Sergio G.

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor–ligand pair can evolve through network-altering mutations independently of receptor–ligand binding, and suggest a potential role for such mutations in disease. PMID:27487915

  4. Buried ionizable networks are an ancient hallmark of G protein-coupled receptor activation

    PubMed Central

    Isom, Daniel G.; Dohlman, Henrik G.

    2015-01-01

    Seven-transmembrane receptors (7TMRs) have evolved in prokaryotes and eukaryotes over hundreds of millions of years. Comparative structural analysis suggests that these receptors may share a remote evolutionary origin, despite their lack of sequence similarity. Here we used structure-based computations to compare 221 7TMRs from all domains of life. Unexpectedly, we discovered that these receptors contain spatially conserved networks of buried ionizable groups. In microbial 7TMRs these networks are used to pump ions across the cell membrane in response to light. In animal 7TMRs, which include light- and ligand-activated G protein-coupled receptors (GPCRs), homologous networks were found to be characteristic of activated receptor conformations. These networks are likely relevant to receptor function because they connect the ligand-binding pocket of the receptor to the nucleotide-binding pocket of the G protein. We propose that agonist and G protein binding facilitate the formation of these electrostatic networks and promote important structural rearrangements such as the displacement of transmembrane helix-6. We anticipate that robust classification of activated GPCR structures will aid the identification of ligands that target activated GPCR structural states. PMID:25902551

  5. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions.

    PubMed

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala; Peisajovich, Sergio G

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease. PMID:27487915

  6. The G Protein-Coupled Receptor Heterodimer Network (GPCR-HetNet) and Its Hub Components

    PubMed Central

    Borroto-Escuela, Dasiel O.; Brito, Ismel; Romero-Fernandez, Wilber; Di Palma, Michael; Oflijan, Julia; Skieterska, Kamila; Duchou, Jolien; Van Craenenbroeck, Kathleen; Suárez-Boomgaard, Diana; Rivera, Alicia; Guidolin, Diego; Agnati, Luigi F.; Fuxe, Kjell

    2014-01-01

    G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/~ismel/GPCR-Nets/index.html. PMID:24830558

  7. Coupled biopolymer networks

    NASA Astrophysics Data System (ADS)

    Schwarz, J. M.; Zhang, Tao

    2015-03-01

    The actin cytoskeleton provides the cell with structural integrity and allows it to change shape to crawl along a surface, for example. The actin cytoskeleton can be modeled as a semiflexible biopolymer network that modifies its morphology in response to both external and internal stimuli. Just inside the inner nuclear membrane of a cell exists a network of filamentous lamin that presumably protects the heart of the cell nucleus--the DNA. Lamins are intermediate filaments that can also be modeled as semiflexible biopolymers. It turns out that the actin cytoskeletal biopolymer network and the lamin biopolymer network are coupled via a sequence of proteins that bridge the outer and inner nuclear membranes. We, therefore, probe the consequences of such a coupling via numerical simulations to understand the resulting deformations in the lamin network in response to perturbations in the cytoskeletal network. Such study could have implications for mechanical mechanisms of the regulation of transcription, since DNA--yet another semiflexible polymer--contains lamin-binding domains, and, thus, widen the field of epigenetics.

  8. Structure-based network analysis of an evolved G protein-coupled receptor homodimer interface

    PubMed Central

    Nichols, Sara E; Hernández, Carlos X; Wang, Yi; McCammon, James Andrew

    2013-01-01

    Crystallographic structures and experimental assays of human CXC chemokine receptor type 4 (CXCR4) provide strong evidence for the capacity to homodimerize, potentially as a means of allosteric regulation. Even so, how this homodimer forms and its biological significance has yet to be fully characterized. By applying principles from network analysis, sequence-based approaches such as statistical coupling analysis to determine coevolutionary residues, can be used in conjunction with molecular dynamics simulations to identify residues relevant to dimerization. Here, the predominant coevolution sector lies along the observed dimer interface, suggesting functional relevance. Furthermore, coevolution scoring provides a basis for determining significant nodes, termed hubs, in the network formed by residues found along the interface of the homodimer. These node residues coincide with hotspots indicating potential druggability. Drug design efforts targeting such key residues could potentially result in modulation of binding and therapeutic benefits for disease states, such as lung cancers, lymphomas and latent HIV-1 infection. Furthermore, this method may be applied to any protein–protein interaction. PMID:23553730

  9. Coupled adaptive complex networks

    NASA Astrophysics Data System (ADS)

    Shai, S.; Dobson, S.

    2013-04-01

    Adaptive networks, which combine topological evolution of the network with dynamics on the network, are ubiquitous across disciplines. Examples include technical distribution networks such as road networks and the internet, natural and biological networks, and social science networks. These networks often interact with or depend upon other networks, resulting in coupled adaptive networks. In this paper we study susceptible-infected-susceptible (SIS) epidemic dynamics on coupled adaptive networks, where susceptible nodes are able to avoid contact with infected nodes by rewiring their intranetwork connections. However, infected nodes can pass the disease through internetwork connections, which do not change with time: The dependencies between the coupled networks remain constant. We develop an analytical formalism for these systems and validate it using extensive numerical simulation. We find that stability is increased by increasing the number of internetwork links, in the sense that the range of parameters over which both endemic and healthy states coexist (both states are reachable depending on the initial conditions) becomes smaller. Finally, we find a new stable state that does not appear in the case of a single adaptive network but only in the case of weakly coupled networks, in which the infection is endemic in one network but neither becomes endemic nor dies out in the other. Instead, it persists only at the nodes that are coupled to nodes in the other network through internetwork links. We speculate on the implications of these findings.

  10. G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

    PubMed Central

    Ichikawa, Osamu; Fujimoto, Kazushi; Yamada, Atsushi; Okazaki, Susumu; Yamazaki, Kazuto

    2016-01-01

    The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs. PMID:27187591

  11. G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation.

    PubMed

    Ichikawa, Osamu; Fujimoto, Kazushi; Yamada, Atsushi; Okazaki, Susumu; Yamazaki, Kazuto

    2016-01-01

    The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand-receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs. PMID:27187591

  12. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro- Receptors Using Neural Network Techniques.

    PubMed

    Agerskov, Claus; Mortensen, Rasmus M; Bohr, Henrik G

    2015-01-01

    A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors µ-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drugdesign. This is done by training on structural features, selected using a "minimum redundancy, maximum relevance"-test, and testing for successful prediction of categorized binding strength. An extensive comparison of the neural network performances was made in order to select the optimal architecture. Deep belief networks, trained with greedy learning algorithms, showed superior performance in prediction over the simple feedback NNs. The best networks obtained scores of more than 90 % accuracy in predicting the degree of binding drug molecules to the mentioned receptors and with a maximal Matthew`s coefficient of 0.925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical characteristics could give the lowest observed IC50 values, meaning largest bio-effect pr. nM substance, around 0.03-0.06 nM. These ligand characteristics could be total number of atoms, their types etc. In conclusion, deep belief networks trained on drug-molecule structures were demonstrated as powerful computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques. PMID:26463104

  13. Controlling allosteric networks in proteins

    NASA Astrophysics Data System (ADS)

    Dokholyan, Nikolay

    2013-03-01

    We present a novel methodology based on graph theory and discrete molecular dynamics simulations for delineating allosteric pathways in proteins. We use this methodology to uncover the structural mechanisms responsible for coupling of distal sites on proteins and utilize it for allosteric modulation of proteins. We will present examples where inference of allosteric networks and its rewiring allows us to ``rescue'' cystic fibrosis transmembrane conductance regulator (CFTR), a protein associated with fatal genetic disease cystic fibrosis. We also use our methodology to control protein function allosterically. We design a novel protein domain that can be inserted into identified allosteric site of target protein. Using a drug that binds to our domain, we alter the function of the target protein. We successfully tested this methodology in vitro, in living cells and in zebrafish. We further demonstrate transferability of our allosteric modulation methodology to other systems and extend it to become ligh-activatable.

  14. Quantitative phosphoproteomic analysis of signaling downstream of the prostaglandin e2/g-protein coupled receptor in human synovial fibroblasts: potential antifibrotic networks.

    PubMed

    Gerarduzzi, Casimiro; He, QingWen; Antoniou, John; Di Battista, John A

    2014-11-01

    The Prostaglandin E2 (PGE2) signaling mechanism within fibroblasts is of growing interest as it has been shown to prevent numerous fibrotic features of fibroblast activation with limited evidence of downstream pathways. To understand the mechanisms of fibroblasts producing tremendous amounts of PGE2 with autocrine effects, we apply a strategy of combining a wide-screening of PGE2-induced kinases with quantitative phosphoproteomics. Our large-scale proteomic approach identified a PKA signal transmitted through phosphorylation of its substrates harboring the R(R/X)X(S*/T*) motif. We documented 115 substrates, of which 72 had 89 sites with a 2.5-fold phosphorylation difference in PGE2-treated cells than in untreated cells, where approximately half of such sites were defined as being novel. They were compiled by networking software to focus on highlighted activities and to associate them with a functional readout of fibroblasts. The substrates were associated with a variety of cellular functions including cytoskeletal structures (migration/motility), regulators of G-protein coupled receptor function, protein kinases, and transcriptional/translational regulators. For the first time, we extended the PGE2 pathway into an elaborate network of interconnecting phosphoproteins, providing vital information to a once restricted signalosome. These data provide new insights into eicosanoid-initiated cell signaling with regards to the regulation of fibroblast activation and the identification of new targets for evidenced-based pharmacotherapy against fibrosis. PMID:25223752

  15. Coupled map networks as communication schemes

    NASA Astrophysics Data System (ADS)

    García, P.; Parravano, A.; Cosenza, M. G.; Jiménez, J.; Marcano, A.

    2002-04-01

    Networks of chaotic coupled maps are considered as string and language generators. It is shown that such networks can be used as encrypting systems where the cipher text contains information about the evolution of the network and also about the way to select the plain text symbols from the string associated with the network evolution. The secret key provides the network parameters, such as the coupling strengths.

  16. Support Networks of Dual Career Couples.

    ERIC Educational Resources Information Center

    Lloyd, Sally A.; And Others

    Although social networks play an important role in supporting families under stress, there is some evidence that families living a stressful dual career life style may have limited network resources. To describe support networks of dual career couples and to examine the relationship between the supportiveness of the network and satisfaction with…

  17. Chimeras in networks with purely local coupling

    NASA Astrophysics Data System (ADS)

    Laing, Carlo R.

    2015-11-01

    Chimera states in spatially extended networks of oscillators have some oscillators synchronized while the remainder are asynchronous. These states have primarily been studied in networks with nonlocal coupling, and more recently in networks with global coupling. Here, we present three networks with only local coupling (diffusive, to nearest neighbors) which are numerically found to support chimera states. One of the networks is analyzed using a self-consistency argument in the continuum limit, and this is used to find the boundaries of existence of a chimera state in parameter space.

  18. Universality in Protein Residue Networks

    PubMed Central

    Estrada, Ernesto

    2010-01-01

    Abstract Residue networks representing 595 nonhomologous proteins are studied. These networks exhibit universal topological characteristics as they belong to the topological class of modular networks formed by several highly interconnected clusters separated by topological cavities. There are some networks that tend to deviate from this universality. These networks represent small-size proteins having <200 residues. This article explains such differences in terms of the domain structure of these proteins. On the other hand, the topological cavities characterizing proteins residue networks match very well with protein binding sites. This study investigates the effect of the cutoff value used in building the residue network. For small cutoff values, <5 Å, the cavities found are very large corresponding almost to the whole protein surface. On the contrary, for large cutoff value, >10.0 Å, only very large cavities are detected and the networks look very homogeneous. These findings are useful for practical purposes as well as for identifying protein-like complex networks. Finally, this article shows that the main topological class of residue networks is not reproduced by random networks growing according to Erdös-Rényi model or the preferential attachment method of Barabási-Albert. However, the Watts-Strogatz model reproduces very well the topological class as well as other topological properties of residue network. A more biologically appealing modification of the Watts-Strogatz model to describe residue networks is proposed. PMID:20197043

  19. Estrogen Enhances Linkage in the Vascular Endothelial Calmodulin Network via a Feedforward Mechanism at the G Protein-coupled Estrogen Receptor 1.

    PubMed

    Tran, Quang-Kim; Firkins, Rachel; Giles, Jennifer; Francis, Sarah; Matnishian, Vahe; Tran, Phuong; VerMeer, Mark; Jasurda, Jake; Burgard, Michelle Ann; Gebert-Oberle, Briana

    2016-05-13

    Estrogen exerts many effects on the vascular endothelium. Calmodulin (CaM) is the transducer of Ca(2+) signals and is a limiting factor in cardiovascular tissues. It is unknown whether and how estrogen modifies endothelial functions via the network of CaM-dependent proteins. Here we show that 17β-estradiol (E2) up-regulates total CaM level in endothelial cells. Concurrent measurement of Ca(2+) and Ca(2+)-CaM indicated that E2 also increases free Ca(2+)-CaM. Pharmacological studies, gene silencing, and receptor expression-specific cell studies indicated that the G protein-coupled estrogen receptor 1 (GPER/GPR30) mediates these effects via transactivation of EGFR and subsequent MAPK activation. The outcomes were then examined on four distinct members of the intracellular CaM target network, including GPER/GPR30 itself and estrogen receptor α, the plasma membrane Ca(2+)-ATPase (PMCA), and endothelial nitric-oxide synthase (eNOS). E2 substantially increases CaM binding to estrogen receptor α and GPER/GPR30. Mutations that reduced CaM binding to GPER/GPR30 in separate binding domains do not affect GPER/GPR30-Gβγ preassociation but decrease GPER/GPR30-mediated ERK1/2 phosphorylation. E2 increases CaM-PMCA association, but the expected stimulation of Ca(2+) efflux is reversed by E2-stimulated tyrosine phosphorylation of PMCA. These effects sustain Ca(2+) signals and promote Ca(2+)-dependent CaM interactions with other CaM targets. Consequently, E2 doubles CaM-eNOS interaction and also promotes dual phosphorylation of eNOS at Ser-617 and Ser-1179. Calculations using in-cell and in vitro data revealed substantial individual and combined contribution of these effects to total eNOS activity. Taken together, E2 generates a feedforward loop via GPER/GPR30, which enhances Ca(2+)/CaM signals and functional linkage in the endothelial CaM target network. PMID:26987903

  20. Coupled actin-lamin biopolymer networks and protecting DNA

    NASA Astrophysics Data System (ADS)

    Zhang, Tao; Rocklin, D. Zeb; Mao, Xiaoming; Schwarz, J. M.

    The mechanical properties of cells are largely determined by networks of semiflexible biopolymers forming the cytoskeleton. Similarly, the mechanical properties of cell nuclei are also largely determined by networks of semiflexible biopolymers forming the nuclear cytoskeleton. In particular, a network of filamentous lamin sits just inside the inner nuclear membrane to presumably protect the heart of the cell nucleus--the DNA. It has been demonstrated over the past decade that the actin cytoskeletal biopolymer network and the lamin biopolymer network are coupled via a sequence of proteins bridging the outer and inner nuclear membranes, known as the LINC complex. We, therefore, probe the consequences of such a coupling in a model biopolymer network system via numerical simulations to understand the resulting deformations in the lamin network in response to perturbations in the actin cytoskeletal network. We find, for example, that the force transmission across the coupled system can depend sensitively on the concentration of LINC complexes. Such study could have implications for mechanical mechanisms of the regulation of transcription since DNA couples to lamin via lamin-binding domains so that deformations in the lamin network may result in deformations in the DNA.

  1. Information filtering on coupled social networks.

    PubMed

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

  2. Information Filtering on Coupled Social Networks

    PubMed Central

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

  3. Gradient systems on coupled cell networks

    NASA Astrophysics Data System (ADS)

    Manoel, Miriam; Roberts, Mark

    2015-10-01

    For networks of coupled dynamical systems we characterize admissible functions, that is, functions whose gradient is an admissible vector field. The schematic representation of a gradient network dynamical system is of an undirected cell graph, and we use tools from graph theory to deduce the general form of such functions, relating it to the topological structure of the graph defining the network. The coupling of pairs of dynamical systems cells is represented by edges of the graph, and from spectral graph theory we detect the existence and nature of equilibria of the gradient system from the critical points of the coupling function. In particular, we study fully synchronous and 2-state patterns of equilibria on regular graphs. These are two special types of equilibrium configurations for gradient networks. We also investigate equilibrium configurations of {{\\mathbf{S}}1} -invariant admissible functions on a ring of cells.

  4. The protein folding network

    NASA Astrophysics Data System (ADS)

    Rao, Francesco; Caflisch, Amedeo

    2004-03-01

    Networks are everywhere. The conformation space of a 20-residue antiparallel beta-sheet peptide [1], sampled by molecular dynamics simulations, is mapped to a network. Conformations are nodes of the network, and the transitions between them are links. As previously found for the World-Wide Web as well as for social and biological networks , the conformation space contains highly connected hubs like the native state which is the most populated free energy basin. Furthermore, the network shows a hierarchical modularity [2] which is consistent with the funnel mechanism of folding [3] and is not observed for a random heteropolymer lacking a native state. Here we show that the conformation space network describes the free energy landscape without requiring projections into arbitrarily chosen reaction coordinates. The network analysis provides a basis for understanding the heterogeneity of the folding transition state and the existence of multiple pathways. [1] P. Ferrara and A. Caflisch, Folding simulations of a three-stranded antiparallel beta-sheet peptide, PNAS 97, 10780-10785 (2000). [2] Ravasz, E. and Barabási, A. L. Hierarchical organization in complex networks. Phys. Rev. E 67, 026112 (2003). [3] Dill, K. and Chan, H From Levinthal to pathways to funnels. Nature Struct. Biol. 4, 10-19 (1997)

  5. Novel effects of FTY720 on perinuclear reorganization of keratin network induced by sphingosylphosphorylcholine: Involvement of protein phosphatase 2A and G-protein-coupled receptor-12.

    PubMed

    Park, Mi Kyung; Park, Soyeun; Kim, Hyun Ji; Kim, Eun Ji; Kim, So Yeon; Kang, Gyeoung Jin; Byun, Hyun Jung; Kim, Sang Hee; Lee, Ho; Lee, Chang Hoon

    2016-03-15

    Sphingosylphosphorylcholine (SPC) evokes perinuclear reorganization of keratin 8 (K8) filaments and regulates the viscoelasticity of metastatic cancer cells leading to enhanced migration. Few studies have addressed the compounds modulating the viscoelasticity of metastatic cancer cells. We studied the effects of sphingosine (SPH), sphingosine 1-phosphate (S1P), FTY720 and FTY720-phosphate (FTY720P) on SPC-induced K8 phosphorylation and reorganization using Western blot and confocal microscopy, and also evaluated the elasticity of PANC-1 cells by atomic force microscopy. FTY720, FTY720P, SPH, and S1P concentration-dependently inhibited SPC-evoked phosphorylation and reorganization of K8, and migration of PANC-1 cells. SPC triggered reduction and narrow distribution of elastic constant K and conversely, FTY720 blocked them. A common upstream regulator of JNK and ERK, protein phosphatase 2A (PP2A) expression was reduced by SPC, but was restored by FTY720 and FTY72P. Butyryl forskolin, a PP2A activator, suppressed SPC-induced K8 phosphorylation and okadaic acid, a PP2A inhibitor, induced K8 phosphorylation. Gene silencing of PP2A also led to K8 phosphorylation, reorganization and migration. We also investigated the involvement of GPR12, a high-affinity SPC receptor, in SPC-evoked keratin phosphorylation and reorganization. GPR12 siRNA suppressed the SPC-triggered phosphorylation and reorganization of K8. GPR12 overexpression stimulated keratin phosphorylation and reorganization even without SPC. FTY720 and FTY720P suppressed the GPR12-induced phosphorylation and reorganization of K8. The collective data indicates that FTY720 and FTY720P suppress SPC-induced phosphorylation and reorganization of K8 in PANC-1 cells by restoring the expression of PP2A via GPR12. These findings might be helpful in the development of compounds that modulate the viscoelasticity of metastatic cancer cells and various SPC actions. PMID:26872988

  6. Enhancing robustness of coupled networks under targeted recoveries

    PubMed Central

    Gong, Maoguo; Ma, Lijia; Cai, Qing; Jiao, Licheng

    2015-01-01

    Coupled networks are extremely fragile because a node failure of a network would trigger a cascade of failures on the entire system. Existing studies mainly focused on the cascading failures and the robustness of coupled networks when the networks suffer from attacks. In reality, it is necessary to recover the damaged networks, and there are cascading failures in recovery processes. In this study, firstly, we analyze the cascading failures of coupled networks during recoveries. Then, a recovery robustness index is presented for evaluating the resilience of coupled networks to cascading failures in the recovery processes. Finally, we propose a technique aiming at protecting several influential nodes for enhancing robustness of coupled networks under the recoveries, and adopt six strategies based on the potential knowledge of network centrality to find the influential nodes. Experiments on three coupling networks demonstrate that with a small number of influential nodes protected, the robustness of coupled networks under the recoveries can be greatly enhanced. PMID:25675980

  7. 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

  8. Model reduction for networks of coupled oscillators

    NASA Astrophysics Data System (ADS)

    Gottwald, Georg A.

    2015-05-01

    We present a collective coordinate approach to describe coupled phase oscillators. We apply the method to study synchronisation in a Kuramoto model. In our approach, an N-dimensional Kuramoto model is reduced to an n-dimensional ordinary differential equation with n ≪ N , constituting an immense reduction in complexity. The onset of both local and global synchronisation is reproduced to good numerical accuracy, and we are able to describe both soft and hard transitions. By introducing two collective coordinates, the approach is able to describe the interaction of two partially synchronised clusters in the case of bimodally distributed native frequencies. Furthermore, our approach allows us to accurately describe finite size scalings of the critical coupling strength. We corroborate our analytical results by comparing with numerical simulations of the Kuramoto model with all-to-all coupling networks for several distributions of the native frequencies.

  9. Regulation of heartbeat by G protein-coupled ion channels.

    PubMed

    Brown, A M

    1990-12-01

    The coupling of ion channels to receptors by G proteins is the subject of this American Physiological Society Walter B. Cannon Memorial "Physiology in Perspective" Lecture. This subject is particularly appropriate because it includes a molecular explanation of a homeostatic mechanism involving the autonomic nervous system and the latter subject preoccupied Dr. Cannon during most of his career. With the use of reconstitution methods, we and others have shown that heterotrimeric guanine nucleotide-binding (G) proteins couple receptors to ion channels by both membrane-delimited, direct pathways and cytoplasmic second messenger pathways. Furthermore, one set of receptors may be coupled to as many as three different sets of ion channels to form networks. Dual G protein pathways lead to the prediction of biphasic ion current responses in cell signaling, and this prediction was confirmed. In sinoatrial pacemaker cells, the pacemaking hyperpolarization-activated inward current (If) is directly regulated by the G proteins Gs and Go, and the two can act simultaneously. This could explain the classical observation that vagal inhibition of heart rate is greater during sympathetic stimulation. Because deactivation of the muscarinic response occurs much faster than the G protein alpha-subunit hydrolyzes guanosine 5'-triphosphate, we looked for accessory cellular factors. A surprising result was that the small monomeric ras G protein blocked the muscarinic pathway. The significance of this observation is unknown, but it appears that small and large G proteins may interact in ion channel signaling pathways. PMID:1701981

  10. Interdependencies and Causalities in Coupled Financial Networks

    PubMed Central

    Vodenska, Irena; Aoyama, Hideaki; Fujiwara, Yoshi; Iyetomi, Hiroshi; Arai, Yuta

    2016-01-01

    We explore the foreign exchange and stock market networks for 48 countries from 1999 to 2012 and propose a model, based on complex Hilbert principal component analysis, for extracting significant lead-lag relationships between these markets. The global set of countries, including large and small countries in Europe, the Americas, Asia, and the Middle East, is contrasted with the limited scopes of targets, e.g., G5, G7 or the emerging Asian countries, adopted by previous works. We construct a coupled synchronization network, perform community analysis, and identify formation of four distinct network communities that are relatively stable over time. In addition to investigating the entire period, we divide the time period into into “mild crisis,” (1999–2002), “calm,” (2003–2006) and “severe crisis” (2007–2012) sub-periods and find that the severe crisis period behavior dominates the dynamics in the foreign exchange-equity synchronization network. We observe that in general the foreign exchange market has predictive power for the global stock market performances. In addition, the United States, German and Mexican markets have forecasting power for the performances of other global equity markets. PMID:26977806

  11. Interdependencies and Causalities in Coupled Financial Networks.

    PubMed

    Vodenska, Irena; Aoyama, Hideaki; Fujiwara, Yoshi; Iyetomi, Hiroshi; Arai, Yuta

    2016-01-01

    We explore the foreign exchange and stock market networks for 48 countries from 1999 to 2012 and propose a model, based on complex Hilbert principal component analysis, for extracting significant lead-lag relationships between these markets. The global set of countries, including large and small countries in Europe, the Americas, Asia, and the Middle East, is contrasted with the limited scopes of targets, e.g., G5, G7 or the emerging Asian countries, adopted by previous works. We construct a coupled synchronization network, perform community analysis, and identify formation of four distinct network communities that are relatively stable over time. In addition to investigating the entire period, we divide the time period into into "mild crisis," (1999-2002), "calm," (2003-2006) and "severe crisis" (2007-2012) sub-periods and find that the severe crisis period behavior dominates the dynamics in the foreign exchange-equity synchronization network. We observe that in general the foreign exchange market has predictive power for the global stock market performances. In addition, the United States, German and Mexican markets have forecasting power for the performances of other global equity markets. PMID:26977806

  12. G-protein-coupled receptor heteromer dynamics

    PubMed Central

    Vilardaga, Jean-Pierre; Agnati, Luigi F.; Fuxe, Kjell; Ciruela, Francisco

    2010-01-01

    G-protein-coupled receptors (GPCRs) represent the largest family of cell surface receptors, and have evolved to detect and transmit a large palette of extracellular chemical and sensory signals into cells. Activated receptors catalyze the activation of heterotrimeric G proteins, which modulate the propagation of second messenger molecules and the activity of ion channels. Classically thought to signal as monomers, different GPCRs often pair up with each other as homo- and heterodimers, which have been shown to modulate signaling to G proteins. Here, we discuss recent advances in GPCR heteromer systems involving the kinetics of the early steps in GPCR signal transduction, the dynamic property of receptor–receptor interactions, and how the formation of receptor heteromers modulate the kinetics of G-protein signaling. PMID:21123619

  13. Cascade of failures in interdependent networks coupled by different type networks

    NASA Astrophysics Data System (ADS)

    Cheng, Zunshui; Cao, Jinde

    2015-07-01

    Modern systems are mostly coupled together. Therefore, they should be modeled as interdependent networks. In this paper, the robustness of interdependent networks coupled with different type networks is studied in detail under both targeted and random attack. The critical fraction of nodes leading to a complete fragmentation of two interdependent networks is analyzed. Some findings are summarized as: (i) For random attack problem, the existence criteria for the giant component in interdependent networks coupled by two different type networks are quite different from those coupled by the same type networks. Different type coupled networks are more vulnerable than the same type coupled-networks. (ii) For targeted attack problem, if the highly connected nodes are protected and only the lowly connected nodes failed, the system leads to a first order percolation phase transition for different type coupled-networks, and a second transition for same type coupled-networks as well. The available result implies that different type coupled-networks are difficult to defend by strategies such as protecting the high degree nodes that can be useful to significantly improve robustness of the same type coupled-networks. (iii) For targeted attack problem, when the lowly connected nodes are protected and only the highly connected nodes failed, coupled scale free networks become more vulnerable than the others.

  14. Sparse repulsive coupling enhances synchronization in complex networks.

    PubMed

    Leyva, I; Sendiña-Nadal, I; Almendral, J A; Sanjuán, M A F

    2006-11-01

    Through the last years, different strategies to enhance synchronization in complex networks have been proposed. In this work, we show that synchronization of nonidentical dynamical units that are attractively coupled in a small-world network is strongly improved by just making phase-repulsive a tiny fraction of the couplings. By a purely topological analysis that does not depend on the dynamical model, we link the emerging dynamical behavior with the structural properties of the sparsely coupled repulsive network. PMID:17279973

  15. Symmetry-broken states on networks of coupled oscillators

    NASA Astrophysics Data System (ADS)

    Jiang, Xin; Abrams, Daniel M.

    2016-05-01

    When identical oscillators are coupled together in a network, dynamical steady states are often assumed to reflect network symmetries. Here, we show that alternative persistent states may also exist that break the symmetries of the underlying coupling network. We further show that these symmetry-broken coexistent states are analogous to those dubbed "chimera states," which can occur when identical oscillators are coupled to one another in identical ways.

  16. Investigation of a protein complex network

    NASA Astrophysics Data System (ADS)

    Mashaghi, A. R.; Ramezanpour, A.; Karimipour, V.

    2004-09-01

    The budding yeast Saccharomyces cerevisiae is the first eukaryote whose genome has been completely sequenced. It is also the first eukaryotic cell whose proteome (the set of all proteins) and interactome (the network of all mutual interactions between proteins) has been analyzed. In this paper we study the structure of the yeast protein complex network in which weighted edges between complexes represent the number of shared proteins. It is found that the network of protein complexes is a small world network with scale free behavior for many of its distributions. However we find that there are no strong correlations between the weights and degrees of neighboring complexes. To reveal non-random features of the network we also compare it with a null model in which the complexes randomly select their proteins. Finally we propose a simple evolutionary model based on duplication and divergence of proteins.

  17. Erosion of synchronization: Coupling heterogeneity and network structure

    NASA Astrophysics Data System (ADS)

    Skardal, Per Sebastian; Taylor, Dane; Sun, Jie; Arenas, Alex

    2016-06-01

    We study the dynamics of network-coupled phase oscillators in the presence of coupling frustration. It was recently demonstrated that in heterogeneous network topologies, the presence of coupling frustration causes perfect phase synchronization to become unattainable even in the limit of infinite coupling strength. Here, we consider the important case of heterogeneous coupling functions and extend previous results by deriving analytical predictions for the total erosion of synchronization. Our analytical results are given in terms of basic quantities related to the network structure and coupling frustration. In addition to fully heterogeneous coupling, where each individual interaction is allowed to be distinct, we also consider partially heterogeneous coupling and homogeneous coupling in which the coupling functions are either unique to each oscillator or identical for all network interactions, respectively. We demonstrate the validity of our theory with numerical simulations of multiple network models, and highlight the interesting effects that various coupling choices and network models have on the total erosion of synchronization. Finally, we consider some special network structures with well-known spectral properties, which allows us to derive further analytical results.

  18. 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

  19. Protein interaction networks from literature mining

    NASA Astrophysics Data System (ADS)

    Ihara, Sigeo

    2005-03-01

    The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

  20. Pinning impulsive directed coupled delayed dynamical network and its applications

    NASA Astrophysics Data System (ADS)

    Lin, Chunnan; Wu, Quanjun; Xiang, Lan; Zhou, Jin

    2015-01-01

    The main objective of the present paper is to further investigate pinning synchronisation of a complex delayed dynamical network with directionally coupling by a single impulsive controller. By developing the analysis procedure of pinning impulsive stability for undirected coupled dynamical network previously, some simple yet general criteria of pinning impulsive synchronisation for such directed coupled network are derived analytically. It is shown that a single impulsive controller can always pin a given directed coupled network to a desired homogenous solution, including an equilibrium point, a periodic orbit, or a chaotic orbit. Subsequently, the theoretical results are illustrated by a directed small-world complex network which is a cellular neural network (CNN) and a directed scale-free complex network with the well-known Hodgkin-Huxley neuron oscillators. Numerical simulations are finally given to demonstrate the effectiveness of the proposed control methodology.

  1. Signaling through G protein coupled receptors

    PubMed Central

    2009-01-01

    Heterotrimeric G proteins (Gα, Gβ/Gγ subunits) constitute one of the most important components of cell signaling cascade. G Protein Coupled Receptors (GPCRs) perceive many extracellular signals and transduce them to heterotrimeric G proteins, which further transduce these signals intracellular to appropriate downstream effectors and thereby play an important role in various signaling pathways. GPCRs exist as a superfamily of integral membrane protein receptors that contain seven transmembrane α-helical regions, which bind to a wide range of ligands. Upon activation by a ligand, the GPCR undergoes a conformational change and then activate the G proteins by promoting the exchange of GDP/GTP associated with the Gα subunit. This leads to the dissociation of Gβ/Gγ dimer from Gα. Both these moieties then become free to act upon their downstream effectors and thereby initiate unique intracellular signaling responses. After the signal propagation, the GTP of Gα-GTP is hydrolyzed to GDP and Gα becomes inactive (Gα-GDP), which leads to its re-association with the Gβ/Gγ dimer to form the inactive heterotrimeric complex. The GPCR can also transduce the signal through G protein independent pathway. GPCRs also regulate cell cycle progression. Till to date thousands of GPCRs are known from animal kingdom with little homology among them, but only single GPCR has been identified in plant system. The Arabidopsis GPCR was reported to be cell cycle regulated and also involved in ABA and in stress signaling. Here I have described a general mechanism of signal transduction through GPCR/G proteins, structure of GPCRs, family of GPCRs and plant GPCR and its role. PMID:19826234

  2. A Network Synthesis Model for Generating Protein Interaction Network Families

    PubMed Central

    Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun

    2012-01-01

    In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein–protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/. PMID:22912671

  3. Profiling of Protein Interaction Networks of Protein Complexes Using Affinity Purification and Quantitative Mass Spectrometry*

    PubMed Central

    Kaake, Robyn M.; Wang, Xiaorong; Huang, Lan

    2010-01-01

    Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer. Recent evidence has suggested that protein interaction interfaces describe a new class of attractive targets for drug development. Full characterization of protein interaction networks of protein complexes and their dynamics in response to various cellular cues will provide essential information for us to understand how protein complexes work together in cells to maintain cell viability and normal homeostasis. Affinity purification coupled with quantitative mass spectrometry has become the primary method for studying in vivo protein interactions of protein complexes and whole organism proteomes. Recent developments in sample preparation and affinity purification strategies allow the capture, identification, and quantification of protein interactions of protein complexes that are stable, dynamic, transient, and/or weak. Current efforts have mainly focused on generating reliable, reproducible, and high confidence protein interaction data sets for functional characterization. The availability of increasing amounts of information on protein interactions in eukaryotic systems and new bioinformatics tools allow functional analysis of quantitative protein interaction data to unravel the biological significance of the identified protein interactions. Existing studies in this area have laid a solid foundation toward generating a complete map of in vivo protein interaction networks of protein complexes in cells or tissues. PMID:20445003

  4. Dynamic network analysis of protein interactions

    NASA Astrophysics Data System (ADS)

    Almaas, Eivind; Deri, Joya

    2007-03-01

    Network approaches have recently become a popular tool to study complex systems such as cellular metabolism and protein interactions. A substantial number of analyses of the protein interaction network (PIN) of the yeast Saccharomyces cerevisiae have considered this network as a static entity, not taking the network's dynamic nature into account. Here, we examine the time-variation of gene regulation superimposed on the PIN by defining mRNA expression profiles throughout the cell cycle as node weights. To characterize these network dynamics, we have both developed a set of novel network measures as well as studied previously published measures for weighted networks. We expect that our approach will provide a deeper understanding of protein regulation during the cell cycle.

  5. Exact Computation of Probability Landscape of Stochastic Networks of Single Input and Coupled Toggle Switch Modules

    PubMed Central

    Terebus, Anna; Cao, Youfang; Liang, Jie

    2016-01-01

    Gene regulatory networks depict the interactions between genes, proteins, and other components of the cell. These interactions often are stochastic that can influence behavior of the cells. Discrete Chemical Master Equation (dCME) provides a general framework for understanding the stochastic nature of these networks. However solving dCME is challenging due to the enormous state space, one effective approach is to study the behavior of individual modules of the stochastic network. Here we used the finite buffer dCME method and directly calculated the exact steady state probability landscape for the two stochastic networks of Single Input and Coupled Toggle Switch Modules. The first example is a switch network consisting of three genes, and the second example is a double switching network consisting of four coupled genes. Our results show complex switching behavior of these networks can be quantified. PMID:25571172

  6. How do oncoprotein mutations rewire protein-protein interaction networks?

    PubMed

    Bowler, Emily H; Wang, Zhenghe; Ewing, Rob M

    2015-01-01

    The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype. PMID:26325016

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

    PubMed

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

    2015-06-01

    Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. PMID:25787689

  8. Systematic computational prediction of protein interaction networks.

    PubMed

    Lees, J G; Heriche, J K; Morilla, I; Ranea, J A; Orengo, C A

    2011-06-01

    Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use. PMID:21572181

  9. Optimized Null Model for Protein Structure Networks

    PubMed Central

    Lappe, Michael; Pržulj, Nataša

    2009-01-01

    Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by

  10. Extreme multifunctional proteins identified from a human protein interaction network

    PubMed Central

    Chapple, Charles E.; Robisson, Benoit; Spinelli, Lionel; Guien, Céline; Becker, Emmanuelle; Brun, Christine

    2015-01-01

    Moonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of ‘extreme multifunctional' proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3% of the human interactome). We show that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins. PMID:26054620

  11. Coupling entropy of co-processing model on social networks

    NASA Astrophysics Data System (ADS)

    Zhang, Zhanli

    2015-08-01

    Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

  12. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    SciTech Connect

    MASLOV,S.SNEPPEN,K.

    2003-11-17

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

  13. Complex network synchronization of chaotic systems with delay coupling

    SciTech Connect

    Theesar, S. Jeeva Sathya Ratnavelu, K.

    2014-03-05

    The study of complex networks enables us to understand the collective behavior of the interconnected elements and provides vast real time applications from biology to laser dynamics. In this paper, synchronization of complex network of chaotic systems has been studied. Every identical node in the complex network is assumed to be in Lur’e system form. In particular, delayed coupling has been assumed along with identical sector bounded nonlinear systems which are interconnected over network topology.

  14. Transient Spatiotemporal Chaos in a Synaptically Coupled Neural Network

    NASA Astrophysics Data System (ADS)

    Lafranceschina, Jacopo; Wackerbauer, Renate

    2014-03-01

    Spatiotemporal chaos is transient in a diffusively coupled Morris-Lecar neural network. This study shows that the addition of synaptic coupling in the ring network reduces the average lifetime of spatiotemporal chaos for small to intermediate coupling strength and almost all numbers of synapses. For large coupling strength, close to the threshold of excitation, the average lifetime increases beyond the value for only diffusive coupling, and the collapse to the rest state dominates over the collapse to a traveling pulse state. The regime of spatiotemporal chaos is characterized by a slightly increasing Lyaponov exponent and degree of phase coherence as the number of synaptic links increases. The presence of transient spatiotemporal chaos in a network of coupled neurons and the associated chaotic saddle provides a possibility for switching between metastable states observed in information processing and brain function. This research is supported by the University of Alaska Fairbanks.

  15. NAPS: Network Analysis of Protein Structures.

    PubMed

    Chakrabarty, Broto; Parekh, Nita

    2016-07-01

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

  16. Synchronization of complex networks coupled by periodically intermittent noise

    NASA Astrophysics Data System (ADS)

    Li, Shuang; Yan, Huiyun; Li, Jiaorui

    2016-04-01

    Noise is ubiquitous in real systems, so it is important to investigate the effects of noise on the network system. In this paper, the synchronization of complex network coupled by periodically intermittent noise is investigated and a sufficient condition of noise-induced synchronization is obtained analytically via stability theory of stochastic differential equation. The sufficient condition provides a theoretical reference for the analysis of the impact of coupling noise intensity, duration, coupled oscillator number and other parameters on the synchronization behavior. As examples, Rossler-like and Lorenz network systems are presented to verify the theoretical result.

  17. Dynamics of Coupled Cell Networks: Synchrony, Heteroclinic Cycles and Inflation

    NASA Astrophysics Data System (ADS)

    Aguiar, M.; Ashwin, P.; Dias, A.; Field, M.

    2011-04-01

    We consider the dynamics of small networks of coupled cells. We usually assume asymmetric inputs and no global or local symmetries in the network and consider equivalence of networks in this setting; that is, when two networks with different architectures give rise to the same set of possible dynamics. Focussing on transitive (strongly connected) networks that have only one type of cell (identical cell networks) we address three questions relating the network structure to dynamics. The first question is how the structure of the network may force the existence of invariant subspaces (synchrony subspaces). The second question is how these invariant subspaces can support robust heteroclinic attractors. Finally, we investigate how the dynamics of coupled cell networks with different structures and numbers of cells can be related; in particular we consider the sets of possible "inflations" of a coupled cell network that are obtained by replacing one cell by many of the same type, in such a way that the original network dynamics is still present within a synchrony subspace. We illustrate the results with a number of examples of networks of up to six cells.

  18. Identifying the hub proteins from complicated membrane protein network systems.

    PubMed

    Shen, Yi-Zhen; Ding, Yong-Sheng; Gu, Quan; Chou, Kuo-Chen

    2010-05-01

    The so-called "hub proteins" are those proteins in a protein-protein interaction network system that have remarkably higher interaction relations (or degrees) than the others. Therefore, the information of hub proteins can provide very useful insights for selecting or prioritizing targets during drug development. In this paper, by combining the multi-agent-based method with the graphical spectrum analysis and immune-genetic algorithm, a novel simulator for identifying the hub proteins from membrane protein interaction networks is proposed. As a demonstration of using the simulator, two hub membrane proteins, YPL227C and YIL147C, were identified from a complicated network system consisting of 1500 membrane proteins. Meanwhile, along with the two identified hub proteins, their molecular functions, biological processes, and cellular components were also revealed. It is anticipated that the hub-protein-simulator may become a very useful tool for system biology and drug development, particularly in deciphering unknown protein functions, determining protein complexes, and in identifying the key targets from a complicated disease system. PMID:20507268

  19. Restoration of rhythmicity in diffusively coupled dynamical networks

    PubMed Central

    Zou, Wei; Senthilkumar, D. V.; Nagao, Raphael; Kiss, István Z.; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen

    2015-01-01

    Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks. PMID:26173555

  20. Restoration of rhythmicity in diffusively coupled dynamical networks.

    PubMed

    Zou, Wei; Senthilkumar, D V; Nagao, Raphael; Kiss, István Z; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen

    2015-01-01

    Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks. PMID:26173555

  1. Restoration of rhythmicity in diffusively coupled dynamical networks

    NASA Astrophysics Data System (ADS)

    Zou, Wei; Senthilkumar, D. V.; Nagao, Raphael; Kiss, István Z.; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen

    2015-07-01

    Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks.

  2. Distributed coupling complexity in a weakly coupled oscillatory network with associative properties

    NASA Astrophysics Data System (ADS)

    Kostorz, Kathrin; Hölzel, Robert W.; Krischer, Katharina

    2013-08-01

    We present a novel architecture of an oscillatory neural network capable of performing pattern recognition tasks. Two established strategies for obtaining associative properties in oscillatory networks invoke either a physical, time constant or a global, dynamical all-to-all coupling. Our network distributes the complexity of the coupling between the spatial and the temporal domain. Instead of {O}(N^2) physical connections or a global connection with {O}(N^2) frequency components, each of the N oscillators receives an individual coupling signal which is composed of N - 1 frequency components. We demonstrate that such a network can be built with analog electronic oscillators and possesses reliable pattern recognition properties. Theoretical analysis shows that the scalability is in fact superior to the dynamic global coupling approach, while its physical complexity is greatly reduced compared to the individual time constant coupling.

  3. The centrality of cancer proteins in human protein-protein interaction network: a revisit.

    PubMed

    Xiong, Wei; Xie, Luyu; Zhou, Shuigeng; Liu, Hui; Guan, Jihong

    2014-01-01

    Topological analysis of protein-protein interaction (PPI) networks has been widely applied to the investigation on cancer mechanisms. However, there is still a debate on whether cancer proteins exhibit more topological centrality compared to the other proteins in the human PPI network. To resolve this debate, we first identified four sets of human proteins, and then mapped these proteins into the yeast PPI network by homologous genes. Finally, we compared these proteins' properties in human and yeast PPI networks. Experiments over two real datasets demonstrated that cancer proteins tend to have higher degree and smaller clustering coefficient than non-cancer proteins. Experimental results also validated that cancer proteins have larger betweenness centrality compared to the other proteins on the STRING dataset. However, on the BioGRID dataset, the average betweenness centrality of cancer proteins is larger than that of disease and control proteins, but smaller than that of essential proteins. PMID:24878726

  4. Delayed feedback control of synchronization in weakly coupled oscillator networks

    NASA Astrophysics Data System (ADS)

    Novičenko, Viktor

    2015-08-01

    We study control of synchronization in weakly coupled oscillator networks by using a phase-reduction approach. Starting from a general class of limit-cycle oscillators we derive a phase model, which shows that delayed feedback control changes effective coupling strengths and effective frequencies. We derive the analytical condition for critical control gain, where the phase dynamics of the oscillator becomes extremely sensitive to any perturbations. As a result the network can attain phase synchronization even if the natural interoscillatory couplings are small. In addition, we demonstrate that delayed feedback control can disrupt the coherent phase dynamic in synchronized networks. The validity of our results is illustrated on networks of diffusively coupled Stuart-Landau and FitzHugh-Nagumo models.

  5. Transient electrical coupling regulates formation of neuronal networks.

    PubMed

    Szabo, Theresa M; Zoran, Mark J

    2007-01-19

    Electrical synapses are abundant before and during developmental windows of intense chemical synapse formation, and might therefore contribute to the establishment of neuronal networks. Transient electrical coupling develops and is then eliminated between regenerating Helisoma motoneurons 110 and 19 during a period of 48-72 h in vivo and in vitro following nerve injury. An inverse relationship exists between electrical coupling and chemical synaptic transmission at these synapses, such that the decline in electrical coupling is coincident with the emergence of cholinergic synaptic transmission. In this study, we have generated two- and three-cell neuronal networks to test whether predicted synaptogenic capabilities were affected by previous synaptic interactions. Electrophysiological analyses demonstrated that synapses formed in three-cell neuronal networks were not those predicted based on synaptogenic outcomes in two-cell networks. Thus, new electrical and chemical synapse formation within a neuronal network is dependent on existing connectivity of that network. In addition, new contacts formed with established networks have little impact on these existing connections. These results suggest that network-dependent mechanisms, particularly those mediated by gap junctional coupling, regulate synapse formation within simple neural networks. PMID:17156754

  6. Evolutionarily Conserved Coupling of Adaptive and Excitable Networks Mediates Eukaryotic Chemotaxis

    PubMed Central

    Wang, Mingjie; Shi, Changji; Iglesias, Pablo A.; Devreotes, Peter N.

    2014-01-01

    Numerous models explain how cells sense and migrate toward shallow chemoattractant gradients. Studies show that an excitable signal transduction network acts as a pacemaker that controls the cytoskeleton to drive motility. Here we show that this network is required to link stimuli to actin polymerization and chemotactic motility and we distinguish the various models of chemotaxis. First, signaling activity is suppressed toward the low side in a gradient or following removal of uniform chemoattractant. Second, signaling activities display a rapid shut off and a slower adaptation during which responsiveness to subsequent test stimuli decline. Simulations of various models indicate that these properties require coupled adaptive and excitable networks. Adaptation involves a G-protein independent inhibitor since stimulation of cells lacking G-protein function suppresses basal activities. The salient features of the coupled networks were observed for different chemoattractants in Dictyostelium and in human neutrophils, suggesting an evolutionarily conserved mechanism for eukaryotic chemotaxis. PMID:25346418

  7. Control of coupled oscillator networks with application to microgrid technologies.

    PubMed

    Skardal, Per Sebastian; Arenas, Alex

    2015-08-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  8. Control of coupled oscillator networks with application to microgrid technologies

    PubMed Central

    Skardal, Per Sebastian; Arenas, Alex

    2015-01-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions—a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  9. Energy-coupled outer membrane transport proteins and regulatory proteins.

    PubMed

    Braun, Volkmar; Endriss, Franziska

    2007-06-01

    FhuA and FecA are two examples of energy-coupled outer membrane import proteins of gram-negative bacteria. FhuA transports iron complexed by the siderophore ferrichrome and serves as a receptor for phages, a toxic bacterial peptide, and a toxic protein. FecA transports diferric dicitrate and regulates transcription of an operon encoding five ferric citrate (Fec) transport genes. Properties of FhuA mutants selected according to the FhuA crystal structure are described. FhuA mutants in the TonB box, the hatch, and the beta-barrel are rather robust. TonB box mutants in FhuA FecA, FepA, Cir, and BtuB are compared; some mutations are suppressed by mutations in TonB. Mutant studies have not revealed a ferrichrome diffusion pathway, and tolerance to mutations in the region linking the TonB box to the hatch does not disclose a mechanism for how energy transfer from the cytoplasmic membrane to FhuA changes the conformation of FhuA such that bound substrates are released, the pore is opened, and substrates enter the periplasm, or how surface loops change their conformation such that TonB-dependent phages bind irreversibly and release their DNA into the cells. The FhuA and FecA crystal structures do not disclose the mechanism of these proteins, but they provide important information for specific functional studies. FecA is also a regulatory protein that transduces a signal from the cell surface into the cytoplasm. The interacting subdomains of the proteins in the FecA --> FecR --> FecI --> RNA polymerase signal transduction pathway resulting in fecABCDE transcription have been determined. Energy-coupled transporters transport not only iron and vitamin B12, but also other substrates of very low abundance such as sugars across the outer membrane; transcription regulation of the transport genes may occur similarly to that of the Fec transport genes. PMID:17370038

  10. Targeted attack on networks coupled by connectivity and dependency links

    NASA Astrophysics Data System (ADS)

    Du, Ruijin; Dong, Gaogao; Tian, Lixin; Liu, Runran

    2016-05-01

    Coupled systems used to increase capacity were shown beneficial as long as it does not open pathways to cascades. Previous studies on the robustness of coupled networks except for interdependent networks are almost the cases of random attack. Many challenges remain exist in targeted-attack problem of coupled networks. Since nodes within coupled networks show different functions for each network, this paper both analytically and numerically analyzed the robustness of coupled networks under three types of targeted attacking strategies, including attacking on nodes by considering internal and external degree, internal degree only, and external degree only. For coupled network with both interdependent and interconnected links, all degree distributions of intra- and inter-connectivity links are Poissonian, we find the system undergoes from second to first order phase transition as coupling strength q increases. The fraction of nodes in the giant component P∞ at stable state, the critical phase transition threshold pc (first order threshold pcI and second order threshold pcII), and the critical point (pc ,qc) separating the first and second order phase transitions are analytically obtained for three types of attacking strategies with attacking probability parameter α = 0 , 1. We also find the system becomes more vulnerable as the average degree of intra-links k ¯ or inter-links K ¯ decreases. Therefore, the minimum average degrees k¯min and K¯min to maintain the system stable are obtained for the case of α = 0 , 1. Moreover, we discussed three typical cases of coupled networks, interdependent networks (K ¯ = 0), interacting networks (q = 0) and bipartite network (k = 0, q = 0), the analytical expressions of P∞, pc and (pc ,qc) (only for interdependent) are given respectively. Besides, we study the equivalence relations between interdependent networks and coupled networks with connectivity and dependency links for the same pc. The results imply that we can

  11. Stimulus-dependent synchronization in delayed-coupled neuronal networks

    PubMed Central

    Esfahani, Zahra G.; Gollo, Leonardo L.; Valizadeh, Alireza

    2016-01-01

    Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand. PMID:27001428

  12. Stimulus-dependent synchronization in delayed-coupled neuronal networks.

    PubMed

    Esfahani, Zahra G; Gollo, Leonardo L; Valizadeh, Alireza

    2016-01-01

    Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand. PMID:27001428

  13. Evolution of synchrony under combination of coupled cell networks

    NASA Astrophysics Data System (ADS)

    Aguiar, M. A. D.; Ruan, H.

    2012-11-01

    A natural way of modelling large coupled cell networks is to combine smaller networks through binary network operations. In this paper, we consider several non-product binary operations on networks such as join and coalescence, and examine the evolution of the lattice of synchrony subspaces under these operations. Classification results are obtained for synchrony subspaces of the combined network, which clarify the relation between the lattice of synchrony subspaces of the combined network and its components. Yet, in the case when the initial networks have the same edge type, this classification only applies to those synchrony subspaces that are compatible with respect to the considered operation. Based on the classification results, we give examples to show how the lattice of synchrony subspaces of the combined network can be reconstructed using the initial ones. Also, we show how the classification results can be applied to analyse the evolutionary fitness of synchrony patterns.

  14. Multiplexed imaging of intracellular protein networks.

    PubMed

    Grecco, Hernán E; Imtiaz, Sarah; Zamir, Eli

    2016-08-01

    Cellular functions emerge from the collective action of a large number of different proteins. Understanding how these protein networks operate requires monitoring their components in intact cells. Due to intercellular and intracellular molecular variability, it is important to monitor simultaneously multiple components at high spatiotemporal resolution. However, inherent trade-offs narrow the boundaries of achievable multiplexed imaging. Pushing these boundaries is essential for a better understanding of cellular processes. Here the motivations, challenges and approaches for multiplexed imaging of intracellular protein networks are discussed. © 2016 International Society for Advancement of Cytometry. PMID:27183498

  15. G Protein-Coupled Receptors in Cancer

    PubMed Central

    Bar-Shavit, Rachel; Maoz, Myriam; Kancharla, Arun; Nag, Jeetendra Kumar; Agranovich, Daniel; Grisaru-Granovsky, Sorina; Uziely, Beatrice

    2016-01-01

    Despite the fact that G protein-coupled receptors (GPCRs) are the largest signal-conveying receptor family and mediate many physiological processes, their role in tumor biology is underappreciated. Numerous lines of evidence now associate GPCRs and their downstream signaling targets in cancer growth and development. Indeed, GPCRs control many features of tumorigenesis, including immune cell-mediated functions, proliferation, invasion and survival at the secondary site. Technological advances have further substantiated GPCR modifications in human tumors. Among these are point mutations, gene overexpression, GPCR silencing by promoter methylation and the number of gene copies. At this point, it is imperative to elucidate specific signaling pathways of “cancer driver” GPCRs. Emerging data on GPCR biology point to functional selectivity and “biased agonism”; hence, there is a diminishing enthusiasm for the concept of “one drug per GPCR target” and increasing interest in the identification of several drug options. Therefore, determining the appropriate context-dependent conformation of a functional GPCR as well as the contribution of GPCR alterations to cancer development remain significant challenges for the discovery of dominant cancer genes and the development of targeted therapeutics. PMID:27529230

  16. Crystallization of G Protein-Coupled Receptors

    PubMed Central

    Salom, David; Padayatti, Pius S.; Palczewski, Krzysztof

    2015-01-01

    Oligomerization is one of several mechanisms that can regulate the activity of G protein-coupled receptors (GPCRs), but little is known about the structure of GPCR oligomers. Crystallography and NMR are the only methods able to reveal the details of receptor–receptor interactions at an atomic level, and several GPCR homodimers already have been described from crystal structures. Two clusters of symmetric interfaces have been identified from these structures that concur with biochemical data, one involving helices I, II, and VIII and the other formed mainly by helices V and VI. In this chapter, we describe the protocols used in our laboratory for the crystallization of rhodopsin and the β2-adrenergic receptor (β2-AR). For bovine rhodopsin, we developed a new purification strategy including a (NH4)2SO4-induced phase separation that proved essential to obtain crystals of photoactivated rhodopsin containing parallel dimers. Crystallization of native bovine rhodopsin was achieved by the classic vapor-diffusion technique. For β2-AR, we developed a purification strategy based on previously published protocols employing a lipidic cubic phase to obtain diffracting crystals of a β2-AR/T4-lysozyme chimera bound to the antagonist carazolol. PMID:24143992

  17. Perceptual grouping by entrainment in coupled Kuramoto oscillator networks.

    PubMed

    Meier, Martin; Haschke, Robert; Ritter, Helge J

    2014-01-01

    In this article we present a network composed of coupled Kuramoto oscillators, which is able to solve a broad spectrum of perceptual grouping tasks. Based on attracting and repelling interactions between these oscillators, the network dynamics forms various phase-synchronized clusters of oscillators corresponding to individual groups of similar input features. The degree of similarity between features is determined by a set of underlying receptive fields, which are learned directly from the feature domain. After illustrating the theoretical principles of the network, the approach is evaluated in an image segmentation task. Furthermore, the influence of a varying degree of sparse couplings is evaluated. PMID:24571099

  18. Interface-Resolved Network of Protein-Protein Interactions

    PubMed Central

    Johnson, Margaret E.; Hummer, Gerhard

    2013-01-01

    We define an interface-interaction network (IIN) to capture the specificity and competition between protein-protein interactions (PPI). This new type of network represents interactions between individual interfaces used in functional protein binding and thereby contains the detail necessary to describe the competition and cooperation between any pair of binding partners. Here we establish a general framework for the construction of IINs that merges computational structure-based interface assignment with careful curation of available literature. To complement limited structural data, the inclusion of biochemical data is critical for achieving the accuracy and completeness necessary to analyze the specificity and competition between the protein interactions. Firstly, this procedure provides a means to clarify the information content of existing data on purported protein interactions and to remove indirect and spurious interactions. Secondly, the IIN we have constructed here for proteins involved in clathrin-mediated endocytosis (CME) exhibits distinctive topological properties. In contrast to PPI networks with their global and relatively dense connectivity, the fragmentation of the IIN into distinctive network modules suggests that different functional pressures act on the evolution of its topology. Large modules in the IIN are formed by interfaces sharing specificity for certain domain types, such as SH3 domains distributed across different proteins. The shared and distinct specificity of an interface is necessary for effective negative and positive design of highly selective binding targets. Lastly, the organization of detailed structural data in a network format allows one to identify pathways of specific binding interactions and thereby predict effects of mutations at specific surfaces on a protein and of specific binding inhibitors, as we explore in several examples. Overall, the endocytosis IIN is remarkably complex and rich in features masked in the coarser

  19. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    PubMed

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  20. Regular synchrony lattices for product coupled cell networks

    NASA Astrophysics Data System (ADS)

    Aguiar, Manuela A. D.; Dias, Ana Paula S.

    2015-01-01

    There are several ways for constructing (bigger) networks from smaller networks. We consider here the cartesian and the Kronecker (tensor) product networks. Our main aim is to determine a relation between the lattices of synchrony subspaces for a product network and the component networks of the product. In this sense, we show how to obtain the lattice of regular synchrony subspaces for a product network from the lattices of synchrony subspaces for the component networks. Specifically, we prove that a tensor of subspaces is of synchrony for the product network if and only if the subspaces involved in the tensor are synchrony subspaces for the component networks of the product. We also show that, in general, there are (irregular) synchrony subspaces for the product network that are not described by the synchrony subspaces for the component networks, concluding that, in general, it is not possible to obtain the all synchrony lattice for the product network from the corresponding lattices for the component networks. We also make the following remark concerning the fact that the cartesian and Kronecker products, as graph operations, are quite different, implying that the associated coupled cell systems have distinct structures. Although, the kinds of dynamics expected to occur are difficult to compare, we establish an inclusion relation between the lattices of synchrony subspaces for the cartesian and Kronecker products.

  1. Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

    PubMed Central

    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. PMID:25013377

  2. Observation and inverse problems in coupled cell networks

    NASA Astrophysics Data System (ADS)

    Joly, Romain

    2012-03-01

    A coupled cell network is a model for many situations such as food webs in ecosystems, cellular metabolism and economic networks. It consists in a directed graph G, each node (or cell) representing an agent of the network and each directed arrow representing which agent acts on which. It yields a system of differential equations \\dot x(t)=f(x(t)) , where the component i of f depends only on the cells xj(t) for which the arrow j → i exists in G. In this paper, we investigate the observation problems in coupled cell networks: can one deduce the behaviour of the whole network (oscillations, stabilization, etc) by observing only one of the cells? We show that the natural observation properties hold for almost all the interactions f.

  3. 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

  4. Default and Executive Network Coupling Supports Creative Idea Production.

    PubMed

    Beaty, Roger E; Benedek, Mathias; Kaufman, Scott Barry; 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

  5. Amplitude-phase coupling drives chimera states in globally coupled laser networks

    NASA Astrophysics Data System (ADS)

    Böhm, Fabian; Zakharova, Anna; Schöll, Eckehard; Lüdge, Kathy

    2015-04-01

    For a globally coupled network of semiconductor lasers with delayed optical feedback, we demonstrate the existence of chimera states. The domains of coherence and incoherence that are typical for chimera states are found to exist for the amplitude, phase, and inversion of the coupled lasers. These chimera states defy several of the previously established existence criteria. While chimera states in phase oscillators generally demand nonlocal coupling, large system sizes, and specially prepared initial conditions, we find chimera states that are stable for global coupling in a network of only four coupled lasers for random initial conditions. The existence is linked to a regime of multistability between the synchronous steady state and asynchronous periodic solutions. We show that amplitude-phase coupling, a concept common in different fields, is necessary for the formation of the chimera states.

  6. Delay-induced cluster patterns in coupled Cayley tree networks

    NASA Astrophysics Data System (ADS)

    Singh, A.; Jalan, S.

    2013-07-01

    We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.

  7. Synchronization in complex dynamical networks coupled with complex chaotic system

    NASA Astrophysics Data System (ADS)

    Wei, Qiang; Xie, Cheng-Jun; Wang, Bo

    2015-11-01

    This paper investigates synchronization in complex dynamical networks with time delay and perturbation. The node of complex dynamical networks is composed of complex chaotic system. A complex feedback controller is designed to realize different component of complex state variable synchronize up to different scaling complex function when complex dynamical networks realize synchronization. The synchronization scaling function is changed from real field to complex field. Synchronization in complex dynamical networks with constant delay and time-varying coupling delay are investigated, respectively. Numerical simulations show the effectiveness of the proposed method.

  8. Neural network error correction for solving coupled ordinary differential equations

    NASA Technical Reports Server (NTRS)

    Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

    1992-01-01

    A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

  9. Effects of extracellular potassium diffusion on electrically coupled neuron networks

    NASA Astrophysics Data System (ADS)

    Wu, Xing-Xing; Shuai, Jianwei

    2015-02-01

    Potassium accumulation and diffusion during neuronal epileptiform activity have been observed experimentally, and potassium lateral diffusion has been suggested to play an important role in nonsynaptic neuron networks. We adopt a hippocampal CA1 pyramidal neuron network in a zero-calcium condition to better understand the influence of extracellular potassium dynamics on the stimulus-induced activity. The potassium concentration in the interstitial space for each neuron is regulated by potassium currents, Na+-K+ pumps, glial buffering, and ion diffusion. In addition to potassium diffusion, nearby neurons are also coupled through gap junctions. Our results reveal that the latency of the first spike responding to stimulus monotonically decreases with increasing gap-junction conductance but is insensitive to potassium diffusive coupling. The duration of network oscillations shows a bell-like shape with increasing potassium diffusive coupling at weak gap-junction coupling. For modest electrical coupling, there is an optimal K+ diffusion strength, at which the flow of potassium ions among the network neurons appropriately modulates interstitial potassium concentrations in a degree that provides the most favorable environment for the generation and continuance of the action potential waves in the network.

  10. Network-based prediction of protein function

    PubMed Central

    Sharan, Roded; Ulitsky, Igor; Shamir, Ron

    2007-01-01

    Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community. PMID:17353930

  11. Synchronization-based computation through networks of coupled oscillators

    PubMed Central

    Malagarriga, Daniel; García-Vellisca, Mariano A.; Villa, Alessandro E. P.; Buldú, Javier M.; García-Ojalvo, Jordi; Pons, Antonio J.

    2015-01-01

    The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibits remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean-like computations. Our results reveal that the same neural mass network may process different combinations of dynamical inputs as different logical operations or combinations of them. This dynamical feature of the network allows it to process complex inputs in a very sophisticated manner. The results are reproduced experimentally with electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the coupled oscillators. We also show that the information-processing capabilities of coupled oscillations go beyond the simple juxtaposition of logic gates. PMID:26300765

  12. Synchronization-based computation through networks of coupled oscillators.

    PubMed

    Malagarriga, Daniel; García-Vellisca, Mariano A; Villa, Alessandro E P; Buldú, Javier M; García-Ojalvo, Jordi; Pons, Antonio J

    2015-01-01

    The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibits remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean-like computations. Our results reveal that the same neural mass network may process different combinations of dynamical inputs as different logical operations or combinations of them. This dynamical feature of the network allows it to process complex inputs in a very sophisticated manner. The results are reproduced experimentally with electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the coupled oscillators. We also show that the information-processing capabilities of coupled oscillations go beyond the simple juxtaposition of logic gates. PMID:26300765

  13. Synchronization in output-coupled temporal Boolean networks

    NASA Astrophysics Data System (ADS)

    Lu, Jianquan; Zhong, Jie; Tang, Yang; Huang, Tingwen; Cao, Jinde; Kurths, Jürgen

    2014-09-01

    This paper presents an analytical study of synchronization in an array of output-coupled temporal Boolean networks. A temporal Boolean network (TBN) is a logical dynamic system developed to model Boolean networks with regulatory delays. Both state delay and output delay are considered, and these two delays are assumed to be different. By referring to the algebraic representations of logical dynamics and using the semi-tensor product of matrices, the output-coupled TBNs are firstly converted into a discrete-time algebraic evolution system, and then the relationship between the states of coupled TBNs and the initial state sequence is obtained. Then, some necessary and sufficient conditions are derived for the synchronization of an array of TBNs with an arbitrary given initial state sequence. Two numerical examples including one epigenetic model are finally given to illustrate the obtained results.

  14. Synchronization in an array of coupled Boolean networks

    NASA Astrophysics Data System (ADS)

    Li, Rui; Chu, Tianguang

    2012-10-01

    This Letter presents an analytical study of synchronization in an array of coupled deterministic Boolean networks. A necessary and sufficient criterion for synchronization is established based on algebraic representations of logical dynamics in terms of the semi-tensor product of matrices. Some basic properties of a synchronized array of Boolean networks are then derived for the existence of transient states and the upper bound of the number of fixed points. Particularly, an interesting consequence indicates that a “large” mismatch between two coupled Boolean networks in the array may result in loss of synchrony in the entire system. Examples, including the Boolean model of coupled oscillations in the cell cycle, are given to illustrate the present results.

  15. Vapor Sensors Using Olfactory Proteins Coupled to Carbon Nanotubes

    NASA Astrophysics Data System (ADS)

    Lerner, Mitchell; Goldsmith, Brett; Mitala, Joe; Discher, Bohdana; Johnson, A. T. Charlie

    2010-03-01

    We have constructed bio-nano devices which combine mammalian olfactory proteins with carbon nanotubes to create a new class of vapor sensors. Olfactory proteins are a specific class of G-protein coupled receptors, and require a cell membrane or similar environment for proper function. Functionalization procedures have been developed to meet the challenges of routinely coupling such membrane proteins to nanotubes, while preserving the function of the protein. We have successfully isolated olfactory proteins and attached them to carbon nanotube transistors, which provide fast, all-electronic readout of analyte binding by the olfactory receptor. Several different olfactory proteins have been tested, each showing a different sensing response. This work opens the way for future coupling of biology to nanoelectronics and improved biomimetic chemical sensing. This work is supported by the DARPA RealNose Project and the Nano/Bio Interface Center

  16. Multifractal characterization of protein contact networks

    NASA Astrophysics Data System (ADS)

    Maiorino, Enrico; Livi, Lorenzo; Giuliani, Alessandro; Sadeghian, Alireza; Rizzi, Antonello

    2015-06-01

    The multifractal detrended fluctuation analysis of time series is able to reveal the presence of long-range correlations and, at the same time, to characterize the self-similarity of the series. The rich information derivable from the characteristic exponents and the multifractal spectrum can be further analyzed to discover important insights into the underlying dynamical process. In this paper, we employ multifractal analysis techniques in the study of protein contact networks. To this end, initially a network is mapped to three different time series, each of which is generated by a stationary unbiased random walk. To capture the peculiarities of the networks at different levels, we accordingly consider three observables at each vertex: the degree, the clustering coefficient, and the closeness centrality. To compare the results with suitable references, we consider also instances of three well-known network models and two typical time series with pure monofractal and multifractal properties. The first result of notable interest is that time series associated to protein contact networks exhibit long-range correlations (strong persistence), which are consistent with signals in-between the typical monofractal and multifractal behavior. Successively, a suitable embedding of the multifractal spectra allows to focus on ensemble properties, which in turn gives us the possibility to make further observations regarding the considered networks. In particular, we highlight the different role that small and large fluctuations of the considered observables play in the characterization of the network topology.

  17. Stable and transient multicluster oscillation death in nonlocally coupled networks

    NASA Astrophysics Data System (ADS)

    Schneider, Isabelle; Kapeller, Marie; Loos, Sarah; Zakharova, Anna; Fiedler, Bernold; Schöll, Eckehard

    2015-11-01

    In a network of nonlocally coupled Stuart-Landau oscillators with symmetry-breaking coupling, we study numerically, and explain analytically, a family of inhomogeneous steady states (oscillation death). They exhibit multicluster patterns, depending on the cluster distribution prescribed by the initial conditions. Besides stable oscillation death, we also find a regime of long transients asymptotically approaching synchronized oscillations. To explain these phenomena analytically in dependence on the coupling range and the coupling strength, we first use a mean-field approximation, which works well for large coupling ranges but fails for coupling ranges, which are small compared to the cluster size. Going beyond standard mean-field theory, we predict the boundaries of the different stability regimes as well as the transient times analytically in excellent agreement with numerical results.

  18. Ocean-atmosphere coupling from a climate network perspective

    NASA Astrophysics Data System (ADS)

    Wiedermann, Marc; Donges, Jonathan F.; Donner, Reik V.; Kurths, Jürgen

    2014-05-01

    In recent years extensive studies on the climate system have been carried out making use of advanced complex network statistics. However, most previous studies have been characterized by two conceptual restrictions: First, in most cases network measures have been computed without taking into account the topology of the discrete grid, regular or irregular, that climate data is typically stored on. To overcome this problem, the concept of node splitting invariant network measures has been introduced considering individual node weights, for example according to the surface area a node represents, when computing network measures [1]. Second, the great majority of recent studies have been focussing on single climatological fields located on surfaces parallel to or directly on the Earth's surface. A recent work introduced a methodology for quantifying interaction structures between geopotential height fields at different isobaric surfaces by proposing general definitions for network measures dealing with a network of networks composed from distinct subnetworks [2]. In this work, we combine both, the node-weighting scheme as well as the interacting network measure approach. For this purpose, we invent new node-weighted cross-network measures that provide a general tool for quantifying interaction structures in multilayer networks that is applicable to many fields beyond the study of the climate system, such as communication, social or trade networks. Our new approach is utilized for studying ocean-atmosphere coupling in the northern hemisphere. Specifically, we construct 18 coupled climate networks based on monthly data from the ERA 40 reanalysis, each consisting of two subnetworks. In all cases, one subnetwork represents sea-surface temperature (SST) anomalies while the other is based on the geopotential height (HGT) of isobaric surfaces at different pressure levels. By investigating the connectivity of the resulting interdependent network structures, we identify a

  19. Multifunctional proteins revealed by overlapping clustering in protein interaction network

    PubMed Central

    Chapple, Charles E.; Guénoche, Alain; Brun, Christine

    2012-01-01

    Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters. Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein–protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks. Availability: This software can be downloaded from http://tagc.univ-mrs.fr/welcome/spip.php?rubrique197 Contact: brun@tagc.univ-mrs.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22080466

  20. Coherence Resonance of Small World Networks with Adaptive Coupling

    NASA Astrophysics Data System (ADS)

    Miyakawa, Kenji

    2015-06-01

    The phenomenon of coherence resonance (CR) in small world networks with adaptive coupling is investigated by modeling a real experimental situation with a photosensitive Belousov-Zhabotinsky reaction. We show that both spatial synchronization and temporal coherence of noise-induced firings can be considerably improved by adjusting control parameters, such as the degree of connectivity and the coupling strength. A small fraction of possible long-range connections is enough to obtain a great enhancement in CR.

  1. Protein modules and signalling networks

    NASA Astrophysics Data System (ADS)

    Pawson, Tony

    1995-02-01

    Communication between cells assumes particular importance in multicellular organisms. The growth, migration and differentiation of cells in the embryo, and their organization into specific tissues, depend on signals transmitted from one cell to another. In the adult, cell signalling orchestrates normal cellular behaviour and responses to wounding and infection. The consequences of breakdowns in this signalling underlie cancer, diabetes and disorders of the immune and cardiovascular systems. Conserved protein domains that act as key regulatory participants in many of these different signalling pathways are highlighted.

  2. Controlling Topological Entanglement in Engineered Protein Hydrogels with a Variety of Thiol Coupling Chemistries

    NASA Astrophysics Data System (ADS)

    Tang, Shengchang; Olsen, Bradley

    2014-05-01

    Topological entanglements between polymer chains are achieved in associating protein hydrogels through the synthesis of high molecular weight proteins via chain extension using a variety of thiol coupling chemistries, including disulfide formation, thiol-maleimide, thiol-bromomaleimide and thiol-ene. Coupling of cysteines via disulfide formation results in the most pronounced entanglement effect in hydrogels, while other chemistries provide versatile means of changing the extent of entanglement, achieving faster chain extension, and providing a facile method of controlling the network hierarchy and incorporating stimuli responsivities. The addition of trifunctional coupling agents causes incomplete crosslinking and introduces branching architecture to the protein molecules. The high-frequency plateau modulus and the entanglement plateau modulus can be tuned by changing the ratio of difunctional chain extender to the trifunctional branching unit. Therefore, these chain extension reactions show promise in delicately controlling the relaxation and mechanical properties of engineered protein hydrogels in ways that complement their design through genetic engineering.

  3. Controlling topological entanglement in engineered protein hydrogels with a variety of thiol coupling chemistries

    PubMed Central

    Tang, Shengchang; Olsen, Bradley D.

    2014-01-01

    Topological entanglements between polymer chains are achieved in associating protein hydrogels through the synthesis of high molecular weight proteins via chain extension using a variety of thiol coupling chemistries, including disulfide formation, thiol-maleimide, thiol-bromomaleimide and thiol-ene. Coupling of cysteines via disulfide formation results in the most pronounced entanglement effect in hydrogels, while other chemistries provide versatile means of changing the extent of entanglement, achieving faster chain extension, and providing a facile method of controlling the network hierarchy and incorporating stimuli responsivities. The addition of trifunctional coupling agents causes incomplete crosslinking and introduces branching architecture to the protein molecules. The high-frequency plateau modulus and the entanglement plateau modulus can be tuned by changing the ratio of difunctional chain extender to the trifunctional branching unit. Therefore, these chain extension reactions show promise in delicately controlling the relaxation and mechanical properties of engineered protein hydrogels in ways that complement their design through genetic engineering. PMID:24860800

  4. A dynamically coupled allosteric network underlies binding cooperativity in Src kinase

    NASA Astrophysics Data System (ADS)

    Foda, Zachariah H.; Shan, Yibing; Kim, Eric T.; Shaw, David E.; Seeliger, Markus A.

    2015-01-01

    Protein tyrosine kinases are attractive drug targets because many human diseases are associated with the deregulation of kinase activity. However, how the catalytic kinase domain integrates different signals and switches from an active to an inactive conformation remains incompletely understood. Here we identify an allosteric network of dynamically coupled amino acids in Src kinase that connects regulatory sites to the ATP- and substrate-binding sites. Surprisingly, reactants (ATP and peptide substrates) bind with negative cooperativity to Src kinase while products (ADP and phosphopeptide) bind with positive cooperativity. We confirm the molecular details of the signal relay through the allosteric network by biochemical studies. Experiments on two additional protein tyrosine kinases indicate that the allosteric network may be largely conserved among these enzymes. Our work provides new insights into the regulation of protein tyrosine kinases and establishes a potential conduit by which resistance mutations to ATP-competitive kinase inhibitors can affect their activity.

  5. A dynamically coupled allosteric network underlies binding cooperativity in Src kinase

    PubMed Central

    Foda, Zachariah H.; Shan, Yibing; Kim, Eric T.; Shaw, David E.; Seeliger, Markus A.

    2015-01-01

    Protein tyrosine kinases are attractive drug targets because many human diseases are associated with the deregulation of kinase activity. However, how the catalytic kinase domain integrates different signals and switches from an active to an inactive conformation remains incompletely understood. Here we identify an allosteric network of dynamically coupled amino acids in Src kinase that connects regulatory sites to the ATP- and substrate-binding sites. Surprisingly, reactants (ATP and peptide substrates) bind with negative cooperativity to Src kinase while products (ADP and phosphopeptide) bind with positive cooperativity. We confirm the molecular details of the signal relay through the allosteric network by biochemical studies. Experiments on two additional protein tyrosine kinases indicate that the allosteric network may be largely conserved among these enzymes. Our work provides new insights into the regulation of protein tyrosine kinases and establishes a potential conduit by which resistance mutations to ATP-competitive kinase inhibitors can affect their activity. PMID:25600932

  6. A dynamically coupled allosteric network underlies binding cooperativity in Src kinase.

    PubMed

    Foda, Zachariah H; Shan, Yibing; Kim, Eric T; Shaw, David E; Seeliger, Markus A

    2015-01-01

    Protein tyrosine kinases are attractive drug targets because many human diseases are associated with the deregulation of kinase activity. However, how the catalytic kinase domain integrates different signals and switches from an active to an inactive conformation remains incompletely understood. Here we identify an allosteric network of dynamically coupled amino acids in Src kinase that connects regulatory sites to the ATP- and substrate-binding sites. Surprisingly, reactants (ATP and peptide substrates) bind with negative cooperativity to Src kinase while products (ADP and phosphopeptide) bind with positive cooperativity. We confirm the molecular details of the signal relay through the allosteric network by biochemical studies. Experiments on two additional protein tyrosine kinases indicate that the allosteric network may be largely conserved among these enzymes. Our work provides new insights into the regulation of protein tyrosine kinases and establishes a potential conduit by which resistance mutations to ATP-competitive kinase inhibitors can affect their activity. PMID:25600932

  7. From Topology to Phenotype in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Pržulj, Nataša

    We have recently witnessed an explosion in biological network data along with the development of computational approaches for their analyses. This new interdisciplinary research area is an integral part of systems biology, promising to provide new insights into organizational principles of life, as well as into evolution and disease. However, there is a danger that the area might become hindered by several emerging issues. In particular, there is typically a weak link between biological and computational scientists, resulting in the use of simple computational techniques of limited potential to explain these complex biological data. Hence, there is a danger that the community might view the topological features of network data as mere statistics, ignoring the value of the information contained in these data. This might result in the imposition of scientific doctrines, such as scale-free-centric (on the modelling side) and genome-centric (on the biological side) opinions onto this nascent research area. In this chapter, we take a network science perspective and present a brief, high-level overview of the area, commenting on possible challenges ahead. We focus on protein-protein interaction networks (PINs) in which nodes correspond to proteins in a cell and edges to physical bindings between the proteins.

  8. Receptor activity-modifying proteins; multifunctional G protein-coupled receptor accessory proteins.

    PubMed

    Hay, Debbie L; Walker, Christopher S; Gingell, Joseph J; Ladds, Graham; Reynolds, Christopher A; Poyner, David R

    2016-04-15

    Receptor activity-modifying proteins (RAMPs) are single pass membrane proteins initially identified by their ability to determine the pharmacology of the calcitonin receptor-like receptor (CLR), a family B G protein-coupled receptor (GPCR). It is now known that RAMPs can interact with a much wider range of GPCRs. This review considers recent developments on the structure of the complexes formed between the extracellular domains (ECDs) of CLR and RAMP1 or RAMP2 as these provide insights as to how the RAMPs direct ligand binding. The range of RAMP interactions is also considered; RAMPs can interact with numerous family B GPCRs as well as examples of family A and family C GPCRs. They influence receptor expression at the cell surface, trafficking, ligand binding and G protein coupling. The GPCR-RAMP interface offers opportunities for drug targeting, illustrated by examples of drugs developed for migraine. PMID:27068971

  9. Network measures for protein folding state discrimination

    PubMed Central

    Menichetti, Giulia; Fariselli, Piero; Remondini, Daniel

    2016-01-01

    Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis. PMID:27464796

  10. Molecular Dynamics Study of Naturally Existing Cavity Couplings in Proteins

    PubMed Central

    Barbany, Montserrat; Meyer, Tim; Hospital, Adam; Faustino, Ignacio; D'Abramo, Marco; Morata, Jordi; Orozco, Modesto; de la Cruz, Xavier

    2015-01-01

    Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results. PMID:25816327

  11. Protein-protein interaction network analysis of cirrhosis liver disease

    PubMed Central

    Safaei, Akram; Rezaei Tavirani, Mostafa; Arefi Oskouei, Afsaneh; Zamanian Azodi, Mona; Mohebbi, Seyed Reza; Nikzamir, Abdol Rahim

    2016-01-01

    Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease. PMID:27099671

  12. Network-Based Protein Biomarker Discovery Platforms

    PubMed Central

    Kim, Minhyung

    2016-01-01

    The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms. PMID:27103885

  13. Characterization of Seed Storage Proteins from Chickpea Using 2D Electrophoresis Coupled with Mass Spectrometry.

    PubMed

    Singh, Pramod Kumar; Shrivastava, Nidhi; Chaturvedi, Krishna; Sharma, Bechan; Bhagyawant, Sameer S

    2016-01-01

    Proteomic analysis was employed to map the seed storage protein network in landrace and cultivated chickpea accessions. Protein extracts were separated by two-dimensional gel electrophoresis (2D-GE) across a broad range 3.0-10.0 immobilized pH gradient (IPG) strips. Comparative elucidation of differentially expressed proteins between two diverse geographically originated chickpea accessions was carried out using 2D-GE coupled with mass spectrometry. A total of 600 protein spots were detected in these accessions. In-gel protein expression patterns revealed three protein spots as upregulated and three other as downregulated. Using trypsin in-gel digestion, these differentially expressed proteins were identified by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) which showed 45% amino acid homology of chickpea seed storage proteins with Arabidopsis thaliana. PMID:27144024

  14. Characterization of Seed Storage Proteins from Chickpea Using 2D Electrophoresis Coupled with Mass Spectrometry

    PubMed Central

    Singh, Pramod Kumar; Shrivastava, Nidhi; Chaturvedi, Krishna

    2016-01-01

    Proteomic analysis was employed to map the seed storage protein network in landrace and cultivated chickpea accessions. Protein extracts were separated by two-dimensional gel electrophoresis (2D-GE) across a broad range 3.0–10.0 immobilized pH gradient (IPG) strips. Comparative elucidation of differentially expressed proteins between two diverse geographically originated chickpea accessions was carried out using 2D-GE coupled with mass spectrometry. A total of 600 protein spots were detected in these accessions. In-gel protein expression patterns revealed three protein spots as upregulated and three other as downregulated. Using trypsin in-gel digestion, these differentially expressed proteins were identified by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) which showed 45% amino acid homology of chickpea seed storage proteins with Arabidopsis thaliana. PMID:27144024

  15. Cascades on a stochastic pulse-coupled network.

    PubMed

    Wray, C M; Bishop, S R

    2014-01-01

    While much recent research has focused on understanding isolated cascades of networks, less attention has been given to dynamical processes on networks exhibiting repeated cascades of opposing influence. An example of this is the dynamic behaviour of financial markets where cascades of buying and selling can occur, even over short timescales. To model these phenomena, a stochastic pulse-coupled oscillator network with upper and lower thresholds is described and analysed. Numerical confirmation of asynchronous and synchronous regimes of the system is presented, along with analytical identification of the fixed point state vector of the asynchronous mean field system. A lower bound for the finite system mean field critical value of network coupling probability is found that separates the asynchronous and synchronous regimes. For the low-dimensional mean field system, a closed-form equation is found for cascade size, in terms of the network coupling probability. Finally, a description of how this model can be applied to interacting agents in a financial market is provided. PMID:25213626

  16. A generative model for protein contact networks.

    PubMed

    Livi, Lorenzo; Maiorino, Enrico; Giuliani, Alessandro; Rizzi, Antonello; Sadeghian, Alireza

    2016-07-01

    In this paper, we present a generative model for protein contact networks (PCNs). The soundness of the proposed model is investigated by focusing primarily on mesoscopic properties elaborated from the spectra of the graph Laplacian. To complement the analysis, we also study the classical topological descriptors, such as statistics of the shortest paths and the important feature of modularity. Our experiments show that the proposed model results in a considerable improvement with respect to two suitably chosen generative mechanisms, mimicking with better approximation real PCNs in terms of diffusion properties elaborated from the normalized Laplacian spectra. However, as well as the other network models, it does not reproduce with sufficient accuracy the shortest paths structure. To compensate this drawback, we designed a second step involving a targeted edge reconfiguration process. The ensemble of reconfigured networks denotes further improvements that are statistically significant. As an important byproduct of our study, we demonstrate that modularity, a well-known property of proteins, does not entirely explain the actual network architecture characterizing PCNs. In fact, we conclude that modularity, intended as a quantification of an underlying community structure, should be considered as an emergent property of the structural organization of proteins. Interestingly, such a property is suitably optimized in PCNs together with the feature of path efficiency. PMID:26474097

  17. Origin of chaotic transients in excitatory pulse-coupled networks.

    PubMed

    Zou, Hai-Lin; Li, Menghui; Lai, Choy-Heng; Lai, Ying-Cheng

    2012-12-01

    We develop an approach to understanding long chaotic transients in networks of excitatory pulse-coupled oscillators. Our idea is to identify a class of attractors, sequentially active firing (SAF) attractors, in terms of the temporal event structure of firing and receipt of pulses. Then all attractors can be classified into two groups: SAF attractors and non-SAF attractors. We establish that long transients typically arise in the transitional region of the parameter space where the SAF attractors are collectively destabilized. Bifurcation behavior of the SAF attractors is analyzed to provide a detailed understanding of the long irregular transients. Although demonstrated using pulse-coupled oscillator networks, our general methodology may be useful in understanding the origin of transient chaos in other types of networked systems, an extremely challenging problem in nonlinear dynamics and complex systems. PMID:23368031

  18. Pclust: protein network visualization highlighting experimental data

    PubMed Central

    Li, Wenlin; Kinch, Lisa N.; Grishin, Nick V.

    2013-01-01

    Summary: One approach to infer functions of new proteins from their homologs utilizes visualization of an all-against-all pairwise similarity network (A2ApsN) that exploits the speed of BLAST and avoids the complexity of multiple sequence alignment. However, identifying functions of the protein clusters in A2ApsN is never trivial, due to a lack of linking characterized proteins to their relevant information in current software packages. Given the database errors introduced by automatic annotation transfer, functional deduction should be made from proteins with experimental studies, i.e. ‘reference proteins’. Here, we present a web server, termed Pclust, which provides a user-friendly interface to visualize the A2ApsN, placing emphasis on such ‘reference proteins’ and providing access to their full information in source databases, e.g. articles in PubMed. The identification of ‘reference proteins’ and the ease of cross-database linkage will facilitate understanding the functions of protein clusters in the network, thus promoting interpretation of proteins of interest. Availability: The Pclust server is freely available at http://prodata.swmed.edu/pclust Contact: grishin@chop.swmed.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:23918248

  19. Network-scale effect on synchronizability of fully coupled network with connection delay.

    PubMed

    Zheng, Y G; Wang, Z H

    2016-04-01

    Network-scale effect on synchronizability of fully coupled network with connection delay is investigated in this paper. The master stability function, which governs the stability of synchronization manifold, is first obtained by separating the synchronization manifold direction from other transverse directions. Then, by introducing a new time variable in the master stability function, it is shown the effect of connection delay can be weakened with the increase of network scale, and thus, in contrast to the situation without connection delay, large network scale is more positive to the synchronizability of fully coupled network with connection delay. Those findings are confirmed by the studies on two specific networks with nodes of typical nonlinear dynamical systems. PMID:27131482

  20. Coupled climate network analysis of multidecadal dynamics in the Arctic

    NASA Astrophysics Data System (ADS)

    Wiedermann, M.; Donges, J. F.; Heitzig, J.; Kurths, J.

    2012-04-01

    Climate network analysis provides a powerful tool for investigating the correlation structure of the dynamical system Earth. Elements of time series analysis and the theory of complex networks are combined to give new insights into the dynamics of the climate system by delivering a spatially resolved image of the underlying correlation structure from which the network is constructed. Recent results have indicated a possible correlation between the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) with a time lag of 15 to 30 years. However, identifying the involved physical mechanisms remains an open problem of ocean science and atmospheric research. We perform a climate network analysis aiming at assessing the importance of the Arctic for this connection between North Atlantic and North Pacific. As storm tracks were suggested to play a role and the large delay between AMO and PDO points to oceanic processes at work, we focus on analyzing the coupling structure between oceanic sea surface temperature (SST) and atmospheric sea level pressure (SAP) as well as geopotential height (GPH) fields. We employ the recently developed theory of interacting networks, with the corresponding statistical cross-network measures, that enables us to study the properties of a coupled climate network that divides into several subnetworks representing horizontal fields of different observables. As the analysis is performed in a region close to the north pole one has to bear in mind that climatological datasets are often arranged on a rectangular grid such that the density of nodes increases rapidly towards the poles. To correct for the distortions in our results resulting from this inhomogenous node density, we refine the cross-network measures in a way that enables us to assign every node with an individual weight according to the area that the node represents on the Earth's surface. This method has already been applied to the standard set of measures

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

    PubMed

    Yen, Eric A; Tsay, Aaron; Waldispuhl, Jerome; Vogel, Jackie

    2014-05-01

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

  2. Data Mechanics and Coupling Geometry on Binary Bipartite Networks

    PubMed Central

    Fushing, Hsieh; Chen, Chen

    2014-01-01

    We quantify the notion of pattern and formalize the process of pattern discovery under the framework of binary bipartite networks. Patterns of particular focus are interrelated global interactions between clusters on its row and column axes. A binary bipartite network is built into a thermodynamic system embracing all up-and-down spin configurations defined by product-permutations on rows and columns. This system is equipped with its ferromagnetic energy ground state under Ising model potential. Such a ground state, also called a macrostate, is postulated to congregate all patterns of interest embedded within the network data in a multiscale fashion. A new computing paradigm for indirect searching for such a macrostate, called Data Mechanics, is devised by iteratively building a surrogate geometric system with a pair of nearly optimal marginal ultrametrics on row and column spaces. The coupling measure minimizing the Gromov-Wasserstein distance of these two marginal geometries is also seen to be in the vicinity of the macrostate. This resultant coupling geometry reveals multiscale block pattern information that characterizes multiple layers of interacting relationships between clusters on row and on column axes. It is the nonparametric information content of a binary bipartite network. This coupling geometry is then demonstrated to shed new light and bring resolution to interaction issues in community ecology and in gene-content-based phylogenetics. Its implied global inferences are expected to have high potential in many scientific areas. PMID:25170903

  3. Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling

    PubMed Central

    Tsuji, Toshiyuki; Yoda, Takao; Shirai, Tsuyoshi

    2015-01-01

    Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures. PMID:26549015

  4. Identification of essential proteins based on ranking edge-weights in protein-protein interaction networks.

    PubMed

    Wang, Yan; Sun, Huiyan; Du, Wei; Blanzieri, Enrico; Viero, Gabriella; Xu, Ying; Liang, Yanchun

    2014-01-01

    Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges. PMID:25268881

  5. Rescue of endemic states in interconnected networks with adaptive coupling.

    PubMed

    Vazquez, F; Serrano, M Ángeles; Miguel, M San

    2016-01-01

    We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771

  6. Rescue of endemic states in interconnected networks with adaptive coupling

    NASA Astrophysics Data System (ADS)

    Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San

    2016-07-01

    We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.

  7. Rescue of endemic states in interconnected networks with adaptive coupling

    PubMed Central

    Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San

    2016-01-01

    We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771

  8. Graphical models of residue coupling in protein families.

    PubMed

    Thomas, John; Ramakrishnan, Naren; Bailey-Kellogg, Chris

    2008-01-01

    Many statistical measures and algorithmic techniques have been proposed for studying residue coupling in protein families. Generally speaking, two residue positions are considered coupled if, in the sequence record, some of their amino acid type combinations are significantly more common than others. While the proposed approaches have proven useful in finding and describing coupling, a significant missing component is a formal probabilistic model that explicates and compactly represents the coupling, integrates information about sequence,structure, and function, and supports inferential procedures for analysis, diagnosis, and prediction.We present an approach to learning and using probabilistic graphical models of residue coupling. These models capture significant conservation and coupling constraints observable ina multiply-aligned set of sequences. Our approach can place a structural prior on considered couplings, so that all identified relationships have direct mechanistic explanations. It can also incorporate information about functional classes, and thereby learn a differential graphical model that distinguishes constraints common to all classes from those unique to individual classes. Such differential models separately account for class-specific conservation and family-wide coupling, two different sources of sequence covariation. They are then able to perform interpretable functional classification of new sequences, explaining classification decisions in terms of the underlying conservation and coupling constraints. We apply our approach in studies of both G protein-coupled receptors and PDZ domains, identifying and analyzing family-wide and class-specific constraints, and performing functional classification. The results demonstrate that graphical models of residue coupling provide a powerful tool for uncovering, representing, and utilizing significant sequence structure-function relationships in protein families. PMID:18451428

  9. Dynamic model of neural networks with asymmetric diluted couplings

    NASA Astrophysics Data System (ADS)

    Choi, M. Y.; Choi, Meekyoung

    1990-06-01

    We study an asymmetric diluted version of the dynamic model for neural networks proposed recently, which explicitly takes into account the existence of several time scales without discretizing the time. The dynamics is neither totally synchronous nor totally asynchronous, and the couplings in the neural networks are asymmetric. These considerations may be regarded as more biologically realistic. We obtain the phase diagram as a function of the temperature ɛ-1, the capacity α, and the ratio a of the refractory period to the action potential duration.

  10. Module organization and variance in protein-protein interaction networks

    PubMed Central

    Lin, Chun-Yu; Lee, Tsai-Ling; Chiu, Yi-Yuan; Lin, Yi-Wei; Lo, Yu-Shu; Lin, Chih-Ta; Yang, Jinn-Moon

    2015-01-01

    A module is a group of closely related proteins that act in concert to perform specific biological functions through protein–protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions. PMID:25797237

  11. Localized patterns in homogeneous networks of diffusively coupled reactors

    NASA Astrophysics Data System (ADS)

    Moore, Peter K.; Horsthemke, Werner

    2005-06-01

    We study the influence of network topology on instabilities of the homogeneous steady state of diffusively coupled, monostable nonlinear cells. A particular focus are diffusion-induced instabilities, i.e., Turing instabilities. We present various theorems that make it possible to determine analytically the stability properties of networks with arbitrary topologies and general monostable dynamics of the individual cells. This work aims in particular to determine those topologies that will give rise to localized stationary patterns. Specific examples focus on well-stirred chemical reactors. The reactors are coupled by diffusion-like mass transfer, and the kinetics is given by the Lengyel-Epstein model, a two-variable scheme for the chlorine dioxide-iodine-malonic acid reaction.

  12. Tetramer formation in Arabidopsis MADS domain proteins: analysis of a protein-protein interaction network

    PubMed Central

    2014-01-01

    Background MADS domain proteins are transcription factors that coordinate several important developmental processes in plants. These proteins interact with other MADS domain proteins to form dimers, and it has been proposed that they are able to associate as tetrameric complexes that regulate transcription of target genes. Whether the formation of functional tetramers is a widespread property of plant MADS domain proteins, or it is specific to few of these transcriptional regulators remains unclear. Results We analyzed the structure of the network of physical interactions among MADS domain proteins in Arabidopsis thaliana. We determined the abundance of subgraphs that represent the connection pattern expected for a MADS domain protein heterotetramer. These subgraphs were significantly more abundant in the MADS domain protein interaction network than in randomized analogous networks. Importantly, these subgraphs are not significantly frequent in a protein interaction network of TCP plant transcription factors, when compared to expectation by chance. In addition, we found that MADS domain proteins in tetramer-like subgraphs are more likely to be expressed jointly than proteins in other subgraphs. This effect is mainly due to proteins in the monophyletic MIKC clade, as there is no association between tetramer-like subgraphs and co-expression for proteins outside this clade. Conclusions Our results support that the tendency to form functional tetramers is widespread in the MADS domain protein-protein interaction network. Our observations also suggest that this trend is prevalent, or perhaps exclusive, for proteins in the MIKC clade. Because it is possible to retrodict several experimental results from our analyses, our work can be an important aid to make new predictions and facilitates experimental research on plant MADS domain proteins. PMID:24468197

  13. Evolutionarily Conserved Herpesviral Protein Interaction Networks

    PubMed Central

    Fossum, Even; Friedel, Caroline C.; Rajagopala, Seesandra V.; Titz, Björn; Baiker, Armin; Schmidt, Tina; Kraus, Theo; Stellberger, Thorsten; Rutenberg, Christiane; Suthram, Silpa; Bandyopadhyay, Sourav; Rose, Dietlind; von Brunn, Albrecht; Uhlmann, Mareike; Zeretzke, Christine; Dong, Yu-An; Boulet, Hélène; Koegl, Manfred; Bailer, Susanne M.; Koszinowski, Ulrich; Ideker, Trey; Uetz, Peter; Zimmer, Ralf; Haas, Jürgen

    2009-01-01

    Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species. PMID:19730696

  14. HKC: an algorithm to predict protein complexes in protein-protein interaction networks.

    PubMed

    Wang, Xiaomin; Wang, Zhengzhi; Ye, Jun

    2011-01-01

    With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods. PMID:22174556

  15. Interaction prediction using conserved network motifs in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2005-03-01

    High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. This talk will describe the recent developments in analyzing and understanding protein interaction networks, then present a method that uses the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a ``leave-one-ou approach we find average success rates between 20-50% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org

  16. MHD generator with improved network coupling electrodes to a load

    DOEpatents

    Rosa, Richard J.

    1977-01-01

    An MHD generator has a plurality of segmented electrodes extending longitudinally of a duct, whereby progressively increasing high DC voltages are derived from a set of cathode electrodes and progressively increasing low DC voltages are derived from a set of anode electrodes. First and second load terminals are respectively connected to the cathode and anode electrodes by separate coupling networks, each of which includes a number of SCR's and a number of diode rectifiers.

  17. Pinning synchronization of discrete dynamical networks with delay coupling

    NASA Astrophysics Data System (ADS)

    Cheng, Ranran; Peng, Mingshu; Zuo, Jun

    2016-05-01

    The purpose of this paper is to investigate the pinning synchronization analysis for nonlinear coupled delayed discrete dynamical networks with the identical or nonidentical topological structure. Based on the Lyapunov stability theory, pinning control method and linear matrix inequalities, several adaptive synchronization criteria via two kinds of pinning control method are obtained. Two examples based on Rulkov chaotic system are included to illustrate the effectiveness and verification of theoretical analysis.

  18. Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures

    PubMed Central

    Wang, Xingyuan; Cao, Jianye; Qin, Xiaomeng

    2016-01-01

    Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids. PMID:27494715

  19. Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures.

    PubMed

    Wang, Xingyuan; Cao, Jianye; Qin, Xiaomeng

    2016-01-01

    Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids. PMID:27494715

  20. Modular networks with delayed coupling: Synchronization and frequency control

    NASA Astrophysics Data System (ADS)

    Maslennikov, Oleg V.; Nekorkin, Vladimir I.

    2014-07-01

    We study the collective dynamics of modular networks consisting of map-based neurons which generate irregular spike sequences. Three types of intramodule topology are considered: a random Erdös-Rényi network, a small-world Watts-Strogatz network, and a scale-free Barabási-Albert network. The interaction between the neurons of different modules is organized by relatively sparse connections with time delay. For all the types of the network topology considered, we found that with increasing delay two regimes of module synchronization alternate with each other: inphase and antiphase. At the same time, the average rate of collective oscillations decreases within each of the time-delay intervals corresponding to a particular synchronization regime. A dual role of the time delay is thus established: controlling a synchronization mode and degree and controlling an average network frequency. Furthermore, we investigate the influence on the modular synchronization by other parameters: the strength of intermodule coupling and the individual firing rate.

  1. Pattern Selection in Network of Coupled Multi-Scroll Attractors

    PubMed Central

    Li, Fan

    2016-01-01

    Multi-scroll chaotic attractor makes the oscillator become more complex in dynamic behaviors. The collective behaviors of coupled oscillators with multi-scroll attractors are investigated in the regular network in two-dimensional array, which the local kinetics is described by an improved Chua circuit. A feasible scheme of negative feedback with diversity is imposed on the network to stabilize the spatial patterns. Firstly, the Chua circuit is improved by replacing the nonlinear term with Sine function to generate infinite aquariums so that multi-scroll chaotic attractors could be generated under appropriate parameters, which could be detected by calculating the Lyapunov exponent in the parameter region. Furthermore, negative feedback with different gains (D1, D2) is imposed on the local square center area A2 and outer area A1 of the network, it is found that spiral wave, target wave could be developed in the network under appropriate feedback gain with diversity and size of controlled area. Particularly, homogeneous state could be reached after synchronization by selecting appropriate feedback gain and controlled size in the network. Finally, the distribution for statistical factors of synchronization is calculated in the two-parameter space to understand the transition of pattern region. It is found that developed spiral waves, target waves often are associated with smaller factor of synchronization. These results show that emergence of sustained spiral wave and continuous target wave could be effective for further suppression of spatiotemporal chaos in network by generating stable pacemaker completely. PMID:27119986

  2. Pattern Selection in Network of Coupled Multi-Scroll Attractors.

    PubMed

    Li, Fan; Ma, Jun

    2016-01-01

    Multi-scroll chaotic attractor makes the oscillator become more complex in dynamic behaviors. The collective behaviors of coupled oscillators with multi-scroll attractors are investigated in the regular network in two-dimensional array, which the local kinetics is described by an improved Chua circuit. A feasible scheme of negative feedback with diversity is imposed on the network to stabilize the spatial patterns. Firstly, the Chua circuit is improved by replacing the nonlinear term with Sine function to generate infinite aquariums so that multi-scroll chaotic attractors could be generated under appropriate parameters, which could be detected by calculating the Lyapunov exponent in the parameter region. Furthermore, negative feedback with different gains (D1, D2) is imposed on the local square center area A2 and outer area A1 of the network, it is found that spiral wave, target wave could be developed in the network under appropriate feedback gain with diversity and size of controlled area. Particularly, homogeneous state could be reached after synchronization by selecting appropriate feedback gain and controlled size in the network. Finally, the distribution for statistical factors of synchronization is calculated in the two-parameter space to understand the transition of pattern region. It is found that developed spiral waves, target waves often are associated with smaller factor of synchronization. These results show that emergence of sustained spiral wave and continuous target wave could be effective for further suppression of spatiotemporal chaos in network by generating stable pacemaker completely. PMID:27119986

  3. Chaos in generically coupled phase oscillator networks with nonpairwise interactions

    NASA Astrophysics Data System (ADS)

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana

    2016-09-01

    The Kuramoto-Sakaguchi system of coupled phase oscillators, where interaction between oscillators is determined by a single harmonic of phase differences of pairs of oscillators, has very simple emergent dynamics in the case of identical oscillators that are globally coupled: there is a variational structure that means the only attractors are full synchrony (in-phase) or splay phase (rotating wave/full asynchrony) oscillations and the bifurcation between these states is highly degenerate. Here we show that nonpairwise coupling—including three and four-way interactions of the oscillator phases—that appears generically at the next order in normal-form based calculations can give rise to complex emergent dynamics in symmetric phase oscillator networks. In particular, we show that chaos can appear in the smallest possible dimension of four coupled phase oscillators for a range of parameter values.

  4. Impact of symmetry breaking in networks of globally coupled oscillators

    NASA Astrophysics Data System (ADS)

    Premalatha, K.; Chandrasekar, V. K.; Senthilvelan, M.; Lakshmanan, M.

    2015-05-01

    We analyze the consequences of symmetry breaking in the coupling in a network of globally coupled identical Stuart-Landau oscillators. We observe that symmetry breaking leads to increased disorderliness in the dynamical behavior of oscillatory states and consequently results in a rich variety of dynamical states. Depending on the strength of the nonisochronicity parameter, we find various dynamical states such as amplitude chimera, amplitude cluster, frequency chimera, and frequency cluster states. In addition we also find disparate transition routes to recently observed chimera death states in the presence of symmetry breaking even with global coupling. We also analytically verify the chimera death region, which corroborates the numerical results. These results are compared with that of the symmetry-preserving case as well.

  5. Coupled disease-behavior dynamics on complex networks: A review.

    PubMed

    Wang, Zhen; Andrews, Michael A; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T

    2015-12-01

    It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years. PMID:26211717

  6. Coupled disease-behavior dynamics on complex networks: A review

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.

    2015-12-01

    It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

  7. Emerging dynamics in neuronal networks of diffusively coupled hard oscillators.

    PubMed

    Ponta, L; Lanza, V; Bonnin, M; Corinto, F

    2011-06-01

    Oscillatory networks are a special class of neural networks where each neuron exhibits time periodic behavior. They represent bio-inspired architectures which can be exploited to model biological processes such as the binding problem and selective attention. In this paper we investigate the dynamics of networks whose neurons are hard oscillators, namely they exhibit the coexistence of different stable attractors. We consider a constant external stimulus applied to each neuron, which influences the neuron's own natural frequency. We show that, due to the interaction between different kinds of attractors, as well as between attractors and repellors, new interesting dynamics arises, in the form of synchronous oscillations of various amplitudes. We also show that neurons subject to different stimuli are able to synchronize if their couplings are strong enough. PMID:21411276

  8. Abnormal cross-frequency coupling in the tinnitus network

    PubMed Central

    Adamchic, Ilya; Langguth, Berthold; Hauptmann, Christian; Tass, Peter A.

    2014-01-01

    Neuroimaging studies have identified networks of brain areas and oscillations associated with tinnitus perception. However, how these regions relate to perceptual characteristics of tinnitus, and how oscillations in various frequency bands are associated with communications within the tinnitus network is still incompletely understood. Recent evidence suggests that apart from changes of the tinnitus severity the changes of tinnitus dominant pitch also have modulating effect on the underlying neuronal activity in a number of brain areas within the tinnitus network. Therefore, in a re-analysis of an existing dataset, we sought to determine how the oscillations in the tinnitus network in the various frequency bands interact. We also investigate how changes of tinnitus loudness, annoyance and pitch affect cross-frequency interaction both within and between nodes of the tinnitus network. Results of this study provide, to our knowledge, the first evidence that in tinnitus patients, aside from the previously described changes of oscillatory activity, there are also changes of cross-frequency coupling (CFC); phase-amplitude CFC was increased in tinnitus patients within the auditory cortex and the dorsolateral prefrontal regions between the phase of delta-theta and the amplitude of gamma oscillations (Modulation Index [MI] 0.17 in tinnitus patients vs. 0.08 in tinnitus free controls). Moreover, theta phase in the anterior cingulate region modulated gamma in the auditory (MI 0.1) and dorsolateral prefrontal regions (MI 0.19). Reduction of tinnitus severity after acoustic coordinated reset therapy led to a partial normalization of abnormal CFC. Also treatment induced changes in tinnitus pitch significantly modulated changes in CFC. Thus, tinnitus perception is associated with a more pronounced CFC within and between nodes of the tinnitus network. CFC can coordinate tinnitus-relevant activity in the tinnitus network providing a mechanism for effective communication between

  9. The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction.

    PubMed

    Fang, Yi; Sun, Mengtian; Dai, Guoxian; Ramain, Karthik

    2016-01-01

    Recent developments in high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. However, inherently high false positive and false negative rates in measurement have posed great challenges in computational approaches for the prediction of PPI. A good PPI predictor should be 1) resistant to high rate of missing and spurious PPIs, and 2) robust against incompleteness of observed PPI networks. To predict PPI in a network, we developed an intrinsic geometry structure (IGS) for network, which exploits the intrinsic and hidden relationship among proteins in network through a heat diffusion process. In this process, all explicit PPIs participate simultaneously to glue local infinitesimal and noisy experimental interaction data to generate a global macroscopic descriptions about relationships among proteins. The revealed implicit relationship can be interpreted as the probability of two proteins interacting with each other. The revealed relationship is intrinsic and robust against individual, local and explicit protein interactions in the original network. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods. PMID:26886733

  10. From biological and social network metaphors to coupled bio-social wireless networks.

    PubMed

    Barrett, Christopher L; Channakeshava, Karthik; Eubank, Stephen; Anil Kumar, V S; Marathe, Madhav V

    2011-01-01

    Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

  11. From biological and social network metaphors to coupled bio-social wireless networks

    PubMed Central

    Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

    2010-01-01

    Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

  12. Applications of molecular replacement to G protein-coupled receptors

    SciTech Connect

    Kruse, Andrew C.; Manglik, Aashish; Kobilka, Brian K.; Weis, William I.

    2013-11-01

    The use of molecular replacement in solving the structures of G protein-coupled receptors is discussed, with specific examples being described in detail. G protein-coupled receptors (GPCRs) are a large class of integral membrane proteins involved in regulating virtually every aspect of human physiology. Despite their profound importance in human health and disease, structural information regarding GPCRs has been extremely limited until recently. With the advent of a variety of new biochemical and crystallographic techniques, the structural biology of GPCRs has advanced rapidly, offering key molecular insights into GPCR activation and signal transduction. To date, almost all GPCR structures have been solved using molecular-replacement techniques. Here, the unique aspects of molecular replacement as applied to individual GPCRs and to signaling complexes of these important proteins are discussed.

  13. Rational Coupled Dynamics Network Manipulation Rescues Disease-Relevant Mutant Cystic Fibrosis Transmembrane Conductance Regulator

    PubMed Central

    Proctor, Elizabeth A.; Kota, Pradeep; Aleksandrov, Andrei A.; He, Lihua; Riordan, John R.; Dokholyan, Nikolay V.

    2014-01-01

    Many cellular functions necessary for life are tightly regulated by protein allosteric conformational change, and correlated dynamics between protein regions has been found to contribute to the function of proteins not previously considered allosteric. The ability to map and control such dynamic coupling would thus create opportunities for the extension of current therapeutic design strategy. Here, we present an approach to determine the networks of residues involved in the transfer of correlated motion across a protein, and apply our approach to rescue disease-causative mutant cystic fibrosis transmembrane regulator (CFTR) ion channels, ΔF508 and ΔI507, which together constitute over 90% of cystic fibrosis cases. We show that these mutations perturb dynamic coupling within the first nucleotide-binding domain (NBD1), and uncover a critical residue that mediates trans-domain coupled dynamics. By rationally designing a mutation to this residue, we improve aberrant dynamics of mutant CFTR as well as enhance surface expression and function of both mutants, demonstrating the rescue of a disease mutation by rational correction of aberrant protein dynamics. PMID:25685315

  14. Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions

    PubMed Central

    Burgard, Anthony P.; Nikolaev, Evgeni V.; Schilling, Christophe H.; Maranas, Costas D.

    2004-01-01

    In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v1 and v2, are (1) directionally coupled, if a non-zero flux for v1 implies a non-zero flux for v2 but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v1 implies a non-zero, though variable, flux for v2 and vice versa; or (3) fully coupled, if a non-zero flux for v1 implies not only a non-zero but also a fixed flux for v2 and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations. PMID:14718379

  15. Collective prediction of protein functions from protein-protein interaction networks

    PubMed Central

    2014-01-01

    Background Automated assignment of functions to unknown proteins is one of the most important task in computational biology. The development of experimental methods for genome scale analysis of molecular interaction networks offers new ways to infer protein function from protein-protein interaction (PPI) network data. Existing techniques for collective classification (CC) usually increase accuracy for network data, wherein instances are interlinked with each other, using a large amount of labeled data for training. However, the labeled data are time-consuming and expensive to obtain. On the other hand, one can easily obtain large amount of unlabeled data. Thus, more sophisticated methods are needed to exploit the unlabeled data to increase prediction accuracy for protein function prediction. Results In this paper, we propose an effective Markov chain based CC algorithm (ICAM) to tackle the label deficiency problem in CC for interrelated proteins from PPI networks. Our idea is to model the problem using two distinct Markov chain classifiers to make separate predictions with regard to attribute features from protein data and relational features from relational information. The ICAM learning algorithm combines the results of the two classifiers to compute the ranks of labels to indicate the importance of a set of labels to an instance, and uses an ICA framework to iteratively refine the learning models for improving performance of protein function prediction from PPI networks in the paucity of labeled data. Conclusion Experimental results on the real-world Yeast protein-protein interaction datasets show that our proposed ICAM method is better than the other ICA-type methods given limited labeled training data. This approach can serve as a valuable tool for the study of protein function prediction from PPI networks. PMID:24564855

  16. Epidemic spreading on one-way-coupled networks

    NASA Astrophysics Data System (ADS)

    Wang, Lingna; Sun, Mengfeng; Chen, Shanshan; Fu, Xinchu

    2016-09-01

    Numerous real-world networks (e.g., social, communicational, and biological networks) have been observed to depend on each other, and this results in interconnected networks with different topology structures and dynamics functions. In this paper, we focus on the scenario of epidemic spreading on one-way-coupled networks comprised of two subnetworks, which can manifest the transmission of some zoonotic diseases. By proposing a mathematical model through mean-field approximation approach, we prove the global stability of the disease-free and endemic equilibria of this model. Through the theoretical and numerical analysis, we obtain interesting results: the basic reproduction number R0 of the whole network is the maximum of the basic reproduction numbers of the two subnetworks; R0 is independent of the cross-infection rate and cross contact pattern; R0 increases rapidly with the growth of inner infection rate if the inner contact pattern is scale-free; in order to eradicate zoonotic diseases from human beings, we must simultaneously eradicate them from animals; bird-to-bird infection rate has bigger impact on the human's average infected density than bird-to-human infection rate.

  17. Self-organized network evolution coupled to extremal dynamics

    NASA Astrophysics Data System (ADS)

    Garlaschelli, Diego; Capocci, Andrea; Caldarelli, Guido

    2007-11-01

    The interplay between topology and dynamics in complex networks is a fundamental but widely unexplored problem. Here, we study this phenomenon on a prototype model in which the network is shaped by a dynamical variable. We couple the dynamics of the Bak-Sneppen evolution model with the rules of the so-called fitness network model for establishing the topology of a network; each vertex is assigned a `fitness', and the vertex with minimum fitness and its neighbours are updated in each iteration. At the same time, the links between the updated vertices and all other vertices are drawn anew with a fitness-dependent connection probability. We show analytically and numerically that the system self-organizes to a non-trivial state that differs from what is obtained when the two processes are decoupled. A power-law decay of dynamical and topological quantities above a threshold emerges spontaneously, as well as a feedback between different dynamical regimes and the underlying correlation and percolation properties of the network.

  18. Co-Authorship and Bibliographic Coupling Network Effects on Citations

    PubMed Central

    Biscaro, Claudio; Giupponi, Carlo

    2014-01-01

    This paper analyzes the effects of the co-authorship and bibliographic coupling networks on the citations received by scientific articles. It expands prior research that limited its focus on the position of co-authors and incorporates the effects of the use of knowledge sources within articles: references. By creating a network on the basis of shared references, we propose a way to understand whether an article bridges among extant strands of literature and infer the size of its research community and its embeddedness. Thus, we map onto the article – our unit of analysis – the metrics of authors' position in the co-authorship network and of the use of knowledge on which the scientific article is grounded. Specifically, we adopt centrality measures – degree, betweenneess, and closeness centrality – in the co-authorship network and degree, betweenness centrality and clustering coefficient in the bibliographic coupling and show their influence on the citations received in first two years after the year of publication. Findings show that authors' degree positively impacts citations. Also closeness centrality has a positive effect manifested only when the giant component is relevant. Author's betweenness centrality has instead a negative effect that persists until the giant component - largest component of the network in which all nodes can be linked by a path - is relevant. Moreover, articles that draw on fragmented strands of literature tend to be cited more, whereas the size of the scientific research community and the embeddedness of the article in a cohesive cluster of literature have no effect. PMID:24911416

  19. Stability of the splay state in pulse-coupled networks

    SciTech Connect

    Zillmer, Ruediger; Livi, Roberto; Politi, Antonio; Torcini, Alessandro

    2007-10-15

    The stability of the dynamical states characterized by a uniform firing rate (splay states) is analyzed in a network of globally coupled leaky integrate-and-fire neurons. This is done by reducing the set of differential equations to a map that is investigated in the limit of large network size. We show that the stability of the splay state depends crucially on the ratio between the pulse width and the interspike interval. More precisely, the spectrum of Floquet exponents turns out to consist of three components: (i) one that coincides with the predictions of the mean-field analysis [Abbott and van Vreesvijk, Phys. Rev. E 48, 1483 (1993)], (ii) a component measuring the instability of 'finite-frequency' modes, (iii) a number of 'isolated' eigenvalues that are connected to the characteristics of the single pulse and may give rise to strong instabilities (the Floquet exponent being proportional to the network size). Finally, as a side result, we find that the splay state can be stable even for inhibitory coupling.

  20. The Lattice of Synchrony Subspaces of a Coupled Cell Network: Characterization and Computation Algorithm

    NASA Astrophysics Data System (ADS)

    Aguiar, Manuela A. D.; Dias, Ana Paula S.

    2014-12-01

    Coupled cell systems are networks of dynamical systems (the cells), where the links between the cells are described through the network structure, the coupled cell network. Synchrony subspaces are spaces defined in terms of equalities of certain cell coordinates that are flow-invariant for all coupled cell systems associated with a given network structure. The intersection of synchrony subspaces of a network is also a synchrony subspace of the network. It follows, then, that, given a coupled cell network, its set of synchrony subspaces, taking the inclusion partial order relation, forms a lattice. In this paper we show how to obtain the lattice of synchrony subspaces for a general network and present an algorithm that generates that lattice. We prove that this problem is reduced to obtain the lattice of synchrony subspaces for regular networks. For a regular network we obtain the lattice of synchrony subspaces based on the eigenvalue structure of the network adjacency matrix.

  1. Metal ion coupled protein folding and allosteric motions

    NASA Astrophysics Data System (ADS)

    Wang, Wei

    2014-03-01

    Many proteins need the help of cofactors for their successful folding and functioning. Metal ions, i.e., Zn2+, Ca2+, and Mg2+ etc., are typical biological cofactors. Binding of metal ions can reshape the energy landscapes of proteins, thereby modifying the folding and allosteric motions. For example, such binding may make the intrinsically disordered proteins have funneled energy landscapes, consequently, ensures their spontaneous folding. In addition, the binding may activate certain biological processes by inducing related conformational changes of regulation proteins. However, how the local interactions involving the metal ion binding can induce the global conformational motions of proteins remains elusive. Investigating such question requires multiple models with different details, including quantum mechanics, atomistic models, and coarse grained models. In our recent work, we have been developing such multiscale methods which can reasonably model the metal ion binding induced charge transfer, protonation/deprotonation, and large conformational motions of proteins. With such multiscale model, we elucidated the zinc-binding induced folding mechanism of classical zinc finger and the calcium-binding induced dynamic symmetry breaking in the allosteric motions of calmodulin. In addition, we studied the coupling of folding, calcium binding and allosteric motions of calmodulin domains. In this talk, I will introduce the above progresses on the metal ion coupled protein folding and allosteric motions. We thank the finacial support from NSFC and the 973 project.

  2. Experimental multistable states for small network of coupled pendula.

    PubMed

    Dudkowski, Dawid; Grabski, Juliusz; Wojewoda, Jerzy; Perlikowski, Przemyslaw; Maistrenko, Yuri; Kapitaniak, Tomasz

    2016-01-01

    Chimera states are dynamical patterns emerging in populations of coupled identical oscillators where different groups of oscillators exhibit coexisting synchronous and incoherent behaviors despite homogeneous coupling. Although these states are typically observed in the large ensembles of oscillators, recently it has been shown that so-called weak chimera states may occur in the systems with small numbers of oscillators. Here, we show that similar multistable states demonstrating partial frequency synchronization, can be observed in simple experiments with identical mechanical oscillators, namely pendula. The mathematical model of our experiment shows that the observed multistable states are controlled by elementary dynamical equations, derived from Newton's laws that are ubiquitous in many physical and engineering systems. Our finding suggests that multistable chimera-like states are observable in small networks relevant to various real-world systems. PMID:27445038

  3. Experimental multistable states for small network of coupled pendula

    NASA Astrophysics Data System (ADS)

    Dudkowski, Dawid; Grabski, Juliusz; Wojewoda, Jerzy; Perlikowski, Przemyslaw; Maistrenko, Yuri; Kapitaniak, Tomasz

    2016-07-01

    Chimera states are dynamical patterns emerging in populations of coupled identical oscillators where different groups of oscillators exhibit coexisting synchronous and incoherent behaviors despite homogeneous coupling. Although these states are typically observed in the large ensembles of oscillators, recently it has been shown that so-called weak chimera states may occur in the systems with small numbers of oscillators. Here, we show that similar multistable states demonstrating partial frequency synchronization, can be observed in simple experiments with identical mechanical oscillators, namely pendula. The mathematical model of our experiment shows that the observed multistable states are controlled by elementary dynamical equations, derived from Newton’s laws that are ubiquitous in many physical and engineering systems. Our finding suggests that multistable chimera-like states are observable in small networks relevant to various real-world systems.

  4. Experimental multistable states for small network of coupled pendula

    PubMed Central

    Dudkowski, Dawid; Grabski, Juliusz; Wojewoda, Jerzy; Perlikowski, Przemyslaw; Maistrenko, Yuri; Kapitaniak, Tomasz

    2016-01-01

    Chimera states are dynamical patterns emerging in populations of coupled identical oscillators where different groups of oscillators exhibit coexisting synchronous and incoherent behaviors despite homogeneous coupling. Although these states are typically observed in the large ensembles of oscillators, recently it has been shown that so-called weak chimera states may occur in the systems with small numbers of oscillators. Here, we show that similar multistable states demonstrating partial frequency synchronization, can be observed in simple experiments with identical mechanical oscillators, namely pendula. The mathematical model of our experiment shows that the observed multistable states are controlled by elementary dynamical equations, derived from Newton’s laws that are ubiquitous in many physical and engineering systems. Our finding suggests that multistable chimera-like states are observable in small networks relevant to various real-world systems. PMID:27445038

  5. Pulse-coupled neural network implementation in FPGA

    NASA Astrophysics Data System (ADS)

    Waldemark, Joakim T. A.; Lindblad, Thomas; Lindsey, Clark S.; Waldemark, Karina E.; Oberg, Johnny; Millberg, Mikael

    1998-03-01

    Pulse Coupled Neural Networks (PCNN) are biologically inspired neural networks, mainly based on studies of the visual cortex of small mammals. The PCNN is very well suited as a pre- processor for image processing, particularly in connection with object isolation, edge detection and segmentation. Several implementations of PCNN on von Neumann computers, as well as on special parallel processing hardware devices (e.g. SIMD), exist. However, these implementations are not as flexible as required for many applications. Here we present an implementation in Field Programmable Gate Arrays (FPGA) together with a performance analysis. The FPGA hardware implementation may be considered a platform for further, extended implementations and easily expanded into various applications. The latter may include advanced on-line image analysis with close to real-time performance.

  6. G-protein coupled receptor kinases in inflammation and disease

    PubMed Central

    Packiriswamy, Nandakumar; Parameswaran, Narayanan

    2015-01-01

    G-protein coupled receptor kinases (GRKs) are serine/threonine protein kinases originally discovered for their role in G-protein coupled receptor (GPCR) phosphorylation. Recent studies have demonstrated a much broader function for this kinase family including phosphorylation of cytosolic substrates involved in cell signaling pathways stimulated by GPCRs as well as non-GPCRs. In addition, GRKs modulate signaling via phosphorylation-independent functions. Because of these various biochemical functions, GRKs have been shown to affect critical physiological and pathophysiological processes and thus are considered as drug targets in diseases such as heart failure. Role of GRKs in inflammation and inflammatory diseases is an evolving area of research and several studies including work from our lab in the recent years have demonstrated critical role of GRKs in the immune system. In this review we discuss the classical and the newly emerging functions of GRKs in the immune system and their role in inflammation and disease processes. PMID:26226012

  7. Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay

    SciTech Connect

    Tang, Longkun E-mail: xqwu@whu.edu.cn; Wu, Xiaoqun E-mail: xqwu@whu.edu.cn; Lu, Jun-an; Lü, Jinhu

    2015-03-15

    Network synchronized regions play an extremely important role in network synchronization according to the master stability function framework. This paper focuses on network synchronous state stability via studying the effects of nodal dynamics, coupling delay, and coupling way on synchronized regions in Logistic map networks. Theoretical and numerical investigations show that (1) network synchronization is closely associated with its nodal dynamics. Particularly, the synchronized region bifurcation points through which the synchronized region switches from one type to another are in good agreement with those of the uncoupled node system, and chaotic nodal dynamics can greatly impede network synchronization. (2) The coupling delay generally impairs the synchronizability of Logistic map networks, which is also dominated by the parity of delay for some nodal parameters. (3) A simple nonlinear coupling facilitates network synchronization more than the linear one does. The results found in this paper will help to intensify our understanding for the synchronous state stability in discrete-time networks with coupling delay.

  8. Ribo-Proteomics Approach to Profile RNA-Protein and Protein-Protein Interaction Networks.

    PubMed

    Yeh, Hsin-Sung; Chang, Jae-Woong; Yong, Jeongsik

    2016-01-01

    Characterizing protein-protein and protein-RNA interaction networks is a fundamental step to understanding the function of an RNA-binding protein. In many cases, these interactions are transient and highly dynamic. Therefore, capturing stable as well as transient interactions in living cells for the identification of protein-binding partners and the mapping of RNA-binding sequences is key to a successful establishment of the molecular interaction network. In this chapter, we will describe a method for capturing the molecular interactions in living cells using formaldehyde as a crosslinker and enriching a specific RNA-protein complex from cell extracts followed by mass spectrometry and Next-Gen sequencing analyses. PMID:26965265

  9. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  10. Dynamic interactions of proteins in complex networks

    SciTech Connect

    Appella, E.; Anderson, C.

    2009-10-01

    Recent advances in techniques such as NMR and EPR spectroscopy have enabled the elucidation of how proteins undergo structural changes to act in concert in complex networks. The three minireviews in this series highlight current findings and the capabilities of new methodologies for unraveling the dynamic changes controlling diverse cellular functions. They represent a sampling of the cutting-edge research presented at the 17th Meeting of Methods in Protein Structure Analysis, MPSA2008, in Sapporo, Japan, 26-29 August, 2008 (http://www.iapsap.bnl.gov). The first minireview, by Christensen and Klevit, reports on a structure-based yeast two-hybrid method for identifying E2 ubiquitin-conjugating enzymes that interact with the E3 BRCA1/BARD1 heterodimer ligase to generate either mono- or polyubiquitinated products. This method demonstrated for the first time that the BRCA1/BARD1 E3 can interact with 10 different E2 enzymes. Interestingly, the interaction with multiple E2 enzymes displayed unique ubiquitin-transfer properties, a feature expected to be common among other RING and U-box E3s. Further characterization of new E3 ligases and the E2 enzymes that interact with them will greatly enhance our understanding of ubiquitin transfer and facilitate studies of roles of ubiquitin and ubiquitin-like proteins in protein processing and trafficking. Stein et al., in the second minireview, describe recent progress in defining the binding specificity of different peptide-binding domains. The authors clearly point out that transient peptide interactions mediated by both post-translational modifications and disordered regions ensure a high level of specificity. They postulate that a regulatory code may dictate the number of combinations of domains and post-translational modifications needed to achieve the required level of interaction specificity. Moreover, recognition alone is not enough to obtain a stable complex, especially in a complex cellular environment. Increasing

  11. Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

    PubMed Central

    Abrams, Jennifer L.; Morano, Kevin A.

    2013-01-01

    Proteostasis, defined as the combined processes of protein folding/biogenesis, refolding/repair, and degradation, is a delicate cellular balance that must be maintained to avoid deleterious consequences 1. External or internal factors that disrupt this balance can lead to protein aggregation, toxicity and cell death. In humans this is a major contributing factor to the symptoms associated with neurodegenerative disorders such as Huntington's, Parkinson's, and Alzheimer's diseases 10. It is therefore essential that the proteins involved in maintenance of proteostasis be identified in order to develop treatments for these debilitating diseases. This article describes techniques for monitoring in vivo protein folding at near-real time resolution using the model protein firefly luciferase fused to green fluorescent protein (FFL-GFP). FFL-GFP is a unique model chimeric protein as the FFL moiety is extremely sensitive to stress-induced misfolding and aggregation, which inactivates the enzyme 12. Luciferase activity is monitored using an enzymatic assay, and the GFP moiety provides a method of visualizing soluble or aggregated FFL using automated microscopy. These coupled methods incorporate two parallel and technically independent approaches to analyze both refolding and functional reactivation of an enzyme after stress. Activity recovery can be directly correlated with kinetics of disaggregation and re-solubilization to better understand how protein quality control factors such as protein chaperones collaborate to perform these functions. In addition, gene deletions or mutations can be used to test contributions of specific proteins or protein subunits to this process. In this article we examine the contributions of the protein disaggregase Hsp104 13, known to partner with the Hsp40/70/nucleotide exchange factor (NEF) refolding system 5, to protein refolding to validate this approach. PMID:23892247

  12. A Least Square Method Based Model for Identifying Protein Complexes in Protein-Protein Interaction Network

    PubMed Central

    Dai, Qiguo; Guo, Maozu; Guo, Yingjie; Liu, Xiaoyan; Liu, Yang; Teng, Zhixia

    2014-01-01

    Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity. PMID:25405206

  13. Dynamical network of residue–residue contacts reveals coupled allosteric effects in recognition, catalysis, and mutation

    PubMed Central

    Doshi, Urmi; Holliday, Michael J.; Eisenmesser, Elan Z.; Hamelberg, Donald

    2016-01-01

    Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis remains ambiguous. Here, we simulate CypA using multiple-microsecond-long atomistic molecular dynamics in explicit solvent and carry out NMR experiments. We analyze a large amount of time-dependent multidimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue–residue contacts. The effects of substrate binding are observed to be largely sensed at a location over 15 Å from the active site, implying its importance in allostery. Using NMR experiments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is found to be coupled and temporally dispersed, ranging over 4 to 5 orders of magnitude. Finally, using network centrality measures we demonstrate the changes in the communication network, connectivity, and influence of CypA residues upon substrate binding, mutation, and during catalysis. We identify key residues that potentially act as a bottleneck in the communication flow through the distinct regions in CypA and, therefore, as targets for future mutational studies. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins, in general. PMID:27071107

  14. Modeling of price and profit in coupled-ring networks

    NASA Astrophysics Data System (ADS)

    Tangmongkollert, Kittiwat; Suwanna, Sujin

    2016-06-01

    We study the behaviors of magnetization, price, and profit profiles in ring networks in the presence of the external magnetic field. The Ising model is used to determine the state of each node, which is mapped to the buy-or-sell state in a financial market, where +1 is identified as the buying state, and -1 as the selling state. Price and profit mechanisms are modeled based on the assumption that price should increase if demand is larger than supply, and it should decrease otherwise. We find that the magnetization can be induced between two rings via coupling links, where the induced magnetization strength depends on the number of the coupling links. Consequently, the price behaves linearly with time, where its rate of change depends on the magnetization. The profit grows like a quadratic polynomial with coefficients dependent on the magnetization. If two rings have opposite direction of net spins, the price flows in the direction of the majority spins, and the network with the minority spins gets a loss in profit.

  15. Coupled protein diffusion and folding in the cell.

    PubMed

    Guo, Minghao; Gelman, Hannah; Gruebele, Martin

    2014-01-01

    When a protein unfolds in the cell, its diffusion coefficient is affected by its increased hydrodynamic radius and by interactions of exposed hydrophobic residues with the cytoplasmic matrix, including chaperones. We characterize protein diffusion by photobleaching whole cells at a single point, and imaging the concentration change of fluorescent-labeled protein throughout the cell as a function of time. As a folded reference protein we use green fluorescent protein. The resulting region-dependent anomalous diffusion is well characterized by 2-D or 3-D diffusion equations coupled to a clustering algorithm that accounts for position-dependent diffusion. Then we study diffusion of a destabilized mutant of the enzyme phosphoglycerate kinase (PGK) and of its stable control inside the cell. Unlike the green fluorescent protein control's diffusion coefficient, PGK's diffusion coefficient is a non-monotonic function of temperature, signaling 'sticking' of the protein in the cytosol as it begins to unfold. The temperature-dependent increase and subsequent decrease of the PGK diffusion coefficient in the cytosol is greater than a simple size-scaling model suggests. Chaperone binding of the unfolding protein inside the cell is one plausible candidate for even slower diffusion of PGK, and we test the plausibility of this hypothesis experimentally, although we do not rule out other candidates. PMID:25436502

  16. Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks

    PubMed Central

    Wu, Hao; Gao, Lin; Dong, Jihua; Yang, Xiaofei

    2014-01-01

    In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks. PMID:24642838

  17. Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks.

    PubMed

    Wu, Hao; Gao, Lin; Dong, Jihua; Yang, Xiaofei

    2014-01-01

    In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks. PMID:24642838

  18. G Protein-Coupled Receptor Rhodopsin: A Prospectus

    PubMed Central

    Filipek, Sławomir; Stenkamp, Ronald E.; Teller, David C.; Palczewski, Krzysztof

    2006-01-01

    Rhodopsin is a retinal photoreceptor protein of bipartite structure consisting of the transmembrane protein opsin and a light-sensitive chromophore 11-cis-retinal, linked to opsin via a protonated Schiff base. Studies on rhodopsin have unveiled many structural and functional features that are common to a large and pharmacologically important group of proteins from the G protein-coupled receptor (GPCR) superfamily, of which rhodopsin is the best-studied member. In this work, we focus on structural features of rhodopsin as revealed by many biochemical and structural investigations. In particular, the high-resolution structure of bovine rhodopsin provides a template for understanding how GPCRs work. We describe the sensitivity and complexity of rhodopsin that lead to its important role in vision. PMID:12471166

  19. Nanobody stabilization of G protein coupled receptor conformational states

    PubMed Central

    Steyaert, Jan; K Kobilka, Brian

    2011-01-01

    Remarkable progress has been made in the field of G protein coupled receptor (GPCR) structural biology during the past four years. Several obstacles to generating diffraction quality crystals of GPCRs have been overcome by combining innovative methods ranging from protein engineering to lipid-based screens and microdiffraction technology. The initial GPCR structures represent energetically stable inactive-state conformations. However, GPCRs signal through different G protein isoforms or G protein-independent effectors upon ligand binding suggesting the existence of multiple ligand-specific active states. These active-state conformations are unstable in the absence of specific cytosolic signaling partners representing new challenges for structural biology. Camelid single chain antibody fragments (nanobodies) show promise for stabilizing active GPCR conformations and as chaperones for crystallogenesis. PMID:21782416

  20. Joint clustering of protein interaction networks through Markov random walk

    PubMed Central

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  1. Joint clustering of protein interaction networks through Markov random walk.

    PubMed

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  2. Estimating functional connectivity in an electrically coupled interneuron network

    PubMed Central

    Alcami, Pepe; Marty, Alain

    2013-01-01

    Even though it has been known for some time that in many mammalian brain areas interneurons are electrically coupled, a quantitative description of the network electrical connectivity and its impact on cellular passive properties is still lacking. Approaches used so far to solve this problem are limited because they do not readily distinguish junctions among direct neighbors from indirect junctions involving intermediary, multiply connected cells. In the cerebellar cortex, anatomical and functional evidence indicates electrical coupling between molecular layer interneurons (basket and stellate cells). An analysis of the capacitive currents obtained under voltage clamp in molecular layer interneurons of juvenile rats or mice reveals an exponential component with a time constant of ∼20 ms, which represents capacitive loading of neighboring cells through gap junctions. These results, taken together with dual cell recording of electrical synapses, have led us to estimate the number of direct neighbors to be ∼4 for rat basket cells and ∼1 for rat stellate cells. The weighted number of neighbors (number of neighbors, both direct and indirect, weighted with the percentage of voltage deflection at steady state) was 1.69 in basket cells and 0.23 in stellate cells. The last numbers indicate the spread of potential changes in the network and serve to estimate the contribution of gap junctions to cellular input conductance. In conclusion the present work offers effective tools to analyze the connectivity of electrically connected interneuron networks, and it indicates that in juvenile rodents, electrical communication is stronger among basket cells than among stellate cells. PMID:24248377

  3. Spatial and functional organization of mitochondrial protein network

    PubMed Central

    Yang, Jae-Seong; Kim, Jinho; Park, Solip; Jeon, Jouhyun; Shin, Young-Eun; Kim, Sanguk

    2013-01-01

    Characterizing the spatial organization of the human mitochondrial proteome will enhance our understanding of mitochondrial functions at the molecular level and provide key insight into protein-disease associations. However, the sub-organellar location and possible association with mitochondrial diseases are not annotated for most mitochondrial proteins. Here, we characterized the functional and spatial organization of mitochondrial proteins by assessing their position in the Mitochondrial Protein Functional (MPF) network. Network position was assigned to the MPF network and facilitated the determination of sub-organellar location and functional organization of mitochondrial proteins. Moreover, network position successfully identified candidate disease genes of several mitochondrial disorders. Thus, our data support the use of network position as a novel method to explore the molecular function and pathogenesis of mitochondrial proteins. PMID:23466738

  4. Modularity in the evolution of yeast protein interaction network

    PubMed Central

    Ogishima, Soichi; Tanaka, Hiroshi; Nakaya, Jun

    2015-01-01

    Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution. PMID:25914446

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  6. Network pattern of residue packing in helical membrane proteins and its application in membrane protein structure prediction.

    PubMed

    Pabuwal, Vagmita; Li, Zhijun

    2008-01-01

    De novo protein structure prediction plays an important role in studies of helical membrane proteins as well as structure-based drug design efforts. Developing an accurate scoring function for protein structure discrimination and validation remains a current challenge. Network approaches based on overall network patterns of residue packing have proven useful in soluble protein structure discrimination. It is thus of interest to apply similar approaches to the studies of residue packing in membrane proteins. In this work, we first carried out such analysis on a set of diverse, non-redundant and high-resolution membrane protein structures. Next, we applied the same approach to three test sets. The first set includes nine structures of membrane proteins with the resolution worse than 2.5 A; the other two sets include a total of 101 G-protein coupled receptor models, constructed using either de novo or homology modeling techniques. Results of analyses indicate the two criteria derived from studying high-resolution membrane protein structures are good indicators of a high-quality native fold and the approach is very effective for discriminating native membrane protein folds from less-native ones. These findings should be of help for the investigation of the fundamental problem of membrane protein structure prediction. PMID:18178566

  7. On the origin of residual dipolar couplings from denatured proteins.

    PubMed

    Louhivuori, Martti; Pääkkönen, Kimmo; Fredriksson, Kai; Permi, Perttu; Lounila, Juhani; Annila, Arto

    2003-12-17

    Effects of steric obstruction on random flight chains are examined. Spatial probability distributions are elaborated to calculate residual dipolar couplings and residual chemical shift anisotropy, parameters that are acquired by NMR spectroscopy from solutes dissolved in dilute liquid crystals. Calculations yield chain length and residue position-dependent values in good agreement with simulations to provide understanding of recently acquired data from denatured proteins. PMID:14664613

  8. Protein Co-Expression Network Analysis (ProCoNA)

    SciTech Connect

    Gibbs, David L.; Baratt, Arie; Baric, Ralph; Kawaoka, Yoshihiro; Smith, Richard D.; Orwoll, Eric S.; Katze, Michael G.; Mcweeney, Shannon K.

    2013-06-01

    Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

  9. Whole proteome identification of plant candidate G-protein coupled receptors in Arabidopsis, rice, and poplar: computational prediction and in-vivo protein coupling

    PubMed Central

    Gookin, Timothy E; Kim, Junhyong; Assmann, Sarah M

    2008-01-01

    Background The classic paradigm of heterotrimeric G-protein signaling describes a heptahelical, membrane-spanning G-protein coupled receptor that physically interacts with an intracellular Gα subunit of the G-protein heterotrimer to transduce signals. G-protein coupled receptors comprise the largest protein superfamily in metazoa and are physiologically important as they sense highly diverse stimuli and play key roles in human disease. The heterotrimeric G-protein signaling mechanism is conserved across metazoa, and also readily identifiable in plants, but the low sequence conservation of G-protein coupled receptors hampers the identification of novel ones. Using diverse computational methods, we performed whole-proteome analyses of the three dominant model plant species, the herbaceous dicot Arabidopsis thaliana (mouse-eared cress), the monocot Oryza sativa (rice), and the woody dicot Populus trichocarpa (poplar), to identify plant protein sequences most likely to be GPCRs. Results Our stringent bioinformatic pipeline allowed the high confidence identification of candidate G-protein coupled receptors within the Arabidopsis, Oryza, and Populus proteomes. We extended these computational results through actual wet-bench experiments where we tested over half of our highest ranking Arabidopsis candidate G-protein coupled receptors for the ability to physically couple with GPA1, the sole Gα in Arabidopsis. We found that seven out of eight tested candidate G-protein coupled receptors do in fact interact with GPA1. We show through G-protein coupled receptor classification and molecular evolutionary analyses that both individual G-protein coupled receptor candidates and candidate G-protein coupled receptor families are conserved across plant species and that, in some cases, this conservation extends to metazoans. Conclusion Our computational and wet-bench results provide the first step toward understanding the diversity, conservation, and functional roles of plant

  10. Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.

    PubMed

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

    2016-07-01

    Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences. PMID:27267620

  11. Implementation of a pulse coupled neural network in FPGA.

    PubMed

    Waldemark, J; Millberg, M; Lindblad, T; Waldemark, K; Becanovic, V

    2000-06-01

    The Pulse Coupled neural network, PCNN, is a biologically inspired neural net and it can be used in various image analysis applications, e.g. time-critical applications in the field of image pre-processing like segmentation, filtering, etc. a VHDL implementation of the PCNN targeting FPGA was undertaken and the results presented here. The implementation contains many interesting features. By pipelining the PCNN structure a very high throughput of 55 million neuron iterations per second could be achieved. By making the coefficients re-configurable during operation, a complete recognition system could be implemented on one, or maybe two, chip(s). Reconsidering the ranges and resolutions of the constants may save a lot of hardware, since the higher resolution requires larger multipliers, adders, memories etc. PMID:11011789

  12. Signal bi-amplification in networks of unidirectionally coupled MEMS

    NASA Astrophysics Data System (ADS)

    Tchakui, Murielle Vanessa; Woafo, Paul; Colet, Pere

    2016-01-01

    The purpose of this paper is to analyze the propagation and the amplification of an input signal in networks of unidirectionally coupled micro-electro-mechanical systems (MEMS). Two types of external excitations are considered: sinusoidal and stochastic signals. We show that sinusoidal signals are amplified up to a saturation level which depends on the transmission rate and despite MEMS being nonlinear the sinusoidal shape is well preserved if the number of MEMS is not too large. However, increasing the number of MEMS, there is an instability that leads to chaotic behavior and which is triggered by the amplification of the harmonics generated by the nonlinearities. We also show that for stochastic input signals, the MEMS array acts as a band-pass filter and after just a few elements the signal has a narrow power spectra.

  13. Hybrid dynamics in delay-coupled swarms with ``mothership'' networks

    NASA Astrophysics Data System (ADS)

    Hindes, Jason; Schwartz, Ira

    Swarming behavior continues to be a subject of immense interest because of its centrality in many naturally occurring systems in biology and physics. Moreover, the development of autonomous mobile agents that can mimic the behavior of swarms and can be engineered to perform complex tasks without constant intervention is a very active field of practical research. Here we examine the effects on delay-coupled swarm pattern formation from the inclusion of a small fraction of highly connected nodes, ``motherships'', in the swarm interaction network. We find a variety of new behaviors and bifurcations, including new hybrid motions of previously analyzed patterns. Both numerical and analytic techniques are used to classify the dynamics and construct the phase diagram. The implications for swarm control and robustness from topological heterogeneity are also discussed. This research was funded by the office of Naval Research (ONR), and was performed while JH held a National Research Council Research Associateship Award.

  14. A Neural Network Reconstruction of the Coupled Inner Magnetospheric Environment

    NASA Astrophysics Data System (ADS)

    Bortnik, J.; Li, W.; Thorne, R. M.; Yue, C.; Chu, X.; Angelopoulos, V.; Blum, L. W.; Ma, Q.; Kletzing, C.; Reeves, G. D.; Spence, H. E.

    2015-12-01

    The Earth's inner magnetosphere represents a dynamic and highly coupled system that is often challenging for physical models to reproduce accurately, but knowledge of this system and its spatiotemporal evolution is crucial for both practical applications in the form of space weather, and scientific insight. In this presentation, we demonstrate the use of a deep neural network in predicting and specifying the global, time-varying distributions of a number of key wave and particle populations including chorus, hiss and magnetosonic wave, and electrion distributions from cold plasma to plasmasheet energies and right through the relativistic and ultra-relativistic populations. We show the temporal and spatial relationships between these waves and particle populations that ultimately lead to the dynamics of the ultra-relativistic particles and discuss implications.

  15. Information Filtering via Biased Random Walk on Coupled Social Network

    PubMed Central

    Dong, Qiang; Fu, Yan

    2014-01-01

    The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods. PMID:25147867

  16. Three applications of pulse-coupled neural networks

    NASA Astrophysics Data System (ADS)

    Ranganath, Heggere S.; Banish, Michele R.; Karpinsky, John R.; Clark, Rodney L.; Germany, Glynn A.; Richards, Philip G.

    1999-03-01

    Image segmentation is one of the major application areas for Pulsed Coupled Neural Networks (PCNN). Previous research has shown that the ability of PCNN to ignore minor variations in intensity and small spatial discontinuities in images is beneficial to image segmentation as well as image smoothing. This paper describes research and development projects in progress in which PCNN is used for the segmentation of three different types of digital images. The software for the diagnosis of Pulmonary Embolism from VQ lung scans uses PCNN in single burst mode for segmenting perfusion and ventilation images. The second project is attempting to detect ischemia by comparing 3D SPECT (Single Photon Emission Computed Tomography) images of heart obtained during stress and rest conditions, respectively. The third application is a space science project which deals with the study of global auroral images obtained from Ultraviolet Imager. The paper also describes an hardware implementation of PCNN as an electro-optical chip.

  17. Information filtering via biased random walk on coupled social network.

    PubMed

    Nie, Da-Cheng; Zhang, Zi-Ke; Dong, Qiang; Sun, Chongjing; Fu, Yan

    2014-01-01

    The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods. PMID:25147867

  18. Oligomeric forms of G protein-coupled receptors (GPCRs)

    PubMed Central

    Palczewski, Krzysztof

    2010-01-01

    Oligomerization is a general characteristic of cell membrane receptors that is shared by G protein-coupled receptors (GPCRs) together with their G protein partners. Recent studies of these complexes, both in vivo and in purified reconstituted forms, unequivocally support this contention for GPCRs, perhaps with only rare exceptions. As evidence has evolved from experimental cell lines to more relevant in vivo studies and from indirect biophysical approaches to well defined isolated complexes of dimeric receptors alone and complexed with G proteins, there is an expectation that the structural basis of oligomerization and the functional consequences for membrane signaling will be elucidated. Oligomerization of cell membrane receptors is fully supported by both thermodynamic calculations and the selectivity and duration of signaling required to reach targets located in various cellular compartments. PMID:20538466

  19. PDZ Protein Regulation of G Protein-Coupled Receptor Trafficking and Signaling Pathways.

    PubMed

    Dunn, Henry A; Ferguson, Stephen S G

    2015-10-01

    G protein-coupled receptors (GPCRs) contribute to the regulation of every aspect of human physiology and are therapeutic targets for the treatment of numerous diseases. As a consequence, understanding the myriad of mechanisms controlling GPCR signaling and trafficking is essential for the development of new pharmacological strategies for the treatment of human pathologies. Of the many GPCR-interacting proteins, postsynaptic density protein of 95 kilodaltons, disc large, zona occludens-1 (PDZ) domain-containing proteins appear most abundant and have similarly been implicated in disease mechanisms. PDZ proteins play an important role in regulating receptor and channel protein localization within synapses and tight junctions and function to scaffold intracellular signaling protein complexes. In the current study, we review the known functional interactions between PDZ domain-containing proteins and GPCRs and provide insight into the potential mechanisms of action. These PDZ domain-containing proteins include the membrane-associated guanylate-like kinases [postsynaptic density protein of 95 kilodaltons; synapse-associated protein of 97 kilodaltons; postsynaptic density protein of 93 kilodaltons; synapse-associated protein of 102 kilodaltons; discs, large homolog 5; caspase activation and recruitment domain and membrane-associated guanylate-like kinase domain-containing protein 3; membrane protein, palmitoylated 3; calcium/calmodulin-dependent serine protein kinase; membrane-associated guanylate kinase protein (MAGI)-1, MAGI-2, and MAGI-3], Na(+)/H(+) exchanger regulatory factor proteins (NHERFs) (NHERF1, NHERF2, PDZ domain-containing kidney protein 1, and PDZ domain-containing kidney protein 2), Golgi-associated PDZ proteins (Gα-binding protein interacting protein, C-terminus and CFTR-associated ligand), PDZ domain-containing guanine nucleotide exchange factors (GEFs) 1 and 2, regulator of G protein signaling (RGS)-homology-RhoGEFs (PDZ domain-containing RhoGEF and

  20. Surface plasmon resonance applied to G protein-coupled receptors

    PubMed Central

    Locatelli-Hoops, Silvia; Yeliseev, Alexei A.; Gawrisch, Klaus; Gorshkova, Inna

    2013-01-01

    G protein-coupled receptors (GPCR) are integral membrane proteins that transmit signals from external stimuli to the cell interior via activation of GTP-binding proteins (G proteins) thereby mediating key sensorial, hormonal, metabolic, immunological, and neurotransmission processes. Elucidating their structure and mechanism of interaction with extracellular and intracellular binding partners is of fundamental importance and highly relevant to rational design of new effective drugs. Surface plasmon resonance (SPR) has become a method of choice for studying biomolecular interactions at interfaces because measurements take place in real-time and do not require labeling of any of the interactants. However, due to the particular challenges imposed by the high hydrophobicity of membrane proteins and the great diversity of receptor-stimulating ligands, the application of this technique to characterize interactions of GPCR is still in the developmental phase. Here we give an overview of the principle of SPR and analyze current approaches for the preparation of the sensor chip surface, capture and stabilization of GPCR, and experimental design to characterize their interaction with ligands, G proteins and specific antibodies. PMID:24466506

  1. Cell-Free Expression of G Protein-Coupled Receptors.

    PubMed

    Segers, Kenneth; Masure, Stefan

    2015-01-01

    The large-scale production of recombinant G protein-coupled receptors (GPCRs) is one of the major bottlenecks that hamper functional and structural studies of this important class of integral membrane proteins. Heterologous overexpression of GPCRs often results in low yields of active protein, usually due to a combination of several factors, such as low expression levels, protein insolubility, host cell toxicity, and the need to use harsh and often denaturing detergents (e.g., SDS, LDAO, OG, and DDM, among others) to extract the recombinant receptor from the host cell membrane. Many of these problematic issues are inherently linked to cell-based expression systems and can therefore be circumvented by the use of cell-free systems. In this unit, we provide a range of protocols for the production of GPCRs in a cell-free expression system. Using this system, we typically obtain GPCR expression levels of ∼1 mg per ml of reaction mixture in the continuous-exchange configuration. Although the protocols in this unit have been optimized for the cell-free expression of GPCRs, they should provide a good starting point for the production of other classes of membrane proteins, such as ion channels, aquaporins, carrier proteins, membrane-bound enzymes, and even large molecular complexes. PMID:26237676

  2. Structural basis for chemokine recognition and activation of a viral G protein-coupled receptor

    SciTech Connect

    Burg, John S.; Ingram, Jessica R.; Venkatakrishnan, A.J.; Jude, Kevin M.; Dukkipati, Abhiram; Feinberg, Evan N.; Angelini, Alessandro; Waghray, Deepa; Dror, Ron O.; Ploegh, Hidde L.; Garcia, K. Christopher

    2015-03-05

    Chemokines are small proteins that function as immune modulators through activation of chemokine G protein-coupled receptors (GPCRs). Several viruses also encode chemokines and chemokine receptors to subvert the host immune response. How protein ligands activate GPCRs remains unknown. We report the crystal structure at 2.9 angstrom resolution of the human cytomegalovirus GPCR US28 in complex with the chemokine domain of human CX3CL1 (fractalkine). The globular body of CX3CL1 is perched on top of the US28 extracellular vestibule, whereas its amino terminus projects into the central core of US28. The transmembrane helices of US28 adopt an active-state-like conformation. Atomic-level simulations suggest that the agonist-independent activity of US28 may be due to an amino acid network evolved in the viral GPCR to destabilize the receptor’s inactive state.

  3. Self-organized criticality in proteins: Hydropathic roughening profiles of G-protein-coupled receptors

    NASA Astrophysics Data System (ADS)

    Phillips, J. C.

    2013-03-01

    Proteins appear to be the most dramatic natural example of self-organized criticality (SOC), a concept that explains many otherwise apparently unlikely phenomena. Protein conformational functionality is often dominated by long-range hydrophobic or hydrophilic interactions which both drive protein compaction and mediate protein-protein interactions. Superfamily transmembrane G-protein-coupled receptors (GPCRs) are the largest family of proteins in the human genome; their amino acid sequences form the largest database for protein-membrane interactions. While there are now structural data on the heptad transmembrane structures of representatives of several heptad families, here we show how fresh insights into global and some local chemical trends in GPCR properties can be obtained accurately from sequences alone, especially by algebraically separating the extracellular and cytoplasmic loops from transmembrane segments. The global mediation of long-range water-protein interactions occurs in conjunction with modulation of these interactions by roughened interfaces. Hydropathic roughening profiles are defined here solely in terms of amino acid sequences, and knowledge of protein coordinates is not required. Roughening profiles both for GPCR and some simpler protein families display accurate and transparent connections to protein functionality, and identify natural length scales for protein functionality.

  4. Regulation of G protein-coupled receptor export trafficking

    PubMed Central

    Dong, Chunmin; Filipeanu, Catalin M.; Duvernay, Matthew T.; Wu, Guangyu

    2007-01-01

    G protein-coupled receptors (GPCRs) constitute a superfamily of cell-surface receptors which share a common topology of seven transmembrane domains and modulate a variety of cell functions through coupling to heterotrimeric G proteins by responding to a vast array of stimuli. The magnitude of cellular response elicited by a given signal is dictated by the level of GPCR expression at the plasma membrane, which is the balance of elaborately regulated endocytic and exocytic trafficking. This review will cover recent advances in understanding the molecular mechanism underlying anterograde transport of the newly synthesized GPCRs from the endoplasmic reticulum (ER) through the Golgi to the plasma membrane. We will focus on recently identified motifs involved in GPCR exit from the ER and the Golgi, GPCR folding in the ER and the rescue of misfolded receptors from within, GPCR-interacting proteins that modulate receptor cell-surface targeting, pathways that mediate GPCR traffic, and the functional role of export in controlling GPCR signaling. PMID:17074298

  5. Desensitization of G protein-coupled receptors and neuronal functions.

    PubMed

    Gainetdinov, Raul R; Premont, Richard T; Bohn, Laura M; Lefkowitz, Robert J; Caron, Marc G

    2004-01-01

    G protein-coupled receptors (GPCRs) have proven to be the most highly favorable class of drug targets in modern pharmacology. Over 90% of nonsensory GPCRs are expressed in the brain, where they play important roles in numerous neuronal functions. GPCRs can be desensitized following activation by agonists by becoming phosphorylated by members of the family of G protein-coupled receptor kinases (GRKs). Phosphorylated receptors are then bound by arrestins, which prevent further stimulation of G proteins and downstream signaling pathways. Discussed in this review are recent progress in understanding basics of GPCR desensitization, novel functional roles, patterns of brain expression, and receptor specificity of GRKs and beta arrestins in major brain functions. In particular, screening of genetically modified mice lacking individual GRKs or beta arrestins for alterations in behavioral and biochemical responses to cocaine and morphine has revealed a functional specificity in dopamine and mu-opioid receptor regulation of locomotion and analgesia. An important and specific role of GRKs and beta arrestins in regulating physiological responsiveness to psychostimulants and morphine suggests potential involvement of these molecules in certain brain disorders, such as addiction, Parkinson's disease, mood disorders, and schizophrenia. Furthermore, the utility of a pharmacological strategy aimed at targeting this GPCR desensitization machinery to regulate brain functions can be envisaged. PMID:15217328

  6. Identifying protein complexes in protein-protein interaction networks by using clique seeds and graph entropy.

    PubMed

    Chen, Bolin; Shi, Jinhong; Zhang, Shenggui; Wu, Fang-Xiang

    2013-01-01

    The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. In this paper, we first introduce a new seed-selection strategy for seed-growth style algorithms. Cliques rather than individual vertices are employed as initial seeds. After that, a result-modification approach is proposed based on this seed-selection strategy. Predictions generated by higher order clique seeds are employed to modify results that are generated by lower order ones. The performance of this seed-selection strategy and the result-modification approach are tested by using the entropy-based algorithm, which is currently the best seed-growth style algorithm to detect protein complexes from PPI networks. In addition, we investigate four pairs of strategies for this algorithm in order to improve its accuracy. The numerical experiments are conducted on a Saccharomyces cerevisiae PPI network. The group of best predictions consists of 1711 clusters, with the average f-score at 0.68 after removing all similar and redundant clusters. We conclude that higher order clique seeds can generate predictions with higher accuracy and that our improved entropy-based algorithm outputs more reasonable predictions than the original one. PMID:23112006

  7. Morphisms of reaction networks that couple structure to function

    PubMed Central

    2014-01-01

    Background The mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. However, similarity of mechanism is better revealed by relationships between network structures. Results We define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm. Conclusions Structural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks. PMID:25128194

  8. Exact coupling threshold for structural transition reveals diversified behaviors in interconnected networks

    NASA Astrophysics Data System (ADS)

    Darabi Sahneh, Faryad; Scoglio, Caterina; Van Mieghem, Piet

    2015-10-01

    An interconnected network features a structural transition between two regimes [F. Radicchi and A. Arenas, Nat. Phys. 9, 717 (2013), 10.1038/nphys2761]: one where the network components are structurally distinguishable and one where the interconnected network functions as a whole. Our exact solution for the coupling threshold uncovers network topologies with unexpected behaviors. Specifically, we show conditions that superdiffusion, introduced by Gómez et al. [Phys. Rev. Lett. 110, 028701 (2013), 10.1103/PhysRevLett.110.028701], can occur despite the network components functioning distinctly. Moreover, we find that components of certain interconnected network topologies are indistinguishable despite very weak coupling between them.

  9. G Protein-Coupled Receptors in Major Psychiatric Disorders

    PubMed Central

    Catapano, Lisa A.; Manji, Husseini K.

    2007-01-01

    Although the molecular mechanisms underlying psychiatric illnesses such as depression, bipolar disorder and schizophrenia remain incompletely understood, there is increasing clinical, pharmacologic, and genetic evidence that G protein-coupled receptors (GPCRs) play critical roles in these disorders and their treatments. This perspectives paper reviews and synthesizes the available data. Dysfunction of multiple neurotransmitter and neuropeptide GPCRs in frontal cortex and limbic-related regions, such as the hippocampus, hypothalamus and brainstem, likely underlies the complex clinical picture that includes cognitive, perceptual, affective and motoric symptoms. The future development of novel agents targeting GPCR signaling cascades remains an exciting prospect for patients refractory to existing therapeutics. PMID:17078926

  10. G Protein-Coupled Receptors in Anopheles gambiae

    NASA Astrophysics Data System (ADS)

    Hill, Catherine A.; Fox, A. Nicole; Pitts, R. Jason; Kent, Lauren B.; Tan, Perciliz L.; Chrystal, Mathew A.; Cravchik, Anibal; Collins, Frank H.; Robertson, Hugh M.; Zwiebel, Laurence J.

    2002-10-01

    We used bioinformatic approaches to identify a total of 276 G protein-coupled receptors (GPCRs) from the Anopheles gambiae genome. These include GPCRs that are likely to play roles in pathways affecting almost every aspect of the mosquito's life cycle. Seventy-nine candidate odorant receptors were characterized for tissue expression and, along with 76 putative gustatory receptors, for their molecular evolution relative to Drosophila melanogaster. Examples of lineage-specific gene expansions were observed as well as a single instance of unusually high sequence conservation.

  11. G protein-coupled receptors in drug discovery.

    PubMed

    Nambi, Ponnal; Aiyar, Nambi

    2003-04-01

    G protein-coupled receptors (GPCRs) represent one of the most important drug discovery targets such that compounds targeted against GPCRs represent the single largest drug class currently on the market. With the revolutionary advances in human genome sciences and the identification of numerous orphan GPCRs, it is even more important to identify ligands for these orphan GPCRs so that their physiological and pathological roles can be delineated. To this end, major pharmaceutical industries are investing enormous amounts of time and money to achieve this object. This review is a bird's eye view on the various aspects of GPCRs in drug discovery. PMID:15090195

  12. Ultrafast Hydration Dynamics and Coupled Water-Protein Fluctuations in Apomyoglobin

    NASA Astrophysics Data System (ADS)

    Yang, Yi; Zhang, Luyuan; Wang, Lijuan; Zhong, Dongping

    2009-06-01

    Protein hydration dynamics are of fundamental importance to its structure and function. Here, we characterize the global solvation dynamics and anisotropy dynamics around the apomyoglobin surface in different conformational states (native and molten globule) by measuring the Stokes shift and anisotropy decay of tryptophan with femtosecond-resolved fluorescence upconversion. With site-directed mutagenesis, we designed sixteen mutants with one tryptophan in each, and placed the probe at a desirable position ranging from buried in the protein core to fully solvent-exposed on the protein surface. In all protein sites studied, two distinct solvation relaxations (1-8 ps and 20-200 ps) were observed, reflecting the initial collective water relaxation and subsequent hydrogen-bond network restructuring, respectively, and both are strongly correlated with protein's local structures and chemical properties. The hydration dynamics of the mutants in molten globule state are faster than those observed in native state, indicating that the protein becomes more flexible and less structured when its conformation is converted from fully-folded native state to partially-folded molten globule state. Complementary, fluorescence anisotropy dynamics of all mutants in native state show an increasing trend of wobbling times (40-260 ps) when the location of the probe is changed from a loop, to a lateral helix, and then, to the compact protein core. Such an increase in wobbling times is related to the local protein structural rigidity, which relates the interaction of water with side chains. The ultrafast hydration dynamics and related side-chain motion around the protein surface unravel the coupled water-protein fluctuations on the picosecond time scales and indicate that the local protein motions are slaved by hydrating water fluctuations.

  13. Therapeutic antibodies directed at G protein-coupled receptors

    PubMed Central

    Hutchings, Catherine J; Koglin, Markus

    2010-01-01

    G protein-coupled receptors (GPCRs) are one of the most important classes of targets for small molecule drug discovery, but many current GPCRs of interest are proving intractable to small molecule discovery and may be better approached with bio-therapeutics. GPCRs are implicated in a wide variety of diseases where antibody therapeutics are currently used. These include inflammatory diseases such as rheumatoid arthritis and Crohn disease, as well as metabolic disease and cancer. Raising antibodies to GPCRs has been difficult due to problems in obtaining suitable antigen because GPCRs are often expressed at low levels in cells and are very unstable when purified. A number of new developments in overexpressing receptors, as well as formulating stable pure protein, are contributing to the growing interest in targeting GPCRs with antibodies. This review discusses the opportunities for targeting GPCRs with antibodies using these approaches and describes the therapeutic antibodies that are currently in clinical development. PMID:20864805

  14. A Usual G-Protein-Coupled Receptor in Unusual Membranes.

    PubMed

    Chawla, Udeep; Jiang, Yunjiang; Zheng, Wan; Kuang, Liangju; Perera, Suchithranga M D C; Pitman, Michael C; Brown, Michael F; Liang, Hongjun

    2016-01-11

    G-protein-coupled receptors (GPCRs) are the largest family of membrane-bound receptors and constitute about 50% of all known drug targets. They offer great potential for membrane protein nanotechnologies. We report here a charge-interaction-directed reconstitution mechanism that induces spontaneous insertion of bovine rhodopsin, the eukaryotic GPCR, into both lipid- and polymer-based artificial membranes. We reveal a new allosteric mode of rhodopsin activation incurred by the non-biological membranes: the cationic membrane drives a transition from the inactive MI to the activated MII state in the absence of high [H(+)] or negative spontaneous curvature. We attribute this activation to the attractive charge interaction between the membrane surface and the deprotonated Glu134 residue of the rhodopsin-conserved ERY sequence motif that helps break the cytoplasmic "ionic lock". This study unveils a novel design concept of non-biological membranes to reconstitute and harness GPCR functions in synthetic systems. PMID:26633591

  15. Serial Femtosecond Crystallography of G Protein-Coupled Receptors

    PubMed Central

    Liu, Wei; Wacker, Daniel; Gati, Cornelius; Han, Gye Won; James, Daniel; Wang, Dingjie; Nelson, Garrett; Weierstall, Uwe; Katritch, Vsevolod; Barty, Anton; Zatsepin, Nadia A.; Li, Dianfan; Messerschmidt, Marc; Boutet, Sébastien; Williams, Garth J.; Koglin, Jason E.; Seibert, M. Marvin; Wang, Chong; Shah, Syed T.A.; Basu, Shibom; Fromme, Raimund; Kupitz, Christopher; Rendek, Kimberley N.; Grotjohann, Ingo; Fromme, Petra; Kirian, Richard A.; Beyerlein, Kenneth R.; White, Thomas A.; Chapman, Henry N.; Caffrey, Martin; Spence, John C.H.; Stevens, Raymond C.; Cherezov, Vadim

    2014-01-01

    X-ray crystallography of G protein-coupled receptors and other membrane proteins is hampered by difficulties associated with growing sufficiently large crystals that withstand radiation damage and yield high-resolution data at synchrotron sources. Here we used an x-ray free-electron laser (XFEL) with individual 50-fs duration x-ray pulses to minimize radiation damage and obtained a high-resolution room temperature structure of a human serotonin receptor using sub-10 µm microcrystals grown in a membrane mimetic matrix known as lipidic cubic phase. Compared to the structure solved by traditional microcrystallography from cryo-cooled crystals of about two orders of magnitude larger volume, the room temperature XFEL structure displays a distinct distribution of thermal motions and conformations of residues that likely more accurately represent the receptor structure and dynamics in a cellular environment. PMID:24357322

  16. G protein-coupled receptors and the regulation of autophagy

    PubMed Central

    Wauson, Eric M.; Dbouk, Hashem A.; Ghosh, Anwesha B.; Cobb, Melanie H.

    2014-01-01

    Autophagy is an important catabolic cellular process that eliminates damaged and unnecessary cytoplasmic proteins and organelles. Basal autophagy occurs during normal physiological conditions, but the activity of this process can be significantly altered in human diseases. Thus, defining the regulatory inputs and signals that control autophagy is essential. Nutrients are key modulators of autophagy. While autophagy is generally accepted to be regulated in a cell autonomous fashion, recent studies suggest nutrients can modulate autophagy in a systemic manner by inducing the secretion of hormones and neurotransmitters that regulate G protein-coupled receptors (GPCRs). Emerging studies show that GPCRs also regulate autophagy by directly detecting extracellular nutrients. We review the role of GPCRs in autophagy regulation, highlighting their potential as therapeutic drug targets. PMID:24751357

  17. NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration

    PubMed Central

    Xia, Jianguo; Benner, Maia J.; Hancock, Robert E. W.

    2014-01-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  18. Directed disassembly of an interfacial rubisco protein network.

    PubMed

    Onaizi, Sagheer A; Malcolm, Andrew S; He, Lizhong; Middelberg, Anton P J

    2007-05-22

    We present the first study of the directed disassembly of a protein network at the air-water interface by the synergistic action of a surfactant and an enzyme. We seek to understand the fundamentals of protein network disassembly by using rubisco adsorbed at the air-water interface as a model. We propose that rubisco adsorption at the air-water interface results in the formation of a fishnet-like network of interconnected protein molecules, capable of transmitting lateral force. The mechanical properties of the rubisco network during assembly and disassembly at the air-water interface were characterized by direct measurement of laterally transmitted force through the protein network using the Cambridge interfacial tensiometer. We have shown that, when used individually, either 2 ppm of the surfactant, sodium dodecyl benzyl sulfonate (SDOBS), or 2 ppm of the enzyme, subtilisin A (SA), were insufficient to completely disassemble the rubisco network within 1 h of treatment. However, a combination of 2 ppm SDOBS and 2 ppm SA led to almost complete disassembly within 1 h. Increasing the concentration of SA in the mixture from 2 to 10 ppm, while keeping the SDOBS concentration constant, significantly decreased the time required to completely disassemble the rubisco network. Furthermore, the initial rate of network disassembly using formulations containing SDOBS was surprisingly insensitive to this increase in SA concentration. This study gives insight into the role of lateral interactions between protein molecules at interfaces in stabilizing interfacial protein networks and shows that surfactant and enzyme working in combination proves more effective at disrupting and mobilizing the interfacial protein network than the action of either agent alone. PMID:17447802

  19. Cluster synchronization of starlike networks with normalized Laplacian coupling: master stability function approach

    NASA Astrophysics Data System (ADS)

    Kuptsov, Pavel V.; Kuptsova, Anna V.

    2016-04-01

    A generalized model of star-like network is suggested that takes into account non-additive coupling and nonlinear transformation of coupling variables. For this model a method of analysis of synchronized cluster stability is developed. Using this method three star-like networks based on Ikeda, predator-prey and Hénon maps are studied.

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

    PubMed

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

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically i ntegrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet. PMID:26800544

  1. Alpha-Bulges in G Protein-Coupled Receptors

    PubMed Central

    van der Kant, Rob; Vriend, Gert

    2014-01-01

    Agonist binding is related to a series of motions in G protein-coupled receptors (GPCRs) that result in the separation of transmembrane helices III and VI at their cytosolic ends and subsequent G protein binding. A large number of smaller motions also seem to be associated with activation. Most helices in GPCRs are highly irregular and often contain kinks, with extensive literature already available about the role of prolines in kink formation and the precise function of these kinks. GPCR transmembrane helices also contain many α-bulges. In this article we aim to draw attention to the role of these α-bulges in ligand and G-protein binding, as well as their role in several aspects of the mobility associated with GPCR activation. This mobility includes regularization and translation of helix III in the extracellular direction, a rotation of the entire helix VI, an inward movement of the helices near the extracellular side, and a concerted motion of the cytosolic ends of the helices that makes their orientation appear more circular and that opens up space for the G protein to bind. In several cases, α-bulges either appear or disappear as part of the activation process. PMID:24806342

  2. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Such networks are typically constructed using high throughput techniques (e.g., Yeast-2-Hybrid experiments). Of particular interest are hub proteins that can interact wit...

  3. Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks

    PubMed Central

    Shen, Ru; Wang, Xiaosheng; Guda, Chittibabu

    2015-01-01

    Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks. PMID:26495282

  4. Distinct Lysosomal Network Protein Profiles in Parkinsonian Syndrome Cerebrospinal Fluid

    PubMed Central

    Boman, Andrea; Svensson, Samuel; Boxer, Adam; Rojas, Julio C.; Seeley, William W.; Karydas, Anna; Miller, Bruce; Kågedal, Katarina; Svenningsson, Per

    2016-01-01

    Background: Clinical diagnosis of parkinsonian syndromes like Parkinson’s disease (PD), corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP) is hampered by overlapping symptomatology and lack of diagnostic biomarkers, and definitive diagnosis is only possible post-mortem. Objective: Since impaired protein degradation plays an important role in many neurodegenerative disorders, we hypothesized that profiles of select lysosomal network proteins in cerebrospinal fluid could be differentially expressed in these parkinsonian syndromes. Methods: Cerebrospinal fluid samples were collected from PD patients (n = 18), clinically diagnosed 4-repeat tauopathy patients; corticobasal syndrome (CBS) (n = 3) and PSP (n = 8); and pathologically diagnosed PSP (n = 8) and CBD patients (n = 7). Each patient set was compared to its appropriate control group consisting of age and gender matched individuals. Select lysosomal network protein levels were detected via Western blotting. Factor analysis was used to test the diagnostic sensitivity, specificity and accuracy of the select lysosomal network protein expression profiles. Results: PD, CBD and PSP were markedly different in their cerebrospinal fluid lysosomal network protein profiles. Lysosomal-associated membrane proteins 1 and 2 were significantly decreased in PD; early endosomal antigen 1 was decreased and lysozyme increased in PSP; and lysosomal-associated membrane proteins 1 and 2, microtubule-associated protein 1 light chain 3 and lysozyme were increased in CBD. A panel of lysosomal-associated membrane protein 2, lysozyme and microtubule-associated protein 1 light chain discriminated between controls, PD and 4-repeat tauopathies. Conclusions: This study offers proof of concept that select lysosomal network proteins are differentially expressed in cerebrospinal fluid of Parkinson’s disease, corticobasal syndrome and progressive supranuclear palsy. Lysosomal network protein analysis

  5. Inhibition of receptor/G protein coupling by suramin analogues.

    PubMed

    Beindl, W; Mitterauer, T; Hohenegger, M; Ijzerman, A P; Nanoff, C; Freissmuth, M

    1996-08-01

    Suramin analogues act as direct antagonists of heterotrimeric G proteins because they block the rate-limiting step of G protein activation (i.e., the dissociation of GDP prebound to the G protein alpha subunit). We have used the human brain A1 adenosine receptor and the rat striatal D2 dopamine receptor, two prototypical Gi/G(o)-coupled receptors, as a model system to test whether the following analogues suppress the receptor-dependent activation of G proteins: 8-(3-nitrobenzamido)-1,3,5-naphthalenetrisulfonic acid (NF007), 8-(3-(3-nitrobenzamido)-benzamido)-1,3,5-naphthalenetrisulfonic acid (NF018); 8,8'-(carbonylbis(imino-3,1-phenylene))bis-(1,3,5-naphthalenetr isulfonic acid) (NF023); 8,8'-(carbonylbis(imino-3,1-phenylene)carbonylimino-(3,1- phenylene)) bis(1,3,5-naphthalenetrisulfonic acid) (NF037); and suramin. Suramin and its analogues inhibit the formation of the agonist-specific ternary complex (agonist/receptor/G protein). This inhibition is (i) quasicompetitive with respect to agonist binding in that it can be overcome by increasing receptor occupancy but (ii) does not result from an interaction of the analogues with the ligand binding pocket of the receptors because the binding of antagonists or of agonists in the absence of functional receptor/G protein interaction is not affected. In addition to suppressing the spontaneous release of GDP from defined G protein alpha subunits, suramin and its analogues reduce receptor-catalyzed guanine nucleotide exchange. The site, to which suramin analogues bind, overlaps with the docking site for the receptor on the G protein alpha subunit. The structure-activity relationships for inhibition of agonist binding to the A1 adenosine receptor (suramin > NF037 > NF023) and of agonist binding to the inhibition D2 dopamine receptor (suramin = NF037 > NF023 > NF018) differ. Thus, NF037 discriminates between the ternary complexes formed by the agonist-liganded D2 dopamine receptors and those formed by the A1 adenosine

  6. Model Organisms in G Protein-Coupled Receptor Research.

    PubMed

    Langenhan, Tobias; Barr, Maureen M; Bruchas, Michael R; Ewer, John; Griffith, Leslie C; Maiellaro, Isabella; Taghert, Paul H; White, Benjamin H; Monk, Kelly R

    2015-09-01

    The study of G protein-coupled receptors (GPCRs) has benefited greatly from experimental approaches that interrogate their functions in controlled, artificial environments. Working in vitro, GPCR receptorologists discovered the basic biologic mechanisms by which GPCRs operate, including their eponymous capacity to couple to G proteins; their molecular makeup, including the famed serpentine transmembrane unit; and ultimately, their three-dimensional structure. Although the insights gained from working outside the native environments of GPCRs have allowed for the collection of low-noise data, such approaches cannot directly address a receptor's native (in vivo) functions. An in vivo approach can complement the rigor of in vitro approaches: as studied in model organisms, it imposes physiologic constraints on receptor action and thus allows investigators to deduce the most salient features of receptor function. Here, we briefly discuss specific examples in which model organisms have successfully contributed to the elucidation of signals controlled through GPCRs and other surface receptor systems. We list recent examples that have served either in the initial discovery of GPCR signaling concepts or in their fuller definition. Furthermore, we selectively highlight experimental advantages, shortcomings, and tools of each model organism. PMID:25979002

  7. GPCRDB: an information system for G protein-coupled receptors.

    PubMed

    Isberg, Vignir; Vroling, Bas; van der Kant, Rob; Li, Kang; Vriend, Gert; Gloriam, David

    2014-01-01

    For the past 20 years, the GPCRDB (G protein-coupled receptors database; http://www.gpcr.org/7tm/) has been a 'one-stop shop' for G protein-coupled receptor (GPCR)-related data. The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data, such as multiple sequence alignments and homology models. The GPCRDB also provides visualization and analysis tools, plus a number of query systems. In the latest GPCRDB release, all multiple sequence alignments, and >65,000 homology models, have been significantly improved, thanks to a recent flurry of GPCR X-ray structure data. Tools were introduced to browse X-ray structures, compare binding sites, profile similar receptors and generate amino acid conservation statistics. Snake plots and helix box diagrams can now be custom coloured (e.g. by chemical properties or mutation data) and saved as figures. A series of sequence alignment visualization tools has been added, and sequence alignments can now be created for subsets of sequences and sequence positions, and alignment statistics can be produced for any of these subsets. PMID:24304901

  8. Covalent agonists for studying G protein-coupled receptor activation

    PubMed Central

    Weichert, Dietmar; Kruse, Andrew C.; Manglik, Aashish; Hiller, Christine; Zhang, Cheng; Hübner, Harald; Kobilka, Brian K.; Gmeiner, Peter

    2014-01-01

    Structural studies on G protein-coupled receptors (GPCRs) provide important insights into the architecture and function of these important drug targets. However, the crystallization of GPCRs in active states is particularly challenging, requiring the formation of stable and conformationally homogeneous ligand-receptor complexes. Native hormones, neurotransmitters, and synthetic agonists that bind with low affinity are ineffective at stabilizing an active state for crystallogenesis. To promote structural studies on the pharmacologically highly relevant class of aminergic GPCRs, we here present the development of covalently binding molecular tools activating Gs-, Gi-, and Gq-coupled receptors. The covalent agonists are derived from the monoamine neurotransmitters noradrenaline, dopamine, serotonin, and histamine, and they were accessed using a general and versatile synthetic strategy. We demonstrate that the tool compounds presented herein display an efficient covalent binding mode and that the respective covalent ligand-receptor complexes activate G proteins comparable to the natural neurotransmitters. A crystal structure of the β2-adrenoreceptor in complex with a covalent noradrenaline analog and a conformationally selective antibody (nanobody) verified that these agonists can be used to facilitate crystallogenesis. PMID:25006259

  9. G protein-coupled receptors as promising cancer targets.

    PubMed

    Liu, Ying; An, Su; Ward, Richard; Yang, Yang; Guo, Xiao-Xi; Li, Wei; Xu, Tian-Rui

    2016-07-01

    G protein-coupled receptors (GPCRs) regulate an array of fundamental biological processes, such as growth, metabolism and homeostasis. Specifically, GPCRs are involved in cancer initiation and progression. However, compared with the involvement of the epidermal growth factor receptor in cancer, that of GPCRs have been largely ignored. Recent findings have implicated many GPCRs in tumorigenesis, tumor progression, invasion and metastasis. Moreover, GPCRs contribute to the establishment and maintenance of a microenvironment which is permissive for tumor formation and growth, including effects upon surrounding blood vessels, signaling molecules and the extracellular matrix. Thus, GPCRs are considered to be among the most useful drug targets against many solid cancers. Development of selective ligands targeting GPCRs may provide novel and effective treatment strategies against cancer and some anticancer compounds are now in clinical trials. Here, we focus on tumor related GPCRs, such as G protein-coupled receptor 30, the lysophosphatidic acid receptor, angiotensin receptors 1 and 2, the sphingosine 1-phosphate receptors and gastrin releasing peptide receptor. We also summarize their tissue distributions, activation and roles in tumorigenesis and discuss the potential use of GPCR agonists and antagonists in cancer therapy. PMID:27000991

  10. Exponential Synchronization of Coupled Stochastic Memristor-Based Neural Networks With Time-Varying Probabilistic Delay Coupling and Impulsive Delay.

    PubMed

    Bao, Haibo; Park, Ju H; Cao, Jinde

    2016-01-01

    This paper deals with the exponential synchronization of coupled stochastic memristor-based neural networks with probabilistic time-varying delay coupling and time-varying impulsive delay. There is one probabilistic transmittal delay in the delayed coupling that is translated by a Bernoulli stochastic variable satisfying a conditional probability distribution. The disturbance is described by a Wiener process. Based on Lyapunov functions, Halanay inequality, and linear matrix inequalities, sufficient conditions that depend on the probability distribution of the delay coupling and the impulsive delay were obtained. Numerical simulations are used to show the effectiveness of the theoretical results. PMID:26485723

  11. Domain-mediated protein interaction prediction: From genome to network.

    PubMed

    Reimand, Jüri; Hui, Shirley; Jain, Shobhit; Law, Brian; Bader, Gary D

    2012-08-14

    Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease. PMID:22561014

  12. Fluctuations in Mass-Action Equilibrium of Protein Binding Networks

    NASA Astrophysics Data System (ADS)

    Yan, Koon-Kiu; Walker, Dylan; Maslov, Sergei

    2008-12-01

    We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by slow changes in total concentrations of interacting proteins. The second type (spontaneous) is caused by quickly decaying thermodynamic deviations away from equilibrium. We investigate the effects of network connectivity on fluctuations by comparing them to scenarios in which the interacting pair is isolated from the network and analytically derives bounds on fluctuations. Collective effects are shown to sometimes lead to large amplification of spontaneous fluctuations. The strength of both types of fluctuations is positively correlated with the complex connectivity and negatively correlated with complex concentration. Our general findings are illustrated using a curated network of protein interactions and multiprotein complexes in baker’s yeast, with empirical protein concentrations.

  13. Partial Synchronization in Pulse-Coupled Oscillator Networks I: Theory

    NASA Astrophysics Data System (ADS)

    Engelbrecht, Jan; Chen, Bolun; Mirollo, Renato

    We study N identical integrate and fire model neurons coupled in an all to all network through α-function pulses, weighted by a parameter K. Studies of the dynamics of this system often focus on the stability of the fully synchronous and the fully asynchronous splay states, that naturally depend on the sign of K, i.e. excitation vs inhibition. We find that for finite N there is a rich set of other partially synchronized attractors, such as (N - 1 , 1) fixed states and partially synchronized splay states. Our framework exploits the neutrality of the dynamics for K = 0 which allows us to implement a dimensional reduction strategy that replaces the discrete pulses with a continuous flow, with the sign of K determining the flow direction. This framework naturally incorporates a hierarchy of partially synchronized subspaces in which the new states lie. For N = 2 , 3 , 4 , we completely describe the sequence of bifurcations and the stability of all fixed points and limit cycles. Work Supported by NSF DMS 1413020.

  14. Image fusion by pulse couple neural network with shearlet

    NASA Astrophysics Data System (ADS)

    Geng, Peng; Wang, Zhengyou; Zhang, Zhigang; Xiao, Zhong

    2012-06-01

    The shearlet representation forms a tight frame which decomposes a function into scales and directions, and is optimally sparse in representing images with edges. An image fusion method is proposed based on the shearlet transform. Firstly, transform the image A and image B by the shearlets. Secondly, a pulse couple neural network (PCNN) is used for the frequency subbands, which uses the number of output pulses from the PCNN's neurons to select fusion coefficients. Finally, an inverse shearlet transform is applied on the new fused coefficients to reconstruct the fused image. Some experiments are performed in images such as multi-focus images, multi-sensor images, medical images and multispectral images comparing the proposed algorithm with the wavelet, contourlet and nonsubsampled contourlet method based on the PCNN. The experimental results show that the proposed algorithm can not only extract more important visual information from source images, but also effectively avoid the introduction of artificial information. It significantly outperforms the traditional multiscale transform image fusion methods in terms of both visual quality and objective evaluation criteria such as MI and QAB/F.

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

    PubMed

    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

  16. 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

  17. Synchronization and spatiotemporal patterns in coupled phase oscillators on a weighted planar network

    NASA Astrophysics Data System (ADS)

    Kagawa, Yuki; Takamatsu, Atsuko

    2009-04-01

    To reveal the relation between network structures found in two-dimensional biological systems, such as protoplasmic tube networks in the plasmodium of true slime mold, and spatiotemporal oscillation patterns emerged on the networks, we constructed coupled phase oscillators on weighted planar networks and investigated their dynamics. Results showed that the distribution of edge weights in the networks strongly affects (i) the propensity for global synchronization and (ii) emerging ratios of oscillation patterns, such as traveling and concentric waves, even if the total weight is fixed. In-phase locking, traveling wave, and concentric wave patterns were, respectively, observed most frequently in uniformly weighted, center weighted treelike, and periphery weighted ring-shaped networks. Controlling the global spatiotemporal patterns with the weight distribution given by the local weighting (coupling) rules might be useful in biological network systems including the plasmodial networks and neural networks in the brain.

  18. Human G protein-coupled receptor studies in Saccharomyces cerevisiae.

    PubMed

    Liu, Rongfang; Wong, Winsy; IJzerman, Adriaan P

    2016-08-15

    G protein-coupled receptors (GPCRs) are one of the largest families of membrane proteins, with approximately 800 different GPCRs in the human genome. Signaling via GPCRs regulates many biological processes, such as cell proliferation, differentiation, and development. In addition, many receptors have a pivotal role in immunophysiology. Many hormones and neurotransmitters are ligands for these receptors, and hence it is not surprising that many drugs, either mimicking or blocking the action of the bodily substances, have been developed. It is estimated that 30-40% of current drugs on the market target GPCRs. Further identifying and elucidating the functions of GPCRs will provide opportunities for novel drug discovery, including for immunotherapy. The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a very important and useful platform in this respect. There are many advantages of using a yeast assay system, as it is cheap, safe and stable; it is also convenient for rapid feasibility and optimization studies. Moreover, it offers a "null" background when studying human GPCRs. New developments regarding human GPCRs expressed in a yeast platform are providing insight into GPCR activation and signaling, and facilitate agonist and antagonist identification. In this review we summarize the latest findings regarding human G-protein-coupled receptors in studies using S. cerevisiae, ever since the year 2005 when we last published a review on this topic. We describe 11 families of GPCRs in detail, while including the principles and developments of each yeast system applied to these different GPCRs and highlight and generalize the experimental findings of GPCR function in these systems. PMID:26920251

  19. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment. PMID:23889048

  20. Design of inner coupling matrix for robustly self-synchronizing networks

    NASA Astrophysics Data System (ADS)

    Gequn, Liu; Zhiguo, Zhan; Knowles, Gareth

    2015-12-01

    A self-synchronizing network may undergo change of scale and topology during its functioning, thus adjustment of parameters is necessary to enable the synchronization. The adjustment cost and runtime-break demand a method to maintain continuous operation of the network. To address these issues, this paper presents an analytical method for the design of the inner coupling matrix. The proposed method renders the synchronization robust to change of network scale and topology. It is usual in network models that scale and topology are represented by outer coupling matrix. In this paper we only consider diffusively coupled networks. For these networks, the eigenvalues of the outer coupling matrix are all non-positive. By utilizing this property, the designed inner coupling matrix can cover the entire left half of complex plane within the synchronized region to underlie robustness of synchronization. After elaborating the applicability of several types of synchronization state for a robustly self-synchronizing network, the analytical design method is given for the stable equilibrium point case. Sometimes the Jacobian matrix of the node dynamical equation may lead to an unrealizable complex inner coupling matrix in the method. We then introduce a lemma of matrix transformation to prevent this possibility. Additionally, we investigated the choice of inner coupling matrix to get a desirable self-synchronization speed. The corresponding condition in the design procedure is given to drive the network synchronization faster than convergence of each node. Finally, the article includes examples that show effectiveness and soundness of the method.

  1. Pattern Discovery in Breast Cancer Specific Protein Interaction Network

    PubMed Central

    Wu, Xiaogang; Harrison, Scott H.; Chen, Jake Yue

    2009-01-01

    The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein’s biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of “ant colonies” on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network. PMID:21347162

  2. Structural and Supportive Changes in Couples' Family and Friendship Networks across the Transition to Parenthood.

    ERIC Educational Resources Information Center

    Bost, Kelly K.; Cox, Martha J.; Burchinal, Margaret R.; Payne, Chris

    2002-01-01

    Examines patterns of change in family and friend network with parenthood in 137 couples surveyed before the birth of their first child. Husbands and wives who reported larger network sizes and support prior to their first child's birth were more likely to report larger networks after birth. Changes in parents' social systems were related to…

  3. Minireview: Nutrient Sensing by G Protein-Coupled Receptors

    PubMed Central

    Wauson, Eric M.; Lorente-Rodríguez, Andrés

    2013-01-01

    G protein-coupled receptors (GPCRs) are membrane proteins that recognize molecules in the extracellular milieu and transmit signals inside cells to regulate their behaviors. Ligands for many GPCRs are hormones or neurotransmitters that direct coordinated, stereotyped adaptive responses. Ligands for other GPCRs provide information to cells about the extracellular environment. Such information facilitates context-specific decision making that may be cell autonomous. Among ligands that are important for cellular decisions are amino acids, required for continued protein synthesis, as metabolic starting materials and energy sources. Amino acids are detected by a number of class C GPCRs. One cluster of amino acid-sensing class C GPCRs includes umami and sweet taste receptors, GPRC6A, and the calcium-sensing receptor. We have recently found that the umami taste receptor heterodimer T1R1/T1R3 is a sensor of amino acid availability that regulates the activity of the mammalian target of rapamycin. This review focuses on an array of findings on sensing amino acids and sweet molecules outside of neurons by this cluster of class C GPCRs and some of the physiologic processes regulated by them. PMID:23820899

  4. G-Protein Coupled Receptors: Surface Display and Biosensor Technology

    NASA Astrophysics Data System (ADS)

    McMurchie, Edward; Leifert, Wayne

    Signal transduction by G-protein coupled receptors (GPCRs) underpins a multitude of physiological processes. Ligand recognition by the receptor leads to the activation of a generic molecular switch involving heterotrimeric G-proteins and guanine nucleotides. With growing interest and commercial investment in GPCRs in areas such as drug targets, orphan receptors, high-throughput screening of drugs and biosensors, greater attention will focus on assay development to allow for miniaturization, ultrahigh-throughput and, eventually, microarray/biochip assay formats that will require nanotechnology-based approaches. Stable, robust, cell-free signaling assemblies comprising receptor and appropriate molecular switching components will form the basis of future GPCR/G-protein platforms, which should be able to be adapted to such applications as microarrays and biosensors. This chapter focuses on cell-free GPCR assay nanotechnologies and describes some molecular biological approaches for the construction of more sophisticated, surface-immobilized, homogeneous, functional GPCR sensors. The latter points should greatly extend the range of applications to which technologies based on GPCRs could be applied.

  5. 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.

  6. Conformational Fluctuations in G-Protein-Coupled Receptors

    NASA Astrophysics Data System (ADS)

    Brown, Michael F.

    2014-03-01

    G-protein-coupled receptors (GPCRs) comprise almost 50% of pharmaceutical drug targets, where rhodopsin is an important prototype and occurs naturally in a lipid membrane. Rhodopsin photoactivation entails 11-cis to all-trans isomerization of the retinal cofactor, yielding an equilibrium between inactive Meta-I and active Meta-II states. Two important questions are: (1) Is rhodopsin is a simple two-state switch? Or (2) does isomerization of retinal unlock an activated conformational ensemble? For an ensemble-based activation mechanism (EAM) a role for conformational fluctuations is clearly indicated. Solid-state NMR data together with theoretical molecular dynamics (MD) simulations detect increased local mobility of retinal after light activation. Resultant changes in local dynamics of the cofactor initiate large-scale fluctuations of transmembrane helices that expose recognition sites for the signal-transducing G-protein. Time-resolved FTIR studies and electronic spectroscopy further show the conformational ensemble is strongly biased by the membrane lipid composition, as well as pH and osmotic pressure. A new flexible surface model (FSM) describes how the curvature stress field of the membrane governs the energetics of active rhodopsin, due to the spontaneous monolayer curvature of the lipids. Furthermore, influences of osmotic pressure dictate that a large number of bulk water molecules are implicated in rhodopsin activation. Around 60 bulk water molecules activate rhodopsin, which is much larger than the number of structural waters seen in X-ray crystallography, or inferred from studies of bulk hydrostatic pressure. Conformational selection and promoting vibrational motions of rhodopsin lead to activation of the G-protein (transducin). Our biophysical data give a paradigm shift in understanding GPCR activation. The new view is: dynamics and conformational fluctuations involve an ensemble of substates that activate the cognate G-protein in the amplified visual

  7. Programming Molecular Association and Viscoelastic Behavior in Protein Networks.

    PubMed

    Dooling, Lawrence J; Buck, Maren E; Zhang, Wen-Bin; Tirrell, David A

    2016-06-01

    A set of recombinant artificial proteins that can be cross-linked, by either covalent bonds or association of helical domains or both, is described. The designed proteins can be used to construct molecular networks in which the mechanism of crosslinking determines the time-dependent responses to mechanical deformation. PMID:27061171

  8. Latest development in drug discovery on G protein-coupled receptors.

    PubMed

    Lundstrom, Kenneth

    2006-10-01

    G protein-coupled receptors (GPCRs) represent the family of proteins with the highest impact from social, therapeutic and economic point of view. Today, more than 50% of drug targets are based on GPCRs and the annual worldwide sales exceeds 50 billion dollars. GPCRs are involved in all major disease areas such as cardiovascular, metabolic, neurodegenerative, psychiatric, cancer and infectious diseases. The classical drug discovery process has relied on screening compounds, which interact favorably with the GPCR of interest followed by further chemical engineering as a mean of improving efficacy and selectivity. In this review, methods for sophisticated chemical library screening procedures will be presented. Furthermore, development of cell-based assays for functional coupling of GPCRs to G proteins will be discussed. Finally, the possibility of applying structure-based drug design will be summarized. This includes the application of bioinformatics knowledge and molecular modeling approaches in drug development programs. The major efforts established through large networks of structural genomics on GPCRs, where recombinantly expressed GPCRs are subjected to purification and crystallization attempts with the intention of obtaining high-resolution structures, are presented as a promising future approach for tailor-made drug development. PMID:17073697

  9. GPCRdb: an information system for G protein-coupled receptors

    PubMed Central

    Isberg, Vignir; Mordalski, Stefan; Munk, Christian; Rataj, Krzysztof; Harpsøe, Kasper; Hauser, Alexander S.; Vroling, Bas; Bojarski, Andrzej J.; Vriend, Gert; Gloriam, David E.

    2016-01-01

    Recent developments in G protein-coupled receptor (GPCR) structural biology and pharmacology have greatly enhanced our knowledge of receptor structure-function relations, and have helped improve the scientific foundation for drug design studies. The GPCR database, GPCRdb, serves a dual role in disseminating and enabling new scientific developments by providing reference data, analysis tools and interactive diagrams. This paper highlights new features in the fifth major GPCRdb release: (i) GPCR crystal structure browsing, superposition and display of ligand interactions; (ii) direct deposition by users of point mutations and their effects on ligand binding; (iii) refined snake and helix box residue diagram looks; and (iii) phylogenetic trees with receptor classification colour schemes. Under the hood, the entire GPCRdb front- and back-ends have been re-coded within one infrastructure, ensuring a smooth browsing experience and development. GPCRdb is available at http://www.gpcrdb.org/ and it's open source code at https://bitbucket.org/gpcr/protwis. PMID:26582914

  10. Crystal Structure of a Lipid G Protein-Coupled Receptor

    SciTech Connect

    Hanson, Michael A; Roth, Christopher B; Jo, Euijung; Griffith, Mark T; Scott, Fiona L; Reinhart, Greg; Desale, Hans; Clemons, Bryan; Cahalan, Stuart M; Schuerer, Stephan C; Sanna, M Germana; Han, Gye Won; Kuhn, Peter; Rosen, Hugh; Stevens, Raymond C

    2012-03-01

    The lyso-phospholipid sphingosine 1-phosphate modulates lymphocyte trafficking, endothelial development and integrity, heart rate, and vascular tone and maturation by activating G protein-coupled sphingosine 1-phosphate receptors. Here, we present the crystal structure of the sphingosine 1-phosphate receptor 1 fused to T4-lysozyme (S1P1-T4L) in complex with an antagonist sphingolipid mimic. Extracellular access to the binding pocket is occluded by the amino terminus and extracellular loops of the receptor. Access is gained by ligands entering laterally between helices I and VII within the transmembrane region of the receptor. This structure, along with mutagenesis, agonist structure-activity relationship data, and modeling, provides a detailed view of the molecular recognition and requirement for hydrophobic volume that activates S1P1, resulting in the modulation of immune and stromal cell responses.