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Sample records for protein network coupled

  1. Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins.

    PubMed

    Soltan Ghoraie, Laleh; Burkowski, Forbes; Zhu, Mu

    2015-03-01

    Recent studies have highlighted the role of coupled side-chain fluctuations alone in the allosteric behavior of proteins. Moreover, examination of X-ray crystallography data has recently revealed new information about the prevalence of alternate side-chain conformations (conformational polymorphism), and attempts have been made to uncover the hidden alternate conformations from X-ray data. Hence, new computational approaches are required that consider the polymorphic nature of the side chains, and incorporate the effects of this phenomenon in the study of information transmission and functional interactions of residues in a molecule. These studies can provide a more accurate understanding of the allosteric behavior. In this article, we first present a novel approach to generate an ensemble of conformations and an efficient computational method to extract direct couplings of side chains in allosteric proteins, and provide sparse network representations of the couplings. We take the side-chain conformational polymorphism into account, and show that by studying the intrinsic dynamics of an inactive structure, we are able to construct a network of functionally crucial residues. Second, we show that the proposed method is capable of providing a magnified view of the coupled and conformationally polymorphic residues. This model reveals couplings between the alternate conformations of a coupled residue pair. To the best of our knowledge, this is the first computational method for extracting networks of side chains' alternate conformations. Such networks help in providing a detailed image of side-chain dynamics in functionally important and conformationally polymorphic sites, such as binding and/or allosteric sites. © 2014 Wiley Periodicals, Inc.

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

    PubMed

    Isom, Daniel G; Dohlman, Henrik G

    2015-05-05

    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.

  4. 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

  5. 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

  6. The G protein-coupled receptor heterodimer network (GPCR-HetNet) and its hub components.

    PubMed

    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

    2014-05-14

    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.

  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. 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.

  9. Dynamic Coupling and Allosteric Networks in the α Subunit of Heterotrimeric G Proteins*

    PubMed Central

    Yao, Xin-Qiu; Malik, Rabia U.; Griggs, Nicholas W.; Skjærven, Lars; Traynor, John R.; Sivaramakrishnan, Sivaraj; Grant, Barry J.

    2016-01-01

    G protein α subunits cycle between active and inactive conformations to regulate a multitude of intracellular signaling cascades. Important structural transitions occurring during this cycle have been characterized from extensive crystallographic studies. However, the link between observed conformations and the allosteric regulation of binding events at distal sites critical for signaling through G proteins remain unclear. Here we describe molecular dynamics simulations, bioinformatics analysis, and experimental mutagenesis that identifies residues involved in mediating the allosteric coupling of receptor, nucleotide, and helical domain interfaces of Gαi. Most notably, we predict and characterize novel allosteric decoupling mutants, which display enhanced helical domain opening, increased rates of nucleotide exchange, and constitutive activity in the absence of receptor activation. Collectively, our results provide a framework for explaining how binding events and mutations can alter internal dynamic couplings critical for G protein function. PMID:26703464

  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. 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.

  12. Coupled oscillators on evolving networks

    NASA Astrophysics Data System (ADS)

    Singh, R. K.; Bagarti, Trilochan

    2016-12-01

    In this work we study coupled oscillators on evolving networks. We find that the steady state behavior of the system is governed by the relative values of the spread in natural frequencies and the global coupling strength. For coupling strong in comparison to the spread in frequencies, the system of oscillators synchronize and when coupling strength and spread in frequencies are large, a phenomenon similar to amplitude death is observed. The network evolution provides a mechanism to build inter-oscillator connections and once a dynamic equilibrium is achieved, oscillators evolve according to their local interactions. We also find that the steady state properties change by the presence of additional time scales. We demonstrate these results based on numerical calculations studying dynamical evolution of limit-cycle and van der Pol oscillators.

  13. xPyder: a PyMOL plugin to analyze coupled residues and their networks in protein structures.

    PubMed

    Pasi, Marco; Tiberti, Matteo; Arrigoni, Alberto; Papaleo, Elena

    2012-07-23

    A versatile method to directly identify and analyze short- or long-range coupled or communicating residues in a protein conformational ensemble is of extreme relevance to achieve a complete understanding of protein dynamics and structural communication routes. Here, we present xPyder, an interface between one of the most employed molecular graphics systems, PyMOL, and the analysis of dynamical cross-correlation matrices (DCCM). The approach can also be extended, in principle, to matrices including other indexes of communication propensity or intensity between protein residues, as well as the persistence of intra- or intermolecular interactions, such as those underlying protein dynamics. The xPyder plugin for PyMOL 1.4 and 1.5 is offered as Open Source software via the GPL v2 license, and it can be found, along with the installation package, the user guide, and examples, at http://linux.btbs.unimib.it/xpyder/.

  14. Bacterial chemoreceptor arrays are hexagonally packed trimers of receptor dimers networked by rings of kinase and coupling proteins

    PubMed Central

    Briegel, Ariane; Li, Xiaoxiao; Bilwes, Alexandrine M.; Hughes, Kelly T.; Jensen, Grant J.; Crane, Brian R.

    2012-01-01

    Chemoreceptor arrays are supramolecular transmembrane machines of unknown structure that allow bacteria to sense their surroundings and respond by chemotaxis. We have combined X-ray crystallography of purified proteins with electron cryotomography of native arrays inside cells to reveal the arrangement of the component transmembrane receptors, histidine kinases (CheA) and CheW coupling proteins. Trimers of receptor dimers lie at the vertices of a hexagonal lattice in a “two-facing-two” configuration surrounding a ring of alternating CheA regulatory domains (P5) and CheW couplers. Whereas the CheA kinase domains (P4) project downward below the ring, the CheA dimerization domains (P3) link neighboring rings to form an extended, stable array. This highly interconnected protein architecture underlies the remarkable sensitivity and cooperative nature of transmembrane signaling in bacterial chemotaxis. PMID:22355139

  15. 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.

  16. 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

    2014-11-07

    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.

  17. A minimal ligand binding pocket within a network of correlated mutations identified by multiple sequence and structural analysis of G protein coupled receptors.

    PubMed

    Moitra, Subhodeep; Tirupula, Kalyan C; Klein-Seetharaman, Judith; Langmead, Christopher James

    2012-06-29

    G protein coupled receptors (GPCRs) are seven helical transmembrane proteins that function as signal transducers. They bind ligands in their extracellular and transmembrane regions and activate cognate G proteins at their intracellular surface at the other side of the membrane. The relay of allosteric communication between the ligand binding site and the distant G protein binding site is poorly understood. In this study, GREMLIN 1, a recently developed method that identifies networks of co-evolving residues from multiple sequence alignments, was used to identify those that may be involved in communicating the activation signal across the membrane. The GREMLIN-predicted long-range interactions between amino acids were analyzed with respect to the seven GPCR structures that have been crystallized at the time this study was undertaken. GREMLIN significantly enriches the edges containing residues that are part of the ligand binding pocket, when compared to a control distribution of edges drawn from a random graph. An analysis of these edges reveals a minimal GPCR binding pocket containing four residues (T1183.33, M2075.42, Y2686.51 and A2927.39). Additionally, of the ten residues predicted to have the most long-range interactions (A1173.32, A2726.55, E1133.28, H2115.46, S186EC2, A2927.39, E1223.37, G902.57, G1143.29 and M2075.42), nine are part of the ligand binding pocket. We demonstrate the use of GREMLIN to reveal a network of statistically correlated and functionally important residues in class A GPCRs. GREMLIN identified that ligand binding pocket residues are extensively correlated with distal residues. An analysis of the GREMLIN edges across multiple structures suggests that there may be a minimal binding pocket common to the seven known GPCRs. Further, the activation of rhodopsin involves these long-range interactions between extracellular and intracellular domain residues mediated by the retinal domain.

  18. 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…

  19. Synchronization between two coupled complex networks.

    PubMed

    Li, Changpin; Sun, Weigang; Kurths, Jürgen

    2007-10-01

    We study synchronization for two unidirectionally coupled networks. This is a substantial generalization of several recent papers investigating synchronization inside a network. We derive analytically a criterion for the synchronization of two networks which have the same (inside) topological connectivity. Then numerical examples are given which fit the theoretical analysis. In addition, numerical calculations for two networks with different topological connections are presented and interesting synchronization and desynchronization alternately appear with increasing value of the coupling strength.

  20. Adaptive synchronization of asymmetric coupled networks with multiple coupling delays

    NASA Astrophysics Data System (ADS)

    Sun, Weiwei; Hao, Fei; Chen, Xia

    2012-05-01

    The synchronization problem of asymmetric networks with multiple coupled delays is investigated in this paper. By using Lyapunov stability theory and Lasalle's invariance principle, several synchronization criteria are deduced for both asymmetric networks with and without norm uncertainties. Furthermore, the synchronization problem of a special complex network with each node being a Lurie system is studied. The main results show that the states of all nodes of networks globally asymptotically synchronize to a desired synchronization state by designing suitable adaptive controllers, and these controllers have strong robustness against the uncertain coupling matrixes. Finally, several illustrative examples with numerical simulations are given to show the feasibility and efficiency of theoretical results.

  1. Synchronization in complex networks with adaptive coupling

    NASA Astrophysics Data System (ADS)

    Zhang, Rong; Hu, Manfeng; Xu, Zhenyuan

    2007-08-01

    Generally it is very difficult to realized synchronization for some complex networks. In order to synchronize, the coupling coefficient of networks has to be very large, especially when the number of coupled nodes is larger. In this Letter, we consider the problem of synchronization in complex networks with adaptive coupling. A new concept about asymptotic stability is presented, then we proved by using the well-known LaSalle invariance principle, that the state of such a complex network can synchronize an arbitrary assigned state of an isolated node of the network as long as the feedback gain is positive. Unified system is simulated as the nodes of adaptive coupling complex networks with different topologies.

  2. A minimal ligand binding pocket within a network of correlated mutations identified by multiple sequence and structural analysis of G protein coupled receptors

    PubMed Central

    2012-01-01

    Background G protein coupled receptors (GPCRs) are seven helical transmembrane proteins that function as signal transducers. They bind ligands in their extracellular and transmembrane regions and activate cognate G proteins at their intracellular surface at the other side of the membrane. The relay of allosteric communication between the ligand binding site and the distant G protein binding site is poorly understood. In this study, GREMLIN [1], a recently developed method that identifies networks of co-evolving residues from multiple sequence alignments, was used to identify those that may be involved in communicating the activation signal across the membrane. The GREMLIN-predicted long-range interactions between amino acids were analyzed with respect to the seven GPCR structures that have been crystallized at the time this study was undertaken. Results GREMLIN significantly enriches the edges containing residues that are part of the ligand binding pocket, when compared to a control distribution of edges drawn from a random graph. An analysis of these edges reveals a minimal GPCR binding pocket containing four residues (T1183.33, M2075.42, Y2686.51 and A2927.39). Additionally, of the ten residues predicted to have the most long-range interactions (A1173.32, A2726.55, E1133.28, H2115.46, S186EC2, A2927.39, E1223.37, G902.57, G1143.29 and M2075.42), nine are part of the ligand binding pocket. Conclusions We demonstrate the use of GREMLIN to reveal a network of statistically correlated and functionally important residues in class A GPCRs. GREMLIN identified that ligand binding pocket residues are extensively correlated with distal residues. An analysis of the GREMLIN edges across multiple structures suggests that there may be a minimal binding pocket common to the seven known GPCRs. Further, the activation of rhodopsin involves these long-range interactions between extracellular and intracellular domain residues mediated by the retinal domain. PMID:22748306

  3. Consensus and transitions in coupled Sznajd networks

    NASA Astrophysics Data System (ADS)

    Ludden, Matthew

    2013-03-01

    In this work we investigate two coupled square lattice networks undergoing Sznajd model dynamics. The coupling between the networks is quantified by a coupling strength p. Monte Carlo simulations indicate that the exit probability of each network (to reach either all spins up or all down) depends on p and the initial density of up spins d in the other network. For fixed initial densities, we find a critical coupling pc, above which no further changes in the exit probability are observed. We also find pc to decrease linearly with increasing d. The consensus time scales with system size as Lα, where α = α(p, d). The conditions that must be met for the two networks to reach consensus are also considered. Thomas E. Stone: Husson University, Susan R. McKay: University of Maine

  4. Object detection using pulse coupled neural networks.

    PubMed

    Ranganath, H S; Kuntimad, G

    1999-01-01

    This paper describes an object detection system based on pulse coupled neural networks. The system is designed and implemented to illustrate the power, flexibility and potential the pulse coupled neural networks have in real-time image processing. In the preprocessing stage, a pulse coupled neural network suppresses noise by smoothing the input image. In the segmentation stage, a second pulse coupled neural-network iteratively segments the input image. During each iteration, with the help of a control module, the segmentation network deletes regions that do not satisfy the retention criteria from further processing and produces an improved segmentation of the retained image. In the final stage each group of connected regions that satisfies the detection criteria is identified as an instance of the object of interest.

  5. Coupling functions in networks of oscillators

    NASA Astrophysics Data System (ADS)

    Stankovski, Tomislav; Ticcinelli, Valentina; McClintock, Peter V. E.; Stefanovska, Aneta

    2015-03-01

    Networks of interacting oscillators abound in nature, and one of the prevailing challenges in science is how to characterize and reconstruct them from measured data. We present a method of reconstruction based on dynamical Bayesian inference that is capable of detecting the effective phase connectivity within networks of time-evolving coupled phase oscillators subject to noise. It not only reconstructs pairwise, but also encompasses couplings of higher degree, including triplets and quadruplets of interacting oscillators. Thus inference of a multivariate network enables one to reconstruct the coupling functions that specify possible causal interactions, together with the functional mechanisms that underlie them. The characteristic features of the method are illustrated by the analysis of a numerically generated example: a network of noisy phase oscillators with time-dependent coupling parameters. To demonstrate its potential, the method is also applied to neuronal coupling functions from single- and multi-channel electroencephalograph recordings. The cross-frequency δ, α to α coupling function, and the θ, α, γ to γ triplet are computed, and their coupling strengths, forms of coupling function, and predominant coupling components, are analysed. The results demonstrate the applicability of the method to multivariate networks of oscillators, quite generally.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. Synchronization in Networks of Coupled Chemical Oscillators

    NASA Astrophysics Data System (ADS)

    Showalter, Kenneth; Tinsley, Mark; Nkomo, Simbarashe; Ke, Hua

    2014-03-01

    We have studied networks of coupled photosensitive chemical oscillators. Experiments and simulations are carried out on networks with different topologies and modes of coupling. We describe experimental and modeling studies of chimera and phase-cluster states and their relation to other synchronization states. Networks of integrate-and-fire oscillators are also studied in which sustained coordinated activity is exhibited. Individual nodes display incoherent firing events; however, a dominant frequency within the collective signal is exhibited. The introduction of spike-timing-dependent plasticity allows the network to evolve and leads to a stable unimodal link-weight distribution. M. R. Tinsley et al., Nature Physics 8, 662 (2012); S. Nkomo et al., Phys. Rev. Lett. 110, 244102 (2013); H. Ke et al., in preparation.

  12. Feedback network with space invariant coupling.

    PubMed

    Häusler, G; Lange, E

    1990-11-10

    Processing images by a neural network means performing a repeated sequence of operations on the images. The sequence consists of a general linear transformation and a nonlinear mapping of pixel intensities. The general (shift variant) linear transformation is time consuming for large images if done with a serial computer. A shift invariant linear transformation can be implemented much easier by fast Fourier transform or optically, but the shift invariant transform has fewer degrees of freedom because the coupling matrix is Toeplitz. We present a neural convolution network with shift invariant coupling that nevertheless exhibits autoassociative restoration of distorted images. Besides the simple implementation, the network has one more advantage: associative recall does not depend on object position.

  13. Enhancing robustness of coupled networks under targeted recoveries.

    PubMed

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

    2015-02-13

    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.

  14. 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

  15. 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)

  16. 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

  17. Astroglial networking contributes to neurometabolic coupling

    PubMed Central

    Escartin, Carole; Rouach, Nathalie

    2013-01-01

    The strategic position of astrocytic processes between blood capillaries and neurons, provided the early insight that astrocytes play a key role in supplying energy substrates to neurons in an activity-dependent manner. The central role of astrocytes in neurometabolic coupling has been first established at the level of single cell. Since then, exciting recent work based on cellular imaging and electrophysiological recordings has provided new mechanistic insights into this phenomenon, revealing the crucial role of gap junction (GJ)-mediated networks of astrocytes. Indeed, astrocytes define the local availability of energy substrates by regulating blood flow. Subsequently, in order to efficiently reach distal neurons, these substrates can be taken up, and distributed through networks of astrocytes connected by GJs, a process modulated by neuronal activity. Astrocytic networks can be morphologically and/or functionally altered in the course of various pathological conditions, raising the intriguing possibility of a direct contribution from these networks to neuronal dysfunction. The present review upgrades the current view of neuroglial metabolic coupling, by including the recently unravelled properties of astroglial metabolic networks and their potential contribution to normal and pathological neuronal activity. PMID:23637659

  18. Detecting synchronization in coupled stochastic ecosystem networks

    NASA Astrophysics Data System (ADS)

    Kouvaris, N.; Provata, A.; Kugiumtzis, D.

    2010-01-01

    Instantaneous phase difference, synchronization index and mutual information are considered in order to detect phase transitions, collective behaviours and synchronization phenomena that emerge for different levels of diffusive and reactive activity in stochastic networks. The network under investigation is a spatial 2D lattice which serves as a substrate for Lotka-Volterra dynamics with 3rd order nonlinearities. Kinetic Monte Carlo simulations demonstrate that the system spontaneously organizes into a number of asynchronous local oscillators, when only nearest neighbour interactions are considered. In contrast, the oscillators can be correlated, phase synchronized and completely synchronized when introducing different interactivity rules (diffusive or reactive) for nearby and distant species. The quantitative measures of synchronization show that long distance diffusion coupling induces phase synchronization after a well defined transition point, while long distance reaction coupling induces smeared phase synchronization.

  19. Coupled Oscillations and Circadian Rhythms in Molecular Replication Networks.

    PubMed

    Wagner, Nathaniel; Alasibi, Samaa; Peacock-Lopez, Enrique; Ashkenasy, Gonen

    2015-01-02

    Living organisms often display rhythmic and oscillatory behavior. We investigate here a challenge in contemporary Systems Chemistry, that is, to construct "bottom-up" molecular networks that display such complex behavior. We first describe oscillations during self-replication by applying kinetic parameters relevant to peptide replication in an open environment. Small networks of coupled oscillators are then constructed in silico, producing various functions such as logic gates, integrators, counters, triggers, and detectors. These networks are finally utilized to simulate the connectivity and network topology of the Kai proteins circadian clocks from the S. elongatus cyanobacteria, thus producing rhythms whose constant frequency is independent of the input intake rate and robust toward concentration fluctuations. We suggest that this study helps further reveal the underlying principles of biological clocks and may provide clues into their emergence in early molecular evolution.

  20. Identification of Topological Network Modules in Perturbed Protein Interaction Networks.

    PubMed

    Sardiu, Mihaela E; Gilmore, Joshua M; Groppe, Brad; Florens, Laurence; Washburn, Michael P

    2017-03-08

    Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.

  1. Identification of Topological Network Modules in Perturbed Protein Interaction Networks

    PubMed Central

    Sardiu, Mihaela E.; Gilmore, Joshua M.; Groppe, Brad; Florens, Laurence; Washburn, Michael P.

    2017-01-01

    Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks. PMID:28272416

  2. A Network of Mitogen-Activated Protein Kinases Links G Protein-Coupled Receptors to the c-jun Promoter: a Role for c-Jun NH2-Terminal Kinase, p38s, and Extracellular Signal-Regulated Kinase 5

    PubMed Central

    Marinissen, Maria Julia; Chiariello, Mario; Pallante, Michael; Gutkind, J. Silvio

    1999-01-01

    The expression of the c-jun proto-oncogene is rapidly induced in response to mitogens acting on a large variety of cell surface receptors. The resulting functional activity of c-Jun proteins appears to be critical for cell proliferation. Recently, we have shown that a large family of G protein-coupled receptors (GPCRs), represented by the m1 muscarinic receptor, can initiate intracellular signaling cascades that result in the activation of mitogen-activated protein kinases (MAPK) and c-Jun NH2-terminal kinases (JNK) and that the activation of JNK but not of MAPK correlated with a remarkable increase in the expression of c-jun mRNA. Subsequently, however, we obtained evidence that GPCRs can potently stimulate the activity of the c-jun promoter through MEF2 transcription factors, which do not act downstream from JNK. In view of these observations, we set out to investigate further the nature of the signaling pathway linking GPCRs to the c-jun promoter. Utilizing NIH 3T3 cells, we found that GPCRs can activate the c-jun promoter in a JNK-independent manner. Additionally, we demonstrated that these GPCRs can elevate the activity of novel members of the MAPK family, including ERK5, p38α, p38γ, and p38δ, and that the activation of certain kinases acting downstream from MEK5 (ERK5) and MKK6 (p38α and p38γ) is necessary to fully activate the c-jun promoter. Moreover, in addition to JNK, ERK5, p38α, and p38γ were found to stimulate the c-jun promoter by acting on distinct responsive elements. Taken together, these results suggest that the pathway linking GPCRs to the c-jun promoter involves the integration of numerous signals transduced by a highly complex network of MAPK, rather than resulting from the stimulation of a single linear protein kinase cascade. Furthermore, our findings suggest that each signaling pathway affects one or more regulatory elements on the c-jun promoter and that the transcriptional response most likely results from the temporal integration

  3. Quantum Strong Coupling with Protein Vibrational Modes.

    PubMed

    Vergauwe, Robrecht M A; George, Jino; Chervy, Thibault; Hutchison, James A; Shalabney, Atef; Torbeev, Vladimir Y; Ebbesen, Thomas W

    2016-10-07

    In quantum electrodynamics, matter can be hybridized to confined optical fields by a process known as light-matter strong coupling. This gives rise to new hybrid light-matter states and energy levels in the coupled material, leading to modified physical and chemical properties. Here, we report for the first time the strong coupling of vibrational modes of proteins with the vacuum field of a Fabry-Perot mid-infrared cavity. For two model systems, poly(l-glutamic acid) and bovine serum albumin, strong coupling is confirmed by the anticrossing in the dispersion curve, the square root dependence on the concentration, and a vacuum Rabi splitting that is larger than the cavity and vibration line widths. These results demonstrate that strong coupling can be applied to the study of proteins with many possible applications including the elucidation of the role of vibrational dynamics in enzyme catalysis and in H/D exchange experiments.

  4. Effect of the biopolymer mixing ratio on the formation of electrostatically coupled whey protein-κ- and ι-carrageenan networks in the presence and absence of oil droplets.

    PubMed

    Lam, Ricky S H; Nickerson, Michael T

    2014-08-27

    The rheological properties of 1.0% (w/w) whey protein isolate (WPI)-κ-/ι-carrageenan (CG) mixtures were investigated during a slow acidification process by glucono-δ-lactone from pH 7.00 to ∼4.20 as a function of biopolymer mixing ratio and in the presence and absence of oil droplets. In all cases, electrostatic coupled biopolymer and emulsion gel networks were formed at pH values corresponding to where attractive interactions between WPI and CG began. Formed WPI-CG complexes were found to be surface active, capable of lowering interfacial tension and forming viscoelastic interfacial films within emulsion-based systems. Both biopolymer and emulsion-based gels increased in strength and elasticity as the CG content increased, regardless of the type of CG present. However, WPI-ι-CG coupled networks were stronger than WPI-κ-CG networks, presumably due to the higher number of sulfate groups attracting the WPI molecules.

  5. 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.

  6. 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

  7. Protein hydration dynamics and molecular mechanism of coupled water-protein fluctuations.

    PubMed

    Zhang, Luyuan; Yang, Yi; Kao, Ya-Ting; Wang, Lijuan; Zhong, Dongping

    2009-08-05

    Protein surface hydration is fundamental to its structural stability and flexibility, and water-protein fluctuations are essential to biological function. Here, we report a systematic global mapping of water motions in the hydration layer around a model protein of apomyoglobin in both native and molten globule states. With site-directed mutagenesis, we use intrinsic tryptophan as a local optical probe to scan the protein surface one at a time with single-site specificity. With femtosecond resolution, we examined 16 mutants in two states and observed two types of water-network relaxation with distinct energy and time distributions. The first water motion results from the local collective hydrogen-bond network relaxation and occurs in a few picoseconds. The initial hindered motions, observed in bulk water in femtoseconds, are highly suppressed and drastically slow down due to structured water-network collectivity in the layer. The second water-network relaxation unambiguously results from the lateral cooperative rearrangements in the inner hydration shell and occurs in tens to hundreds of picoseconds. Significantly, this longtime dynamics is the coupled interfacial water-protein motions and is the direct measurement of such cooperative fluctuations. These local protein motions, although highly constrained, are necessary to assist the longtime water-network relaxation. A series of correlations of hydrating water dynamics and coupled fluctuations with local protein's chemical and structural properties were observed. These results are significant and reveal various water behaviors in the hydration layer with wide heterogeneity. We defined a solvation speed and an angular speed to quantify the water-network rigidity and local protein flexibility, respectively. We also observed that the dynamic hydration layer extends to more than 10 A. Finally, from native to molten globule states, the hydration water networks loosen up, and the protein locally becomes more flexible with

  8. Cascading failures in coupled networks: The critical role of node-coupling strength across networks.

    PubMed

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao

    2016-10-17

    The robustness of coupled networks against node failure has been of interest in the past several years, while most of the researches have considered a very strong node-coupling method, i.e., once a node fails, its dependency partner in the other network will fail immediately. However, this scenario cannot cover all the dependency situations in real world, and in most cases, some nodes cannot go so far as to fail due to theirs self-sustaining ability in case of the failures of their dependency partners. In this paper, we use the percolation framework to study the robustness of interdependent networks with weak node-coupling strength across networks analytically and numerically, where the node-coupling strength is controlled by an introduced parameter α. If a node fails, each link of its dependency partner will be removed with a probability 1-α. By tuning the fraction of initial preserved nodes p, we find a rich phase diagram in the plane p-α, with a crossover point at which a first-order percolation transition changes to a second-order percolation transition.

  9. Cascading failures in coupled networks: The critical role of node-coupling strength across networks

    NASA Astrophysics Data System (ADS)

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao

    2016-10-01

    The robustness of coupled networks against node failure has been of interest in the past several years, while most of the researches have considered a very strong node-coupling method, i.e., once a node fails, its dependency partner in the other network will fail immediately. However, this scenario cannot cover all the dependency situations in real world, and in most cases, some nodes cannot go so far as to fail due to theirs self-sustaining ability in case of the failures of their dependency partners. In this paper, we use the percolation framework to study the robustness of interdependent networks with weak node-coupling strength across networks analytically and numerically, where the node-coupling strength is controlled by an introduced parameter α. If a node fails, each link of its dependency partner will be removed with a probability 1‑α. By tuning the fraction of initial preserved nodes p, we find a rich phase diagram in the plane p‑α, with a crossover point at which a first-order percolation transition changes to a second-order percolation transition.

  10. Cascading failures in coupled networks: The critical role of node-coupling strength across networks

    PubMed Central

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao

    2016-01-01

    The robustness of coupled networks against node failure has been of interest in the past several years, while most of the researches have considered a very strong node-coupling method, i.e., once a node fails, its dependency partner in the other network will fail immediately. However, this scenario cannot cover all the dependency situations in real world, and in most cases, some nodes cannot go so far as to fail due to theirs self-sustaining ability in case of the failures of their dependency partners. In this paper, we use the percolation framework to study the robustness of interdependent networks with weak node-coupling strength across networks analytically and numerically, where the node-coupling strength is controlled by an introduced parameter α. If a node fails, each link of its dependency partner will be removed with a probability 1−α. By tuning the fraction of initial preserved nodes p, we find a rich phase diagram in the plane p−α, with a crossover point at which a first-order percolation transition changes to a second-order percolation transition. PMID:27748446

  11. The human protein coevolution network.

    PubMed

    Tillier, Elisabeth R M; Charlebois, Robert L

    2009-10-01

    Coevolution maintains interactions between phenotypic traits through the process of reciprocal natural selection. Detecting molecular coevolution can expose functional interactions between molecules in the cell, generating insights into biological processes, pathways, and the networks of interactions important for cellular function. Prediction of interaction partners from different protein families exploits the property that interacting proteins can follow similar patterns and relative rates of evolution. Current methods for detecting coevolution based on the similarity of phylogenetic trees or evolutionary distance matrices have, however, been limited by requiring coevolution over the entire evolutionary history considered and are inaccurate in the presence of paralogous copies. We present a novel method for determining coevolving protein partners by finding the largest common submatrix in a given pair of distance matrices, with the size of the largest common submatrix measuring the strength of coevolution. This approach permits us to consider matrices of different size and scale, to find lineage-specific coevolution, and to predict multiple interaction partners. We used MatrixMatchMaker to predict protein-protein interactions in the human genome. We show that proteins that are known to interact physically are more strongly coevolving than proteins that simply belong to the same biochemical pathway. The human coevolution network is highly connected, suggesting many more protein-protein interactions than are currently known from high-throughput and other experimental evidence. These most strongly coevolving proteins suggest interactions that have been maintained over long periods of evolutionary time, and that are thus likely to be of fundamental importance to cellular function.

  12. Cascading load model in interdependent networks with coupled strength

    NASA Astrophysics Data System (ADS)

    Wang, Jianwei; Li, Yun; Zheng, Qiaofang

    2015-07-01

    Considering the coupled strength between interdependent networks, we introduce a new method to define the initial load on an edge and propose a cascading load model in interdependent networks. We explore the robustness of the interdependent networks against cascading failures by two measures, i.e., the critical threshold βc quantifying the whole robustness of the interdependent networks to avoid the emergence of cascading failure, and the new proposed smallest capacity threshold βc,s quantifying the degree of the worst damage of the interdependent networks. We numerically find that the AL (high-degree nodes in network A connect high-degree ones in network B) link between two networks can greatly enhance the robust level of the interdependent networks against cascading failures. Especially we observe that the values of βc in the interdependent networks with both the DL (high-degree nodes in network A connect low-degree ones in network B) link and the RL (nodes in network A randomly connect ones in network B) link increase monotonically with the coupled strength, while the values of βc,s in the interdependent networks with three types of link patterns almost monotonically decreases with the coupled strength. In the interdependent networks with the AL, the value of βc first decreases and then increases with the coupled strength. We further explain this interesting phenomenon by a simple graph. In addition, we study the influence of the coupled strength on the efficiency of two attacks to destroy the interdependent networks. We find that, when the coupled strength between two networks is weaker, attacking the edges with the lower load is more easier to trigger the cascading propagation than attacking the nodes with the higher load, however, when the coupled strength in two networks is stronger, the case is on the contrary. Finally, we give reasonable explanations from the local perspective of the total capacity of all neighboring edges of a failed edge.

  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. 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.

  15. 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.

  16. Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium

    NASA Astrophysics Data System (ADS)

    Dahmen, David; Bos, Hannah; Helias, Moritz

    2016-07-01

    Randomly coupled Ising spins constitute the classical model of collective phenomena in disordered systems, with applications covering glassy magnetism and frustration, combinatorial optimization, protein folding, stock market dynamics, and social dynamics. The phase diagram of these systems is obtained in the thermodynamic limit by averaging over the quenched randomness of the couplings. However, many applications require the statistics of activity for a single realization of the possibly asymmetric couplings in finite-sized networks. Examples include reconstruction of couplings from the observed dynamics, representation of probability distributions for sampling-based inference, and learning in the central nervous system based on the dynamic and correlation-dependent modification of synaptic connections. The systematic cumulant expansion for kinetic binary (Ising) threshold units with strong, random, and asymmetric couplings presented here goes beyond mean-field theory and is applicable outside thermodynamic equilibrium; a system of approximate nonlinear equations predicts average activities and pairwise covariances in quantitative agreement with full simulations down to hundreds of units. The linearized theory yields an expansion of the correlation and response functions in collective eigenmodes, leads to an efficient algorithm solving the inverse problem, and shows that correlations are invariant under scaling of the interaction strengths.

  17. The human protein coevolution network

    PubMed Central

    Tillier, Elisabeth R.M.; Charlebois, Robert L.

    2009-01-01

    Coevolution maintains interactions between phenotypic traits through the process of reciprocal natural selection. Detecting molecular coevolution can expose functional interactions between molecules in the cell, generating insights into biological processes, pathways, and the networks of interactions important for cellular function. Prediction of interaction partners from different protein families exploits the property that interacting proteins can follow similar patterns and relative rates of evolution. Current methods for detecting coevolution based on the similarity of phylogenetic trees or evolutionary distance matrices have, however, been limited by requiring coevolution over the entire evolutionary history considered and are inaccurate in the presence of paralogous copies. We present a novel method for determining coevolving protein partners by finding the largest common submatrix in a given pair of distance matrices, with the size of the largest common submatrix measuring the strength of coevolution. This approach permits us to consider matrices of different size and scale, to find lineage-specific coevolution, and to predict multiple interaction partners. We used MatrixMatchMaker to predict protein–protein interactions in the human genome. We show that proteins that are known to interact physically are more strongly coevolving than proteins that simply belong to the same biochemical pathway. The human coevolution network is highly connected, suggesting many more protein–protein interactions than are currently known from high-throughput and other experimental evidence. These most strongly coevolving proteins suggest interactions that have been maintained over long periods of evolutionary time, and that are thus likely to be of fundamental importance to cellular function. PMID:19696150

  18. Protein sectors: statistical coupling analysis versus conservation.

    PubMed

    Teşileanu, Tiberiu; Colwell, Lucy J; Leibler, Stanislas

    2015-02-01

    Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed "sectors". The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation.

  19. 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.

  20. Impulsive synchronization of coupled dynamical networks with nonidentical Duffing oscillators and coupling delays.

    PubMed

    Wang, Zhengxin; Duan, Zhisheng; Cao, Jinde

    2012-03-01

    This paper aims to investigate the synchronization problem of coupled dynamical networks with nonidentical Duffing-type oscillators without or with coupling delays. Different from cluster synchronization of nonidentical dynamical networks in the previous literature, this paper focuses on the problem of complete synchronization, which is more challenging than cluster synchronization. By applying an impulsive controller, some sufficient criteria are obtained for complete synchronization of the coupled dynamical networks of nonidentical oscillators. Furthermore, numerical simulations are given to verify the theoretical results.

  1. Heterotrimeric G protein-coupled signaling in plants.

    PubMed

    Urano, Daisuke; Jones, Alan M

    2014-01-01

    Investigators studying G protein-coupled signaling--often called the best-understood pathway in the world owing to intense research in medical fields--have adopted plants as a new model to explore the plasticity and evolution of G signaling. Much research on plant G signaling has not disappointed. Although plant cells have most of the core elements found in animal G signaling, differences in network architecture and intrinsic properties of plant G protein elements make G signaling in plant cells distinct from the animal paradigm. In contrast to animal G proteins, plant G proteins are self-activating, and therefore regulation of G activation in plants occurs at the deactivation step. The self-activating property also means that plant G proteins do not need and therefore do not have typical animal G protein-coupled receptors. Targets of activated plant G proteins, also known as effectors, are unlike effectors in animal cells. The simpler repertoire of G signal elements in Arabidopsis makes G signaling easier to manipulate in a multicellular context.

  2. 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.

  3. Similar Others in Same-Sex Couples' Social Networks.

    PubMed

    LeBlanc, Allen J; Frost, David M; Alston-Stepnitz, Eli; Bauermeister, Jose; Stephenson, Rob; Woodyatt, Cory R; de Vries, Brian

    2015-01-01

    Same-sex couples experience unique minority stressors. It is known that strong social networks facilitate access to psychosocial resources that help people reduce and manage stress. However, little is known about the social networks of same-sex couples, in particular their connections to other same-sex couples, which is important to understand given that the presence of similar others in social networks can ameliorate social stress for stigmatized populations. In this brief report, we present data from a diverse sample of 120 same-sex couples in Atlanta and San Francisco. The median number of other same-sex couples known was 12; couples where one partner was non-Hispanic White and the other a person of color knew relatively few other same-sex couples; and there was a high degree of homophily within the social networks of same-sex couples. These data establish a useful starting point for future investigations of couples' social networks, especially couples whose relationships are stigmatized or marginalized in some way. Better understandings of the size, composition, and functions of same-sex couples' social networks are critically needed.

  4. Amplitude death of identical oscillators in networks with direct coupling

    NASA Astrophysics Data System (ADS)

    Illing, Lucas

    2016-08-01

    It is known that amplitude death can occur in networks of coupled identical oscillators if they interact via diffusive time-delayed coupling links. Here we consider networks of oscillators that interact via direct time-delayed coupling links. It is shown analytically that amplitude death is impossible for directly coupled Stuart-Landau oscillators, in contradistinction to the case of diffusive coupling. We demonstrate that amplitude death in the strict sense does become possible in directly coupled networks if the node dynamics is governed by second-order delay differential equations. Finally, we analyze in detail directly coupled nodes whose dynamics are described by first-order delay differential equations and find that, while amplitude death in the strict sense is impossible, other interesting oscillation quenching scenarios exist.

  5. Coupling of protein dynamics with the solvent

    NASA Astrophysics Data System (ADS)

    Caliskan, Gokhan; Sauzan, Azzam; Mehtani, Disha; Sokolov, Alexei

    2003-03-01

    Glycerol and trehalose are among the many viscous solvents that are widely used for biostabilization and controlling the dynamics of proteins. It is believed that the suppression of the structural relaxations by high viscosity of solvent is responsible for improved stability in proteins. However, results of [1] and [2] demonstrate stronger suppression of biochemical activity and dynamics of proteins by liquid glycerol than by solid trehalose in a wide temperature range. The authors tried to explain the counterintuitive observations by a possible decoupling of the dynamics of the protein from trehalose. In order to test the validity of this assumption and to investigate the influence of the fast dynamics in proteins, the low frequency Raman scattering spectroscopy technique is used. Both relaxational and vibrational dynamics of glycerol, trehalose, and lysozyme in glycerol and in trehalose are studied in a wide temperature range. Dynamics of lysozyme in glycerol follows the strong temperature dependence of relaxational and vibrational dynamics of the bulk glycerol. On the other hand, the weak temperature dependence of dynamics of lysozyme in trehalose follows exactly the behavior of pure trehalose. This proves that there is a strong dynamic coupling between the protein and the solvents used. Interestingly, stronger relaxations in solid trehalose as compared to liquid glycerol are observed in the GHz region at low temperatures. This could be the reason for the enhanced protein activity observed in trehalose, compared to that in glycerol in this temperature range. Suppression of these fast relaxations should be the key for providing long-term stability to proteins. 1. Sastry, G.M. and N. Agmon, Trehalose prevents myoglobin collapse and preserves its internal mobility. BIOCHEMISTRY, 1997, 36(23): p. 7097-108. 2. Caliskan, G., et al., Influence of solvent on dynamics and stability of a protein. Journal of Non-Crystalline Solids, 2002, 307-310: p. 887-893.

  6. Exact computation of probability landscape of stochastic networks of Single Input and Coupled Toggle Switch Modules.

    PubMed

    Terebus, Anna; Cao, Youfang; Liang, Jie

    2014-01-01

    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.

  7. 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.

  8. Transportation dynamics on coupled networks with limited bandwidth

    PubMed Central

    Li, Ming; Hu, Mao-Bin; Wang, Bing-Hong

    2016-01-01

    The communication networks in real world often couple with each other to save costs, which results in any network does not have a stand-alone function and efficiency. To investigate this, in this paper we propose a transportation model on two coupled networks with bandwidth sharing. We find that the free-flow state and the congestion state can coexist in the two coupled networks, and the free-flow path and congestion path can coexist in each network. Considering three bandwidth-sharing mechanisms, random, assortative and disassortative couplings, we also find that the transportation capacity of the network only depends on the coupling mechanism, and the fraction of coupled links only affects the performance of the system in the congestion state, such as the traveling time. In addition, with assortative coupling, the transportation capacity of the system will decrease significantly. However, the disassortative coupling has little influence on the transportation capacity of the system, which provides a good strategy to save bandwidth. Furthermore, a theoretical method is developed to obtain the bandwidth usage of each link, based on which we can obtain the congestion transition point exactly. PMID:27966624

  9. Transportation dynamics on coupled networks with limited bandwidth

    NASA Astrophysics Data System (ADS)

    Li, Ming; Hu, Mao-Bin; Wang, Bing-Hong

    2016-12-01

    The communication networks in real world often couple with each other to save costs, which results in any network does not have a stand-alone function and efficiency. To investigate this, in this paper we propose a transportation model on two coupled networks with bandwidth sharing. We find that the free-flow state and the congestion state can coexist in the two coupled networks, and the free-flow path and congestion path can coexist in each network. Considering three bandwidth-sharing mechanisms, random, assortative and disassortative couplings, we also find that the transportation capacity of the network only depends on the coupling mechanism, and the fraction of coupled links only affects the performance of the system in the congestion state, such as the traveling time. In addition, with assortative coupling, the transportation capacity of the system will decrease significantly. However, the disassortative coupling has little influence on the transportation capacity of the system, which provides a good strategy to save bandwidth. Furthermore, a theoretical method is developed to obtain the bandwidth usage of each link, based on which we can obtain the congestion transition point exactly.

  10. Transportation dynamics on coupled networks with limited bandwidth.

    PubMed

    Li, Ming; Hu, Mao-Bin; Wang, Bing-Hong

    2016-12-14

    The communication networks in real world often couple with each other to save costs, which results in any network does not have a stand-alone function and efficiency. To investigate this, in this paper we propose a transportation model on two coupled networks with bandwidth sharing. We find that the free-flow state and the congestion state can coexist in the two coupled networks, and the free-flow path and congestion path can coexist in each network. Considering three bandwidth-sharing mechanisms, random, assortative and disassortative couplings, we also find that the transportation capacity of the network only depends on the coupling mechanism, and the fraction of coupled links only affects the performance of the system in the congestion state, such as the traveling time. In addition, with assortative coupling, the transportation capacity of the system will decrease significantly. However, the disassortative coupling has little influence on the transportation capacity of the system, which provides a good strategy to save bandwidth. Furthermore, a theoretical method is developed to obtain the bandwidth usage of each link, based on which we can obtain the congestion transition point exactly.

  11. G-protein-coupled receptors and cancer.

    PubMed

    Dorsam, Robert T; Gutkind, J Silvio

    2007-02-01

    G-protein-coupled receptors (GPCRs), the largest family of cell-surface molecules involved in signal transmission, have recently emerged as crucial players in tumour growth and metastasis. Malignant cells often hijack the normal physiological functions of GPCRs to survive, proliferate autonomously, evade the immune system, increase their blood supply, invade their surrounding tissues and disseminate to other organs. This Review will address our current understanding of the many roles of GPCRs and their signalling circuitry in tumour progression and metastasis. We will also discuss how interfering with GPCRs might provide unique opportunities for cancer prevention and treatment.

  12. Adaptive synchronization of two nonlinearly coupled complex dynamical networks with delayed coupling

    NASA Astrophysics Data System (ADS)

    Zheng, Song; Wang, Shuguo; Dong, Gaogao; Bi, Qinsheng

    2012-01-01

    This paper investigates the adaptive synchronization between two nonlinearly delay-coupled complex networks with the bidirectional actions and nonidentical topological structures. Based on LaSalle's invariance principle, some criteria for the synchronization between two coupled complex networks are achieved via adaptive control. To validate the proposed methods, the unified chaotic system as the nodes of the networks are analyzed in detail, and numerical simulations are given to illustrate the theoretical results.

  13. 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.

  14. Synchronization for linear singularly perturbed complex networks with coupling delays

    NASA Astrophysics Data System (ADS)

    Cai, Chenxiao; Xu, Jing; Liu, Yurong; Zou, Yun

    2015-02-01

    This paper is concerned with the synchronization problem about linear singularly perturbed complex network system with coupling delay. The sufficient delay-dependent conditions for the synchronization of the network are established by introducing an equivalent network system with the Lyapunov stability theory. These conditions, which are formulated in terms of linear matrix inequalities, can be solved efficiently by the LMI toolbox of MATLAB. A simulation example is provided to show the validity of the proposed the synchronization conditions of the whole network.

  15. 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.

  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. The fragility of protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Schneider, C. M.; Andrade, R. F. S.; Shinbrot, T.; Herrmann, H. J.

    2011-07-01

    The capacity to resist perturbations from the environment is crucial to the survival of all organisms. We quantitatively analyze the susceptibility of protein interaction networks of numerous organisms to random and targeted failures. We find for all organisms studied that random rewiring improves protein network robustness, so that actual networks are more fragile than rewired surrogates. This unexpected fragility contrasts with the behavior of networks such as the Internet, whose robustness decreases with random rewiring. We trace this surprising effect to the modular structure of protein networks.

  18. Dopamine supports coupling of attention-related networks.

    PubMed

    Dang, Linh C; O'Neil, James P; Jagust, William J

    2012-07-11

    Attentional processing has been associated with the dorsal attention, default mode, and frontoparietal control networks. The dorsal attention network is involved in externally focused attention whereas the default mode network is involved in internally directed attention. The frontoparietal control network has been proposed to mediate the transition between external and internal attention by coupling its activity to either the dorsal attention network or the default mode network, depending on the attentional demand. Dopamine is hypothesized to modulate attention and has been linked to the integrity of these three attention-related networks. We used PET with 6-[(18)F]fluoro-L-m-tyrosine to quantify dopamine synthesis capacity in vivo and fMRI to acquire stimulus-independent brain activity in cognitively healthy human subjects. We found that in the resting state where internal cognition dominates, dopamine enhances the coupling between the frontoparietal control network and the default mode network while reducing the coupling between the frontoparietal control network and the dorsal attention network. These results add a neurochemical perspective to the role of network interaction in modulating attention.

  19. Protein-Protein Interface and Disease: Perspective from Biomolecular Networks.

    PubMed

    Hu, Guang; Xiao, Fei; Li, Yuqian; Li, Yuan; Vongsangnak, Wanwipa

    Protein-protein interactions are involved in many important biological processes and molecular mechanisms of disease association. Structural studies of interfacial residues in protein complexes provide information on protein-protein interactions. Characterizing protein-protein interfaces, including binding sites and allosteric changes, thus pose an imminent challenge. With special focus on protein complexes, approaches based on network theory are proposed to meet this challenge. In this review we pay attention to protein-protein interfaces from the perspective of biomolecular networks and their roles in disease. We first describe the different roles of protein complexes in disease through several structural aspects of interfaces. We then discuss some recent advances in predicting hot spots and communication pathway analysis in terms of amino acid networks. Finally, we highlight possible future aspects of this area with respect to both methodology development and applications for disease treatment.

  20. Weak chimeras in minimal networks of coupled phase oscillators

    NASA Astrophysics Data System (ADS)

    Ashwin, Peter; Burylko, Oleksandr

    2015-01-01

    We suggest a definition for a type of chimera state that appears in networks of indistinguishable phase oscillators. Defining a "weak chimera" as a type of invariant set showing partial frequency synchronization, we show that this means they cannot appear in phase oscillator networks that are either globally coupled or too small. We exhibit various networks of four, six, and ten indistinguishable oscillators, where weak chimeras exist with various dynamics and stabilities. We examine the role of Kuramoto-Sakaguchi coupling in giving degenerate (neutrally stable) families of weak chimera states in these example networks.

  1. Causal and Structural Connectivity of Pulse-Coupled Nonlinear Networks

    NASA Astrophysics Data System (ADS)

    Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

    2013-08-01

    We study the reconstruction of structural connectivity for a general class of pulse-coupled nonlinear networks and show that the reconstruction can be successfully achieved through linear Granger causality (GC) analysis. Using spike-triggered correlation of whitened signals, we obtain a quadratic relationship between GC and the network couplings, thus establishing a direct link between the causal connectivity and the structural connectivity within these networks. Our work may provide insight into the applicability of GC in the study of the function of general nonlinear networks.

  2. 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

    2015-07-15

    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.

  3. 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

  4. Effect of resource constraints on intersimilar coupled networks

    NASA Astrophysics Data System (ADS)

    Shai, S.; Dobson, S.

    2012-12-01

    Most real-world networks do not live in isolation but are often coupled together within a larger system. Recent studies have shown that intersimilarity between coupled networks increases the connectivity of the overall system. However, unlike connected nodes in a single network, coupled nodes often share resources, like time, energy, and memory, which can impede flow processes through contention when intersimilarly coupled. We study a model of a constrained susceptible-infected-recovered (SIR) process on a system consisting of two random networks sharing the same set of nodes, where nodes are limited to interact with (and therefore infect) a maximum number of neighbors at each epidemic time step. We obtain that, in agreement with previous studies, when no limit exists (regular SIR model), positively correlated (intersimilar) coupling results in a lower epidemic threshold than negatively correlated (interdissimilar) coupling. However, in the case of the constrained SIR model, the obtained epidemic threshold is lower with negatively correlated coupling. The latter finding differentiates our work from previous studies and provides another step towards revealing the qualitative differences between single and coupled networks.

  5. 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.

  6. Hox protein interactions: screening and network building.

    PubMed

    Bergiers, Isabelle; Lambert, Barbara; Daakour, Sarah; Twizere, Jean-Claude; Rezsohazy, René

    2014-01-01

    Understanding the mode of action of Hox proteins requires the identification of molecular and cellular pathways they take part in. This includes to characterize the networks of protein-protein interactions involving Hox proteins. In this chapter we propose a strategy and methods to map Hox interaction networks, from yeast two-hybrid and high-throughput yeast two-hybrid interaction screening to bioinformatic analyses based on the software platform Cytoscape.

  7. 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.

  8. Dynamics of a network of phase oscillators with plastic couplings

    SciTech Connect

    Nekorkin, V. I.; Kasatkin, D. V.

    2016-06-08

    The processes of synchronization and phase cluster formation are investigated in a complex network of dynamically coupled phase oscillators. Coupling weights evolve dynamically depending on the phase relations between the oscillators. It is shown that the network exhibits several types of behavior: the globally synchronized state, two-cluster and multi-cluster states, different synchronous states with a fixed phase relationship between the oscillators and chaotic desynchronized state.

  9. 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

  10. 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.

  11. Lethality and entropy of protein interaction networks.

    PubMed

    Manke, Thomas; Demetrius, Lloyd; Vingron, Martin

    2005-01-01

    We characterize protein interaction networks in terms of network entropy. This approach suggests a ranking principle, which strongly correlates with elements of functional importance, such as lethal proteins. Our combined analysis of protein interaction networks and functional profiles in single cellular yeast and multi-cellular worm shows that proteins with large contribution to network entropy are preferentially lethal. While entropy is inherently a dynamical concept, the present analysis incorporates only structural information. Our result therefore highlights the importance of topological features, which appear as correlates of an underlying dynamical property, and which in turn determine functional traits. We argue that network entropy is a natural extension of previously studied observables, such as pathway multiplicity and centrality. It is also applicable to networks in which the processes can be quantified and therefore serves as a link to study questions of structural and dynamical robustness in a unified way.

  12. 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.

  13. Workshop: Theory an Applications of Coupled Cell Networks

    DTIC Science & Technology

    2006-03-22

    Economia and Centro de Matematica, Universidade do Porto) Application of coupled cell systems have been made to a wide range of problems in the physical and...the propagation of perturbations across the optical spectrum. Minimal coupled cell networks M. Aguiar (Faculdade de Economia do Porto), A.P.S. Dias

  14. G-protein-coupled receptors and melanoma.

    PubMed

    Lee, Hwa Jin; Wall, Brian; Chen, Suzie

    2008-08-01

    G-protein-coupled receptors (GPCR) are the largest family of receptors with over 500 members. Evaluation of GPCR gene expression in primary human tumors identified over-expression of GPCR in several tumor types. Analysis of cancer samples in different disease stages also suggests that some GPCR may be involved in early tumor progression and others may play a critical role in tumor invasion and metastasis. Currently, >50% of drug targets to various human diseases are based on GPCR. In this review, the relationships between several GPCR and melanoma development and/or progression will be discussed. Finally, the possibility of using one or more of these GPCR as therapeutic targets in melanoma will be summarized.

  15. G Protein-Coupled Receptor Biased Agonism

    PubMed Central

    Hodavance, Sima Y.; Gareri, Clarice; Torok, Rachel D.; Rockman, Howard A.

    2016-01-01

    G protein-coupled receptors (GPCR) are the largest family of targets for current therapeutics. The classic model of their activation was binary, where agonist binding induced an active conformation and subsequent downstream signaling. Subsequently, the revised concept of biased agonism emerged, where different ligands at the same GPCR selectively activate one downstream pathway versus another. Advances in understanding the mechanism of biased agonism has led to the development of novel ligands, which have the potential for improved therapeutic and safety profiles. In this review, we summarize the theory and most recent breakthroughs in understanding biased signaling, examine recent laboratory investigations concerning biased ligands across different organ systems, and discuss the promising clinical applications of biased agonism. PMID:26751266

  16. Synchronization in complex delayed dynamical networks with nonsymmetric coupling

    NASA Astrophysics Data System (ADS)

    Wu, Jianshe; Jiao, Licheng

    2007-12-01

    A new general complex delayed dynamical network model with nonsymmetric coupling is introduced, and then we investigate its synchronization phenomena. Several synchronization criteria for delay-independent and delay-dependent synchronization are provided which generalize some previous results. The matrix Jordan canonical formalization method is used instead of the matrix diagonalization method, so in our synchronization criteria, the coupling configuration matrix of the network does not required to be diagonalizable and may have complex eigenvalues. Especially, we show clearly that the synchronizability of a delayed dynamical network is not always characterized by the second-largest eigenvalue even though all the eigenvalues of the coupling configuration matrix are real. Furthermore, the effects of time-delay on synchronizability of networks with unidirectional coupling are studied under some typical network structures. The results are illustrated by delayed networks in which each node is a two-dimensional limit cycle oscillator system consisting of a two-cell cellular neural network, numerical simulations show that these networks can realize synchronization with smaller time-delay, and will lose synchronization when the time-delay increase larger than a threshold.

  17. Protein-protein interaction network of celiac disease.

    PubMed

    Zamanian Azodi, Mona; Peyvandi, Hassan; Rostami-Nejad, Mohammad; Safaei, Akram; Rostami, Kamran; Vafaee, Reza; Heidari, Mohammadhossein; Hosseini, Mostafa; Zali, Mohammad Reza

    2016-01-01

    The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease.

  18. 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

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

    PubMed

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

    2016-03-22

    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.

  20. Evolutionarily conserved coupling of adaptive and excitable networks mediates eukaryotic chemotaxis

    NASA Astrophysics Data System (ADS)

    Tang, Ming; Wang, Mingjie; Shi, Changji; Iglesias, Pablo A.; Devreotes, Peter N.; Huang, Chuan-Hsiang

    2014-10-01

    Numerous models explain how cells sense and migrate towards 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, signalling activity is suppressed towards the low side in a gradient or following removal of uniform chemoattractant. Second, signalling 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, as 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.

  1. Spectral statistics of Lyapunov exponents in coupled map networks

    NASA Astrophysics Data System (ADS)

    Patra, Soumen K.; Ghosh, Anandamohan

    2017-03-01

    Spectral statistics of the Lyapunov exponents computed for coupled map networks bear strong signatures of different phases emergent from the spatiotemporal dynamics. We find that the distributions of gaps in the Lyapunov spectrum for the chaotic and the synchronized phases show Poisson and GOE statistics, respectively, in agreement with the universal predictions of the random matrix theory. The presence of quenched disorder in coupled map networks generates a non-trivial chaotic Griffiths phase for intermediate coupling strengths. The Lyapunov spectral statistics obtained for the chaotic Griffiths phase show strong suppression of gaps and the Lyapunov vectors indicate a unique intermittent dynamical localization.

  2. Synchronization of networks of oscillators with distributed delay coupling

    NASA Astrophysics Data System (ADS)

    Kyrychko, Y. N.; Blyuss, K. B.; Schöll, E.

    2014-12-01

    This paper studies the stability of synchronized states in networks, where couplings between nodes are characterized by some distributed time delay, and develops a generalized master stability function approach. Using a generic example of Stuart-Landau oscillators, it is shown how the stability of synchronized solutions in networks with distributed delay coupling can be determined through a semi-analytic computation of Floquet exponents. The analysis of stability of fully synchronized and of cluster or splay states is illustrated for several practically important choices of delay distributions and network topologies.

  3. Alignment-free protein interaction network comparison

    PubMed Central

    Ali, Waqar; Rito, Tiago; Reinert, Gesine; Sun, Fengzhu; Deane, Charlotte M.

    2014-01-01

    Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction. Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type. Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources. Contact: w.ali@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161230

  4. Optimal control of coupled PDE networks with automated code generation

    NASA Astrophysics Data System (ADS)

    Papadopoulos, D.

    2012-09-01

    The purpose of this work is to present a framework for the optimal control of coupled PDE networks. A coupled PDE network is a system of partial differential equations coupled together. Such systems can be represented as a directed graph. A domain specific language (DSL)—an extension of the DOT language—is used for the description of such a coupled PDE network. The adjoint equations and the gradient, required for its optimal control, are computed with the help of a computer algebra system (CAS). Automated code generation techniques have been used for the generation of the PDE systems of both the direct and the adjoint equations. Both the direct and adjoint equations are solved with the standard finite element method. Finally, for the numerical optimization of the system standard optimization techniques are used such as BFGS and Newton conjugate gradient.

  5. Synchronization in complex dynamical networks with nonsymmetric coupling

    NASA Astrophysics Data System (ADS)

    Wu, Jianshe; Jiao, Licheng

    2008-10-01

    Based on the work of Nishikawa and Motter, who have extended the well-known master stability framework to include non-diagonalizable cases, we develop another extension of the master stability framework to obtain criteria for global synchronization. Several criteria for global synchronization are provided which generalize some previous results. The Jordan canonical transformation method is used in stead of the matrix diagonalization method. Especially, we show clearly that, the synchronizability of a dynamical network with nonsymmetric coupling is not always characterized by its second-largest eigenvalue, even though all the eigenvalues of the nonsymmetric coupling matrix are real. Furthermore, the effects of the asymmetry of coupling on synchronizability of networks with different structures are analyzed. Numerical simulations are also done to illustrate and verify the theoretical results on networks in which each node is a dynamical limit cycle oscillator consisting of a two-cell cellular neural network.

  6. Hepatitis C virus infection protein network.

    PubMed

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

    2008-01-01

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

  7. Coupling network simulation for the PEP-II RF cavity

    SciTech Connect

    Ng, C.K.; Ko, K.; Kroll, N.; Rimmer, R.

    1994-06-01

    Two different input coupling networks are being proposed for the PEP-II RF cavity: a loop type and an aperture type. Both designs are expected to provide a varying coupling factor ranging from three to ten and to handle up to 500 kW of transmitted power. For beam stability reasons, it is further desirable for the coupling network to couple out any HOM`s that are not adequately damped by the dedicated waveguides. This paper evaluates the coupling factors for the two types of input couplers using MAFIA, and estimates the additional damping they provide to the TM{sub 021} mode which has the highest residual impedance after the effect of the damping waveguides is included. Peak power densities at areas of high current concentration will also be presented.

  8. Protein complexes predictions within protein interaction networks using genetic algorithms.

    PubMed

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

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

    PubMed

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

    2015-06-17

    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.

  10. 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

  11. 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

  12. 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.

  13. Hepatitis C virus infection protein network

    PubMed Central

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

    2008-01-01

    A proteome-wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein–protein interactions between HCV and human proteins was identified by yeast two-hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFβ pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins. PMID:18985028

  14. 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.

  15. Bistable synchronization of coupled random network of cubic maps

    NASA Astrophysics Data System (ADS)

    Nag, Mayurakshi

    2017-06-01

    The spatiotemporal behavior of coupled cubic maps over a dynamic network having randomness in coupling connections is investigated here. Due to the bistable nature of cubic map the synchronization behavior is dependent on the initial conditions. The network can stabilize to any one of the nonzero unstable fixed point of the map depending on the initial conditions. Linear stability analysis of synchronized fixed point gives the value of coupling at which onset of synchronization occurs. The critical coupling strength depends on the randomness in rewiring, properties of the local map, but it is independent of lattice size. Numerical simulation results match very well with predictions from theoretical analysis. Behaviors of the network for synchronized initial conditions are pointed out. Looking at the case of stability in a network with static rewiring, it is found that, the range of synchronization of fixed point becomes shorter than the dynamical random one. Contribution of delay in the synchronization phenomenon is studied both analytically and numerically and the range of synchronized period-2 orbit is found to be quite similar in both the cases. Multistable nature of the delay coupled network is shown numerically.

  16. 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.

  17. Link weight evolution in a network of coupled chemical oscillators

    NASA Astrophysics Data System (ADS)

    Ke, Hua; Tinsley, Mark R.; Steele, Aaron; Wang, Fang; Showalter, Kenneth

    2014-05-01

    Link weight evolution is studied in a network of coupled chemical oscillators. Oscillators are perturbed by adjustments in imposed light intensity based on excitatory or inhibitory links to other oscillators undergoing excitation. Experimental and modeling studies demonstrate that the network is capable of producing sustained coordinated activity. The individual nodes of the network exhibit incoherent firing events; however, a dominant frequency can be discerned within the collective signal by Fourier analysis. The introduction of spike-timing-dependent plasticity yields a network that evolves to a stable unimodal link weight distribution.

  18. 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.

  19. Network simulation reveals significant contribution of network motifs to the age-dependency of yeast protein-protein interaction networks.

    PubMed

    Liang, Cheng; Luo, Jiawei; Song, Dan

    2014-07-29

    Advances in proteomic technologies combined with sophisticated computing and modeling methods have generated an unprecedented amount of high-throughput data for system-scale analysis. As a result, the study of protein-protein interaction (PPI) networks has garnered much attention in recent years. One of the most fundamental problems in studying PPI networks is to understand how their architecture originated and evolved to their current state. By investigating how proteins of different ages are connected in the yeast PPI networks, one can deduce their expansion procedure in evolution and how the ancient primitive network expanded and evolved. Studies have shown that proteins are often connected to other proteins of a similar age, suggesting a high degree of age preference between interacting proteins. Though several theories have been proposed to explain this phenomenon, none of them considered protein-clusters as a contributing factor. Here we first investigate the age-dependency of the proteins from the perspective of network motifs. Our analysis confirms that proteins of the same age groups tend to form interacting network motifs; furthermore, those proteins within motifs tend to be within protein complexes and the interactions among them largely contribute to the observed age preference in the yeast PPI networks. In light of these results, we describe a new modeling approach, based on "network motifs", whereby topologically connected protein clusters in the network are treated as single evolutionary units. Instead of modeling single proteins, our approach models the connections and evolutionary relationships of multiple related protein clusters or "network motifs" that are collectively integrated into an existing PPI network. Through simulation studies, we found that the "network motif" modeling approach can capture yeast PPI network properties better than if individual proteins were considered to be the simplest evolutionary units. Our approach provides a fresh

  20. Modeling the network dynamics of pulse-coupled neurons

    NASA Astrophysics Data System (ADS)

    Chandra, Sarthak; Hathcock, David; Crain, Kimberly; Antonsen, Thomas M.; Girvan, Michelle; Ott, Edward

    2017-03-01

    We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions and degree correlations (assortativity). Using the ansatz of Ott and Antonsen [Chaos 18, 037113 (2008)], we obtain a reduced system of ordinary differential equations describing the mean-field dynamics, with significantly lower dimensionality compared with the complete set of dynamical equations for the system. We find that, for sufficiently large networks and degrees, the dynamical behavior of the reduced system agrees well with that of the full network. This dimensional reduction allows for an efficient characterization of system phase transitions and attractors. For networks with tightly peaked degree distributions, the macroscopic behavior closely resembles that of fully connected networks previously studied by others. In contrast, networks with highly skewed degree distributions exhibit different macroscopic dynamics due to the emergence of degree dependent behavior of different oscillators. For nonassortative networks (i.e., networks without degree correlations), we observe the presence of a synchronously firing phase that can be suppressed by the presence of either assortativity or disassortativity in the network. We show that the results derived here can be used to analyze the effects of network topology on macroscopic behavior in neuronal networks in a computationally efficient fashion.

  1. 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

  2. G Protein-Coupled Receptors in Cancer.

    PubMed

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

    2016-08-12

    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.

  3. 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

  4. Molecular dynamics techniques for modeling G protein-coupled receptors.

    PubMed

    McRobb, Fiona M; Negri, Ana; Beuming, Thijs; Sherman, Woody

    2016-10-01

    G protein-coupled receptors (GPCRs) constitute a major class of drug targets and modulating their signaling can produce a wide range of pharmacological outcomes. With the growing number of high-resolution GPCR crystal structures, we have the unprecedented opportunity to leverage structure-based drug design techniques. Here, we discuss a number of advanced molecular dynamics (MD) techniques that have been applied to GPCRs, including long time scale simulations, enhanced sampling techniques, water network analyses, and free energy approaches to determine relative binding free energies. On the basis of the many success stories, including those highlighted here, we expect that MD techniques will be increasingly applied to aid in structure-based drug design and lead optimization for GPCRs.

  5. 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.

  6. 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.

  7. Synchronization in output-coupled temporal Boolean networks

    PubMed Central

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

    2014-01-01

    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. PMID:25189531

  8. 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

  9. NAPS: Network Analysis of Protein Structures

    PubMed Central

    Chakrabarty, Broto; Parekh, Nita

    2016-01-01

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

  10. Structural dependencies of protein backbone 2JNC' couplings.

    PubMed

    Juranić, Nenad; Dannenberg, J J; Cornilescu, Gabriel; Salvador, Pedro; Atanasova, Elena; Ahn, Hee-Chul; Macura, Slobodan; Markley, John L; Prendergast, Franklyn G

    2008-04-01

    Protein folding can introduce strain in peptide covalent geometry, including deviations from planarity that are difficult to detect, especially for a protein in solution. We have found dependencies in protein backbone (2)J(NC') couplings on the planarity and the relative orientation of the sequential peptide planes. These dependences were observed in experimental (2)J(NC') couplings from seven proteins, and also were supported by DFT calculations for a model tripeptide. Findings indicate that elevated (2)J(NC') couplings may serve as reporters of structural strain in the protein backbone imposed by protein folds. Such information, supplemented with the H-bond strengths derived from (h3)J(NC') couplings, provides useful insight into the overall energy profile of the protein backbone in solution.

  11. Structural dependencies of protein backbone 2JNC′ couplings

    PubMed Central

    Juranić, Nenad; Dannenberg, J.J.; Cornilescu, Gabriel; Salvador, Pedro; Atanasova, Elena; Ahn, Hee-Chul; Macura, Slobodan; Markley, John L.; Prendergast, Franklyn G.

    2008-01-01

    Protein folding can introduce strain in peptide covalent geometry, including deviations from planarity that are difficult to detect, especially for a protein in solution. We have found dependencies in protein backbone 2JNC′ couplings on the planarity and the relative orientation of the sequential peptide planes. These dependences were observed in experimental 2JNC′ couplings from seven proteins, and also were supported by DFT calculations for a model tripeptide. Findings indicate that elevated 2JNC′ couplings may serve as reporters of structural strain in the protein backbone imposed by protein folds. Such information, supplemented with the H-bond strengths derived from h3JNC′ couplings, provides useful insight into the overall energy profile of the protein backbone in solution. PMID:18305196

  12. 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.

  13. Delay, noise and phase locking in pulse coupled neural networks.

    PubMed

    Haken, H

    2001-01-01

    This paper studies the effect of several delay times and noise on the stability of the phase-locked state in the lighthouse model and the integrate and fire model of a pulse coupled neural network. The coupling between neurons may be arbitrary. In both models the increase of delay times leads to a weakening of the stability and to the occurrence of relaxation oscillations.

  14. Synchronization of Markovian coupled neural networks with nonidentical node-delays and random coupling strengths.

    PubMed

    Yang, Xinsong; Cao, Jinde; Lu, Jianquan

    2012-01-01

    In this paper, a general model of coupled neural networks with Markovian jumping and random coupling strengths is introduced. In the process of evolution, the proposed model switches from one mode to another according to a Markovian chain, and all the modes have different constant time-delays. The coupling strengths are characterized by mutually independent random variables. When compared with most of existing dynamical network models which share common time-delay for all modes and have constant coupling strengths, our model is more practical because different chaotic neural network models can have different time-delays and coupling strength of complex networks may randomly vary around a constant due to environmental and artificial factors. By designing a novel Lyapunov functional and using some inequalities and the properties of random variables, we derive several new sufficient synchronization criteria formulated by linear matrix inequalities. The obtained criteria depend on mode-delays and mathematical expectations and variances of the random coupling strengths as well. Numerical examples are given to demonstrate the effectiveness of the theoretical results, meanwhile right-continuous Markovian chain is also presented.

  15. Protein-protein interaction network of celiac disease

    PubMed Central

    Zamanian Azodi, Mona; Peyvandi, Hassan; Rostami-Nejad, Mohammad; Safaei, Akram; Rostami, Kamran; Vafaee, Reza; Heidari, Mohammadhossein; Hosseini, Mostafa; Zali, Mohammad Reza

    2016-01-01

    Aim: The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Background: Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. Material and methods: In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. Results: According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Conclusion: Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease. PMID:27895852

  16. Characterization and modeling of protein protein interaction networks

    NASA Astrophysics Data System (ADS)

    Colizza, Vittoria; Flammini, Alessandro; Maritan, Amos; Vespignani, Alessandro

    2005-07-01

    The recent availability of high-throughput gene expression and proteomics techniques has created an unprecedented opportunity for a comprehensive study of the structure and dynamics of many biological networks. Global proteomic interaction data, in particular, are synthetically represented as undirected networks exhibiting features far from the random paradigm which has dominated past effort in network theory. This evidence, along with the advances in the theory of complex networks, has triggered an intense research activity aimed at exploiting the evolutionary and biological significance of the resulting network's topology. Here we present a review of the results obtained in the characterization and modeling of the yeast Saccharomyces Cerevisiae protein interaction networks obtained with different experimental techniques. We provide a comparative assessment of the topological properties and discuss possible biases in interaction networks obtained with different techniques. We report on dynamical models based on duplication mechanisms that cast the protein interaction networks in the family of dynamically growing complex networks. Finally, we discuss various results and analysis correlating the networks’ topology with the biological function of proteins.

  17. Structural organization of G-protein-coupled receptors

    NASA Astrophysics Data System (ADS)

    Lomize, Andrei L.; Pogozheva, Irina D.; Mosberg, Henry I.

    1999-07-01

    Atomic-resolution structures of the transmembrane 7-α-helical domains of 26 G-protein-coupled receptors (GPCRs) (including opsins, cationic amine, melatonin, purine, chemokine, opioid, and glycoprotein hormone receptors and two related proteins, retinochrome and Duffy erythrocyte antigen) were calculated by distance geometry using interhelical hydrogen bonds formed by various proteins from the family and collectively applied as distance constraints, as described previously [Pogozheva et al., Biophys. J., 70 (1997) 1963]. The main structural features of the calculated GPCR models are described and illustrated by examples. Some of the features reflect physical interactions that are responsible for the structural stability of the transmembrane α-bundle: the formation of extensive networks of interhelical H-bonds and sulfur-aromatic clusters that are spatially organized as 'polarity gradients' the close packing of side-chains throughout the transmembrane domain; and the formation of interhelical disulfide bonds in some receptors and a plausible Zn2+ binding center in retinochrome. Other features of the models are related to biological function and evolution of GPCRs: the formation of a common 'minicore' of 43 evolutionarily conserved residues; a multitude of correlated replacements throughout the transmembrane domain; an Na+-binding site in some receptors, and excellent complementarity of receptor binding pockets to many structurally dissimilar, conformationally constrained ligands, such as retinal, cyclic opioid peptides, and cationic amine ligands. The calculated models are in good agreement with numerous experimental data.

  18. Evolution of protein-protein interaction networks in yeast.

    PubMed

    Schoenrock, Andrew; Burnside, Daniel; Moteshareie, Houman; Pitre, Sylvain; Hooshyar, Mohsen; Green, James R; Golshani, Ashkan; Dehne, Frank; Wong, Alex

    2017-01-01

    Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

  19. Evolution of protein-protein interaction networks in yeast

    PubMed Central

    Schoenrock, Andrew; Burnside, Daniel; Moteshareie, Houman; Pitre, Sylvain; Hooshyar, Mohsen; Green, James R.; Golshani, Ashkan; Dehne, Frank; Wong, Alex

    2017-01-01

    Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms. PMID:28248977

  20. Local dynamics of gap-junction-coupled interneuron networks

    NASA Astrophysics Data System (ADS)

    Lau, Troy; Gage, Gregory J.; Berke, Joshua D.; Zochowski, Michal

    2010-03-01

    Interneurons coupled by both electrical gap-junctions (GJs) and chemical GABAergic synapses are major components of forebrain networks. However, their contributions to the generation of specific activity patterns, and their overall contributions to network function, remain poorly understood. Here we demonstrate, using computational methods, that the topological properties of interneuron networks can elicit a wide range of activity dynamics, and either prevent or permit local pattern formation. We systematically varied the topology of GJ and inhibitory chemical synapses within simulated networks, by changing connection types from local to random, and changing the total number of connections. As previously observed we found that randomly coupled GJs lead to globally synchronous activity. In contrast, we found that local GJ connectivity may govern the formation of highly spatially heterogeneous activity states. These states are inherently temporally unstable when the input is uniformly random, but can rapidly stabilize when the network detects correlations or asymmetries in the inputs. We show a correspondence between this feature of network activity and experimental observations of transient stabilization of striatal fast-spiking interneurons (FSIs), in electrophysiological recordings from rats performing a simple decision-making task. We suggest that local GJ coupling enables an active search-and-select function of striatal FSIs, which contributes to the overall role of cortical-basal ganglia circuits in decision-making.

  1. The Fragility of Interdependency: Coupled Networks Switching Phenomena

    NASA Astrophysics Data System (ADS)

    Stanley, H. Eugene

    2013-03-01

    Recent disasters ranging from abrupt financial ``flash crashes'' and large-scale power outages to sudden death among the elderly dramatically exemplify the fact that the most dangerous vulnerability is hiding in the many interdependencies among different networks. In the past year, we have quantified failures in model of interconnected networks, and demonstrated the need to consider mutually dependent network properties in designing resilient systems. Specifically, we have uncovered new laws governing the nature of switching phenomena in coupled networks, and found that phenomena that are continuous ``second order'' phase transitions in isolated networks become discontinuous abrupt ``first order'' transitions in interdependent networks [S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, and S. Havlin, ``Catastrophic Cascade of Failures in Interdependent Networks,'' Nature 464, 1025 (2010); J. Gao, S. V. Buldyrev, H. E. Stanley, and S. Havlin, ``Novel Behavior of Networks Formed from Interdependent Networks,'' Nature Physics 8, 40 (2012). We conclude by discussing the network basis for understanding sudden death in the elderly, and the possibility that financial ``flash crashes'' are not unlike the catastrophic first-order failure incidents occurring in coupled networks. Specifically, we study the coupled networks that are responsible for financial fluctuations. It appears that ``trend switching phenomena'' that we uncover are remarkably independent of the scale over which they are analyzed. For example, we find that the same laws governing the formation and bursting of the largest financial bubbles also govern the tiniest finance bubbles, over a factor of 1,000,000,000 in time scale [T. Preis, J. Schneider, and H. E. Stanley, ``Switching Processes in Financial Markets,'' Proc. Natl. Acad. Sci. USA 108, 7674 (2011); T. Preis and H. E. Stanley, ``Bubble Trouble: Can a Law Describe Bubbles and Crashes in Financial Markets?'' Physics World 24, No. 5, 29 (May 2011

  2. Controllable coupling of distributed qubits within a microtoroidal cavity network

    NASA Astrophysics Data System (ADS)

    Hu, C.; Xia, Y.; Song, J.

    2012-05-01

    We propose a scheme to control the coupling between two arbitrary atoms scattered within a quantum network composed of microtoroidal cavities linked by a ring-fibre. The atom-atom effective couplings are induced by pairing of off-resonant Raman transitions. The couplings can be arbitrarily controlled by adjusting classical fields. Compared with the previous scheme [S.B. Zheng, C.P. Yang, F. Nori, Phys. Rev. A 82, 042327 (2010)], the present scheme uses microtoroidal cavities with higher coupling efficiency than Fabry-Perot cavities. Furthermore, the scheme is not only suitable for the short-fibre limit, but also for multiple fibre modes. The added fibre modes can play a positive role, especially when the coupling rate between cavity-mode and fibre-mode is not large. In addition, a wider frequency domain of fibre modes can be used in this scheme.

  3. Direct coevolutionary couplings reflect biophysical residue interactions in proteins

    NASA Astrophysics Data System (ADS)

    Coucke, Alice; Uguzzoni, Guido; Oteri, Francesco; Cocco, Simona; Monasson, Remi; Weigt, Martin

    2016-11-01

    Coevolution of residues in contact imposes strong statistical constraints on the sequence variability between homologous proteins. Direct-Coupling Analysis (DCA), a global statistical inference method, successfully models this variability across homologous protein families to infer structural information about proteins. For each residue pair, DCA infers 21 × 21 matrices describing the coevolutionary coupling for each pair of amino acids (or gaps). To achieve the residue-residue contact prediction, these matrices are mapped onto simple scalar parameters; the full information they contain gets lost. Here, we perform a detailed spectral analysis of the coupling matrices resulting from 70 protein families, to show that they contain quantitative information about the physico-chemical properties of amino-acid interactions. Results for protein families are corroborated by the analysis of synthetic data from lattice-protein models, which emphasizes the critical effect of sampling quality and regularization on the biochemical features of the statistical coupling matrices.

  4. Direct coevolutionary couplings reflect biophysical residue interactions in proteins.

    PubMed

    Coucke, Alice; Uguzzoni, Guido; Oteri, Francesco; Cocco, Simona; Monasson, Remi; Weigt, Martin

    2016-11-07

    Coevolution of residues in contact imposes strong statistical constraints on the sequence variability between homologous proteins. Direct-Coupling Analysis (DCA), a global statistical inference method, successfully models this variability across homologous protein families to infer structural information about proteins. For each residue pair, DCA infers 21 × 21 matrices describing the coevolutionary coupling for each pair of amino acids (or gaps). To achieve the residue-residue contact prediction, these matrices are mapped onto simple scalar parameters; the full information they contain gets lost. Here, we perform a detailed spectral analysis of the coupling matrices resulting from 70 protein families, to show that they contain quantitative information about the physico-chemical properties of amino-acid interactions. Results for protein families are corroborated by the analysis of synthetic data from lattice-protein models, which emphasizes the critical effect of sampling quality and regularization on the biochemical features of the statistical coupling matrices.

  5. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  6. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition

    PubMed Central

    Spreng, R. Nathan; Stevens, W. Dale; Chamberlain, Jon P.; Gilmore, Adrian W.; Schacter, Daniel L.

    2010-01-01

    Tasks that demand externalized attention reliably suppress default network activity while activating the dorsal attention network. These networks have an intrinsic competitive relationship; activation of one suppresses activity of the other. Consequently, many assume that default network activity is suppressed during goal-directed cognition. We challenge this assumption in an fMRI study of planning. Recent studies link default network activity with internally focused cognition, such as imagining personal future events, suggesting a role in autobiographical planning. However, it is unclear how goal-directed cognition with an internal focus is mediated by these opposing networks. A third anatomically interposed ‘frontoparietal control network’ might mediate planning across domains, flexibly coupling with either the default or dorsal attention network in support of internally versus externally focused goal-directed cognition, respectively. We tested this hypothesis by comparing brain activity during autobiographical versus visuospatial planning. Autobiographical planning engaged the default network, whereas visuospatial planning engaged the dorsal attention network, consistent with the anti-correlated domains of internalized and externalized cognition. Critically, both planning tasks engaged the frontoparietal control network. Task-related activation of these three networks was anatomically consistent with independently defined resting-state functional connectivity MRI maps. Together, our findings suggest that the default network can be involved in goal-directed cognition when its activity is coupled with the frontoparietal control network. Additionally, the frontoparietal control network may flexibly couple with the default and dorsal attention networks according to task domain, serving as a cortical mediator linking the two networks in support of goal-directed cognitive processes. PMID:20600998

  7. Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis.

    PubMed

    Guerrero, Cortnie; Milenkovic, Tijana; Przulj, Natasa; Kaiser, Peter; Huang, Lan

    2008-09-09

    Quantitative analysis of tandem-affinity purified cross-linked (x) protein complexes (QTAX) is a powerful technique for the identification of protein interactions, including weak and/or transient components. Here, we apply a QTAX-based tag-team mass spectrometry strategy coupled with protein network analysis to acquire a comprehensive and detailed assessment of the protein interaction network of the yeast 26S proteasome. We have determined that the proteasome network is composed of at least 471 proteins, significantly more than the total number of proteins identified by previous reports using proteasome subunits as baits. Validation of the selected proteasome-interacting proteins by reverse copurification and immunoblotting experiments with and without cross-linking, further demonstrates the power of the QTAX strategy for capturing protein interactions of all natures. In addition, >80% of the identified interactions have been confirmed by existing data using protein network analysis. Moreover, evidence obtained through network analysis links the proteasome to protein complexes associated with diverse cellular functions. This work presents the most complete analysis of the proteasome interaction network to date, providing an inclusive set of physical interaction data consistent with physiological roles for the proteasome that have been suggested primarily through genetic analyses. Moreover, the methodology described here is a general proteomic tool for the comprehensive study of protein interaction networks.

  8. Phase-lag synchronization in networks of coupled chemical oscillators.

    PubMed

    Totz, Jan F; Snari, Razan; Yengi, Desmond; Tinsley, Mark R; Engel, Harald; Showalter, Kenneth

    2015-08-01

    Chemical oscillators with a broad frequency distribution are photochemically coupled in network topologies. Experiments and simulations show that the network synchronization occurs by phase-lag synchronization of clusters of oscillators with zero- or nearly zero-lag synchronization. Symmetry also plays a role in the synchronization, the extent of which is explored as a function of coupling strength, frequency distribution, and the highest frequency oscillator location. The phase-lag synchronization occurs through connected synchronized clusters, with the highest frequency node or nodes setting the frequency of the entire network. The synchronized clusters successively "fire," with a constant phase difference between them. For low heterogeneity and high coupling strength, the synchronized clusters are made up of one or more clusters of nodes with the same permutation symmetries. As heterogeneity is increased or coupling strength decreased, the phase-lag synchronization occurs partially through clusters of nodes sharing the same permutation symmetries. As heterogeneity is further increased or coupling strength decreased, partial synchronization and, finally, independent unsynchronized oscillations are observed. The relationships between these classes of behavior are explored with numerical simulations, which agree well with the experimentally observed behavior.

  9. Cascades on a stochastic pulse-coupled network

    PubMed Central

    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

  10. Cascades on a stochastic pulse-coupled network

    NASA Astrophysics Data System (ADS)

    Wray, C. M.; Bishop, S. R.

    2014-09-01

    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.

  11. 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

  12. 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

    2015-01-20

    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.

  13. Lethality and centrality in protein networks

    NASA Astrophysics Data System (ADS)

    Jeong, H.; Mason, S. P.; Barabási, A.-L.; Oltvai, Z. N.

    2001-05-01

    Proteins are traditionally identified on the basis of their individual actions as catalysts, signalling molecules, or building blocks in cells and microorganisms. But our post-genomic view is expanding the protein's role into an element in a network of protein-protein interactions as well, in which it has a contextual or cellular function within functional modules. Here we provide quantitative support for this idea by demonstrating that the phenotypic consequence of a single gene deletion in the yeast Saccharomyces cerevisiae is affected to a large extent by the topological position of its protein product in the complex hierarchical web of molecular interactions.

  14. Geometric de-noising of protein-protein interaction networks.

    PubMed

    Kuchaiev, Oleksii; Rasajski, Marija; Higham, Desmond J; Przulj, Natasa

    2009-08-01

    Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.

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

    PubMed Central

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

    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.

  17. Chimera patterns in two-dimensional networks of coupled neurons

    NASA Astrophysics Data System (ADS)

    Schmidt, Alexander; Kasimatis, Theodoros; Hizanidis, Johanne; Provata, Astero; Hövel, Philipp

    2017-03-01

    We discuss synchronization patterns in networks of FitzHugh-Nagumo and leaky integrate-and-fire oscillators coupled in a two-dimensional toroidal geometry. A common feature between the two models is the presence of fast and slow dynamics, a typical characteristic of neurons. Earlier studies have demonstrated that both models when coupled nonlocally in one-dimensional ring networks produce chimera states for a large range of parameter values. In this study, we give evidence of a plethora of two-dimensional chimera patterns of various shapes, including spots, rings, stripes, and grids, observed in both models, as well as additional patterns found mainly in the FitzHugh-Nagumo system. Both systems exhibit multistability: For the same parameter values, different initial conditions give rise to different dynamical states. Transitions occur between various patterns when the parameters (coupling range, coupling strength, refractory period, and coupling phase) are varied. Many patterns observed in the two models follow similar rules. For example, the diameter of the rings grows linearly with the coupling radius.

  18. Chimera patterns in two-dimensional networks of coupled neurons.

    PubMed

    Schmidt, Alexander; Kasimatis, Theodoros; Hizanidis, Johanne; Provata, Astero; Hövel, Philipp

    2017-03-01

    We discuss synchronization patterns in networks of FitzHugh-Nagumo and leaky integrate-and-fire oscillators coupled in a two-dimensional toroidal geometry. A common feature between the two models is the presence of fast and slow dynamics, a typical characteristic of neurons. Earlier studies have demonstrated that both models when coupled nonlocally in one-dimensional ring networks produce chimera states for a large range of parameter values. In this study, we give evidence of a plethora of two-dimensional chimera patterns of various shapes, including spots, rings, stripes, and grids, observed in both models, as well as additional patterns found mainly in the FitzHugh-Nagumo system. Both systems exhibit multistability: For the same parameter values, different initial conditions give rise to different dynamical states. Transitions occur between various patterns when the parameters (coupling range, coupling strength, refractory period, and coupling phase) are varied. Many patterns observed in the two models follow similar rules. For example, the diameter of the rings grows linearly with the coupling radius.

  19. 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

  20. Modelling small-patterned neuronal networks coupled to microelectrode arrays

    NASA Astrophysics Data System (ADS)

    Massobrio, Paolo; Martinoia, Sergio

    2008-09-01

    Cultured neurons coupled to planar substrates which exhibit 'well-defined' two-dimensional network architectures can provide valuable insights into cell-to-cell communication, network dynamics versus topology, and basic mechanisms of synaptic plasticity and learning. In the literature several approaches were presented to drive neuronal growth, such as surface modification by silane chemistry, photolithographic techniques, microcontact printing, microfluidic channel flow patterning, microdrop patterning, etc. This work presents a computational model fit for reproducing and explaining the dynamics exhibited by small-patterned neuronal networks coupled to microelectrode arrays (MEAs). The model is based on the concept of meta-neuron, i.e., a small spatially confined number of actual neurons which perform single macroscopic functions. Each meta-neuron is characterized by a detailed morphology, and the membrane channels are modelled by simple Hodgkin-Huxley and passive kinetics. The two main findings that emerge from the simulations can be summarized as follows: (i) the increasing complexity of meta-neuron morphology reflects the variations of the network dynamics as a function of network development; (ii) the dynamics displayed by the patterned neuronal networks considered can be explained by hypothesizing the presence of several short- and a few long-term distance interactions among small assemblies of neurons (i.e., meta-neurons).

  1. Modelling small-patterned neuronal networks coupled to microelectrode arrays.

    PubMed

    Massobrio, Paolo; Martinoia, Sergio

    2008-09-01

    Cultured neurons coupled to planar substrates which exhibit 'well-defined' two-dimensional network architectures can provide valuable insights into cell-to-cell communication, network dynamics versus topology, and basic mechanisms of synaptic plasticity and learning. In the literature several approaches were presented to drive neuronal growth, such as surface modification by silane chemistry, photolithographic techniques, microcontact printing, microfluidic channel flow patterning, microdrop patterning, etc. This work presents a computational model fit for reproducing and explaining the dynamics exhibited by small-patterned neuronal networks coupled to microelectrode arrays (MEAs). The model is based on the concept of meta-neuron, i.e., a small spatially confined number of actual neurons which perform single macroscopic functions. Each meta-neuron is characterized by a detailed morphology, and the membrane channels are modelled by simple Hodgkin-Huxley and passive kinetics. The two main findings that emerge from the simulations can be summarized as follows: (i) the increasing complexity of meta-neuron morphology reflects the variations of the network dynamics as a function of network development; (ii) the dynamics displayed by the patterned neuronal networks considered can be explained by hypothesizing the presence of several short- and a few long-term distance interactions among small assemblies of neurons (i.e., meta-neurons).

  2. 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

  3. 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.

  4. 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

  5. G protein-coupled receptors provide survival signals in prostate cancer.

    PubMed

    Yowell, Charles W; Daaka, Yehia

    2002-12-01

    Prostate cancer is the leading cause for noncutaneous cancer-related deaths among men in the United States. The disease is biologically characterized as being either androgen dependent or androgen independent. Whereas androgen-dependent prostate cancer can be successfully treated with androgen ablative therapy, to date no cure exists for androgen-independent disease. Mechanisms involved in the progression of prostate cancer to androgen independence are not known. Here we present evidence that in addition to growth factor receptor tyrosine kinases, G protein- coupled receptors can mediate survival signals in prostate cancer cells. The G protein- coupled receptors exert their effects by activating multiple intracellular signal transduction networks that promote prostate cancer cell survival, including the activation of c-Jun N-terminal kinase, protein kinase B (Akt) and nuclear factor-kB. Prostate-expressed G protein- coupled receptors and their downstream effectors may prove to be effective targets in the treatment of advanced prostate cancer.

  6. Molecular dynamics study of naturally existing cavity couplings in proteins.

    PubMed

    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 (≥100 ns) 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.

  7. FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

    PubMed

    Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

    2014-10-01

    A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

  8. Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms.

    PubMed

    Jafari, Mohieddin; Mirzaie, Mehdi; Sadeghi, Mehdi

    2015-10-05

    In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary. Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs). The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome. In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.

  9. Characterization of essential proteins based on network topology in proteins interaction networks

    NASA Astrophysics Data System (ADS)

    Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

    2014-06-01

    The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

  10. 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

  11. Biased imitation in coupled evolutionary games in interdependent networks

    PubMed Central

    Santos, M. D.; Dorogovtsev, S. N.; Mendes, J. F. F.

    2014-01-01

    We explore the evolutionary dynamics of two games—the Prisoner's Dilemma and the Snowdrift Game—played within distinct networks (layers) of interdependent networks. In these networks imitation and interaction between individuals of opposite layers is established through interlinks. We explore an update rule in which revision of strategies is a biased imitation process: individuals imitate neighbors from the same layer with probability p, and neighbors from the second layer with complementary probability 1 − p. We demonstrate that a small decrease of p from p = 1 (which corresponds to forbidding strategy transfer between layers) is sufficient to promote cooperation in the Prisoner's Dilemma subpopulation. This, on the other hand, is detrimental for cooperation in the Snowdrift Game subpopulation. We provide results of extensive computer simulations for the case in which layers are modelled as regular random networks, and support this study with analytical results for coupled well-mixed populations. PMID:24658580

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

    PubMed

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-05-01

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

  14. Controlling Directed Protein Interaction Networks in Cancer.

    PubMed

    Kanhaiya, Krishna; Czeizler, Eugen; Gratie, Cristian; Petre, Ion

    2017-09-04

    Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

  15. Study of G-protein-coupled receptor-protein interactions by bioluminescence resonance energy transfer.

    PubMed

    Kroeger, Karen M; Eidne, Karin A

    2004-01-01

    Complex networks of protein-protein interactions are key determinants of cellular function, including those regulated by G-protein-coupled receptors (GPCRs). Formation of either stable or transitory complexes are involved in regulating all aspects of receptor function, from ligand binding through to signal transduction, desensitization, resensitization and downregulation. Today, 50% of all recently launched drugs are targeted against GPCRs. This particular class of proteins is extremely useful as a drug target because the receptors are partly located outside the cell, simplifying bioavailability and delivery of drugs directed against them. However, being located within the cell membrane causes difficulties for the study of GPCR function and bioluminescence resonance energy transfer (BRET), a naturally occurring phenomenon, represents a newly emerging, powerful tool with which to investigate and monitor dynamic interactions involving this receptor class. BRET is a noninvasive, highly sensitive technique, performed as a simple homogeneous assay. involving the proximity-dependent transfer of energy from an energy donor to acceptor resulting in the emission of light. This technology has several advantages over alternative approaches as the detection occurs within live cells, in real time, and is not restricted to a particular cellular compartment. The use of such biophysical techniques as BRET, will not only increase our understanding of the nature of GPCR regulation and the protein complexes involved, but could also potentially lead to the development of novel therapeutics that modulate these interactions.

  16. Correlated loss of ecosystem services in coupled mutualistic networks.

    PubMed

    Albrecht, Jörg; Berens, Dana Gertrud; Jaroszewicz, Bogdan; Selva, Nuria; Brandl, Roland; Farwig, Nina

    2014-05-08

    Networks of species interactions promote biodiversity and provide important ecosystem services. These networks have traditionally been studied in isolation, but species are commonly involved in multiple, diverse types of interaction. Therefore, whether different types of species interaction networks coupled through shared species show idiosyncratic or correlated responses to habitat degradation is unresolved. Here we study the collective response of coupled mutualistic networks of plants and their pollinators and seed dispersers to the degradation of Europe's last relict of old-growth lowland forest (Białowieża, Poland). We show that logging of old-growth forests has correlated effects on the number of partners and interactions of plants in both mutualisms, and that these effects are mediated by shifts in plant densities on logged sites. These results suggest bottom-up-controlled effects of habitat degradation on plant-animal mutualistic networks, and predict that the conversion of primary old-growth forests to secondary habitats may cause a parallel loss of multiple animal-mediated ecosystem services.

  17. An extensive network of coupling among gene expression machines.

    PubMed

    Maniatis, Tom; Reed, Robin

    2002-04-04

    Gene expression in eukaryotes requires several multi-component cellular machines. Each machine carries out a separate step in the gene expression pathway, which includes transcription, several pre-messenger RNA processing steps and the export of mature mRNA to the cytoplasm. Recent studies lead to the view that, in contrast to a simple linear assembly line, a complex and extensively coupled network has evolved to coordinate the activities of the gene expression machines. The extensive coupling is consistent with a model in which the machines are tethered to each other to form 'gene expression factories' that maximize the efficiency and specificity of each step in gene expression.

  18. Design principles of protein biosynthesis-coupled quality control.

    PubMed

    Rodrigo-Brenni, Monica C; Hegde, Ramanujan S

    2012-11-13

    The protein biosynthetic machinery, composed of ribosomes, chaperones, and localization factors, is increasingly found to interact directly with factors dedicated to protein degradation. The coupling of these two opposing processes facilitates quality control of nascent polypeptides at each stage of their maturation. Sequential checkpoints maximize the overall fidelity of protein maturation, minimize the exposure of defective products to the bulk cellular environment, and protect organisms from protein misfolding diseases. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. 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.

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

    PubMed

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

    2016-07-06

    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.

  1. 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

  2. Eukaryotic G Protein Signaling Evolved to Require G Protein–Coupled Receptors for Activation

    PubMed Central

    Bradford, William; Buckholz, Adam; Morton, John; Price, Collin; Jones, Alan M.; Urano, Daisuke

    2016-01-01

    Although bioinformatic analysis of the increasing numbers of diverse genome sequences and amount of functional data has provided insight into the evolution of signaling networks, bioinformatics approaches have limited application for understanding the evolution of highly divergent protein families. We used biochemical analyses to determine the in vitro properties of selected divergent components of the heterotrimeric guanine nucleotide–binding protein (G protein) signaling network to investigate signaling network evolution. In animals, G proteins are activated by cell-surface seven-transmembrane (7TM) receptors, which are named G protein–coupled receptors (GPCRs) and function as guanine nucleotide exchange factors (GEFs). In contrast, the plant G protein is intrinsically active, and a 7TM protein terminates G protein activity by functioning as a guanosine triphosphatase–activating protein (GAP). We showed that ancient regulation of the G protein active state is GPCR-independent and “self-activating,” a property that is maintained in Bikonts, one of the two fundamental evolutionary clades containing eukaryotes, whereas G proteins of the other clade, the Unikonts, evolved from being GEF-independent to being GEF-dependent. Self-activating G proteins near the base of the Eukaryota are controlled by 7TM-GAPs, suggesting that the ancestral regulator of G protein activation was a GAP-functioning receptor, not a GEF-functioning GPCR. Our findings indicate that the GPCR paradigm describes a recently evolved network architecture found in a relatively small group of Eukaryota and suggest that the evolution of signaling network architecture is constrained by the availability of molecules that control the activation state of nexus proteins. PMID:23695163

  3. Spectral reconstruction of protein contact networks

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

    In this work, we present a method for generating an adjacency matrix encoding a typical protein contact network. This work constitutes a follow-up to our recent work (Livi et al., 2015), whose aim was to estimate the relative contribution of different topological features in discovering of the unique properties of protein structures. We perform a genetic algorithm based optimization in order to modify the matrices generated with the procedures explained in (Livi et al., 2015). Our objective here is to minimize the distance with respect to a target spectral density, which is elaborated using the normalized graph Laplacian representation of graphs. Such a target density is obtained by averaging the kernel-estimated densities of a class of experimental protein maps having different dimensions. This is possible given the bounded-domain property of the normalized Laplacian spectrum. By exploiting genetic operators designed for this specific problem and an exponentially-weighted objective function, we are able to reconstruct adjacency matrices representing networks of varying size whose spectral density is indistinguishable from the target. The topological features of the optimized networks are then compared to the real protein contact networks and they show an increased similarity with respect to the starting networks. Subsequently, the statistical properties of the spectra of the newly generated matrices are analyzed by employing tools borrowed from random matrix theory. The nearest neighbors spacing distribution of the spectra of the generated networks indicates that also the (short-range) correlations of the Laplacian eigenvalues are compatible with those of real proteins.

  4. Application of heterogeneous pulse coupled neural network in image quantization

    NASA Astrophysics Data System (ADS)

    Huang, Yi; Ma, Yide; Li, Shouliang; Zhan, Kun

    2016-11-01

    On the basis of the different strengths of synaptic connections between actual neurons, this paper proposes a heterogeneous pulse coupled neural network (HPCNN) algorithm to perform quantization on images. HPCNNs are developed from traditional pulse coupled neural network (PCNN) models, which have different parameters corresponding to different image regions. This allows pixels of different gray levels to be classified broadly into two categories: background regional and object regional. Moreover, an HPCNN also satisfies human visual characteristics. The parameters of the HPCNN model are calculated automatically according to these categories, and quantized results will be optimal and more suitable for humans to observe. At the same time, the experimental results of natural images from the standard image library show the validity and efficiency of our proposed quantization method.

  5. Isochronous dynamics in pulse coupled oscillator networks with delay

    NASA Astrophysics Data System (ADS)

    Li, Pan; Lin, Wei; Efstathiou, Konstantinos

    2017-05-01

    We consider a network of identical pulse-coupled oscillators with delay and all-to-all coupling. We demonstrate that the discontinuous nature of the dynamics induces the appearance of isochronous regions—subsets of the phase space filled with periodic orbits having the same period. For each fixed value of the network parameters, such an isochronous region corresponds to a subset of initial states on an appropriate surface of section with non-zero dimensions such that all periodic orbits in this set have qualitatively similar dynamical behaviour. We analytically and numerically study in detail such an isochronous region, give proof of its existence, and describe its properties. We further describe other isochronous regions that appear in the system.

  6. 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.

  7. 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.

  8. 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

  9. Inherent features of wavelets and pulse coupled networks.

    PubMed

    Lindblad, T; Kinser, J M

    1999-01-01

    Biologically inspired image/signal processing like the pulse coupled neural network (PCNN) and the wavelet (packet) transforms are described. The two methods are applied to two-dimensional data in order to demonstrate the features of each method, pinpoint differences as well as similarities. The inherent properties (with respect to filtering, segmentation, etc.) of the two approaches with respect to detectors for physics experiments as well as remote sensing are discussed.

  10. 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.

  11. Reconstructing networks of pulse-coupled oscillators from spike trains

    NASA Astrophysics Data System (ADS)

    Cestnik, Rok; Rosenblum, Michael

    2017-07-01

    We present an approach for reconstructing networks of pulse-coupled neuronlike oscillators from passive observation of pulse trains of all nodes. It is assumed that units are described by their phase response curves and that their phases are instantaneously reset by incoming pulses. Using an iterative procedure, we recover the properties of all nodes, namely their phase response curves and natural frequencies, as well as strengths of all directed connections.

  12. 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.

  13. Analysis of firing behaviors in networks of pulse-coupled oscillators with delayed excitatory coupling.

    PubMed

    Wu, Wei; Liu, Bo; Chen, Tianping

    2010-09-01

    In this paper, we investigate the firing behaviors in networks of pulse-coupled oscillators with delayed excitatory coupling according to the coupling strength epsilon and delay tau. We find out that the parameter space A={(epsilon,tau)|0network will have apparently different firing properties on them. In A(1), the firing behavior is relatively simple for rigorous analysis, while it is more complicated on A(2). First, we show that the delay tau is a lower bound for the inter-spike intervals of each oscillator on A(1). Using this lower bound, three important properties of the firing on A(1) are obtained: (a) Any complete synchronized solution is a solution with period 1; (b) If two oscillators fire at same time, and they have the same coupling strength from each other and from other oscillators, then, they will be synchronized forever; (c) The firing order of two oscillators is always preserved. However, examples can be provided to show that these properties do not hold for the region A(2). Yet, numerical simulation still reveals some interesting phenomenon on A(2): (a) Completely synchronized solutions are prevalent; (b) Given (tau,epsilon)inA(2), the fraction of the initial values that will lead to complete synchronization will converge along with increasing network size.

  14. A Protein Complex Network of Drosophila melanogaster

    PubMed Central

    Guruharsha, K. G.; Rual, J. -F.; Zhai, B.; Mintseris, J.; Vaidya, P.; Vaidya, N.; Beekman, C.; Wong, C.; Rhee, D. Y.; Cenaj, O.; McKillip, E.; Shah, S.; Stapleton, M.; Wan, K. H.; Yu, C.; Parsa, B.; Carlson, J. W.; Chen, X.; Kapadia, B.; VijayRaghavan, K.; Gygi, S. P.; Celniker, S. E.; Obar, R. A.; Artavanis-Tsakonas, S.

    2011-01-01

    SUMMARY Determining the composition of protein complexes is an essential step towards understanding the cell as an integrated system. Using co-affinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly five thousand individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a novel statistical framework to define individual protein-protein interactions, led to the generation of a Drosophila Protein interaction Map (DPiM) encompassing 556 protein complexes. The high quality of DPiM and its usefulness as a paradigm for metazoan proteomes is apparent from the recovery of many known complexes, significant enrichment for shared functional attributes and validation in human cells. DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution. PMID:22036573

  15. The Modular Structure of Protein Networks

    NASA Astrophysics Data System (ADS)

    Rozenfeld, Hernán D.; Rybski, Diego; Havlin, Shlomo; Makse, Hernán A.

    2008-03-01

    The evolution of the human protein homology network (H-PHN) has led to a complex network that exhibits a surprisingly high level of modularity. Topologically, the H-PHN presents well connected groups (conformed by proteins of similar aminoacid structure) and weak connectivities between the groups. Here, we perform an empirical study of the H-PHN to characterize the degree of modularity in terms of scale-invariant laws using recently introduced box covering algorithms. We find that the exponent that determines the scale-invariance of the modularity is unexpectedly higher than the box dimension of the network. In addition, we perform a percolation analysis that gives insight into the evolutionary process that led to the modular organization and dynamics of the present H-PHN.

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

    SciTech Connect

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana

    2016-09-15

    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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. Linear stability in networks of pulse-coupled neurons.

    PubMed

    Olmi, Simona; Torcini, Alessandro; Politi, Antonio

    2014-01-01

    In a first step toward the comprehension of neural activity, one should focus on the stability of the possible dynamical states. Even the characterization of an idealized regime, such as that of a perfectly periodic spiking activity, reveals unexpected difficulties. In this paper we discuss a general approach to linear stability of pulse-coupled neural networks for generic phase-response curves and post-synaptic response functions. In particular, we present: (1) a mean-field approach developed under the hypothesis of an infinite network and small synaptic conductances; (2) a "microscopic" approach which applies to finite but large networks. As a result, we find that there exist two classes of perturbations: those which are perfectly described by the mean-field approach and those which are subject to finite-size corrections, irrespective of the network size. The analysis of perfectly regular, asynchronous, states reveals that their stability depends crucially on the smoothness of both the phase-response curve and the transmitted post-synaptic pulse. Numerical simulations suggest that this scenario extends to systems that are not covered by the perturbative approach. Altogether, we have described a series of tools for the stability analysis of various dynamical regimes of generic pulse-coupled oscillators, going beyond those that are currently invoked in the literature.

  3. 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.

  4. 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.

  5. Two networks of electrically coupled inhibitory neurons in neocortex

    NASA Astrophysics Data System (ADS)

    Gibson, Jay R.; Beierlein, Michael; Connors, Barry W.

    1999-11-01

    Inhibitory interneurons are critical to sensory transformations, plasticity and synchronous activity in the neocortex. There are many types of inhibitory neurons, but their synaptic organization is poorly understood. Here we describe two functionally distinct inhibitory networks comprising either fast-spiking (FS) or low-threshold spiking (LTS) neurons. Paired-cell recordings showed that inhibitory neurons of the same type were strongly interconnected by electrical synapses, but electrical synapses between different inhibitory cell types were rare. The electrical synapses were strong enough to synchronize spikes in coupled interneurons. Inhibitory chemical synapses were also common between FS cells, and between FS and LTS cells, but LTS cells rarely inhibited one another. Thalamocortical synapses, which convey sensory information to the cortex, specifically and strongly excited only the FS cell network. The electrical and chemical synaptic connections of different types of inhibitory neurons are specific, and may allow each inhibitory network to function independently.

  6. 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

  7. The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

    PubMed Central

    Rattei, Thomas; Makse, Hernán A.

    2013-01-01

    Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI

  8. Mixed outer synchronization of coupled complex networks with time-varying coupling delay.

    PubMed

    Wang, Jun-Wei; Ma, Qinghua; Zeng, Li; Abd-Elouahab, Mohammed Salah

    2011-03-01

    In this paper, the problem of outer synchronization between two complex networks with the same topological structure and time-varying coupling delay is investigated. In particular, we introduce a new type of outer synchronization behavior, i.e., mixed outer synchronization (MOS), in which different state variables of the corresponding nodes can evolve into complete synchronization, antisynchronization, and even amplitude death simultaneously for an appropriate choice of the scaling matrix. A novel nonfragile linear state feedback controller is designed to realize the MOS between two networks and proved analytically by using Lyapunov-Krasovskii stability theory. Finally, numerical simulations are provided to demonstrate the feasibility and efficacy of our proposed control approach.

  9. 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

  10. Predicting the fission yeast protein interaction network.

    PubMed

    Pancaldi, Vera; Saraç, Omer S; Rallis, Charalampos; McLean, Janel R; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-04-01

    A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein-protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70-80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).

  11. Protein cooperation: from neurons to networks.

    PubMed

    Volonté, Cinzia; D'Ambrosi, Nadia; Amadio, Susanna

    2008-10-01

    A constant pattern through the development of cellular life is that not only cells but also subcellular components such as proteins, either being enzymes, receptors, signaling or structural proteins, strictly cooperate. Discerning how protein cooperation originated and propagates over evolutionary time, how proteins work together to a shared outcome far beyond mere interaction, thus represents a theoretical and experimental challenge for evolutionary, molecular, and computational biology, and a timely fruition also for biotechnology. In this review, we describe some basic principles sustaining not only cellular but especially protein cooperative behavior, with particular emphasis on neurobiological systems. We illustrate experimental results and numerical models substantiating that bench research, as well as computer analysis, indeed concurs in recognizing the natural propensity of proteins to cooperate. At the cellular level, we exemplify network connectivity in the thalamus, hippocampus and basal ganglia. At the protein level, we depict numerical models about the receptosome, the protein machinery connecting neurotransmitters or growth factors to specific, unique downstream effector proteins. We primarily focus on the purinergic P2/P1 receptor systems for extracellular purine and pyrimidine nucleotides/nucleosides. By spanning concepts such as single-molecule biology to membrane computing, we seek to stimulate a scientific debate on the implications of protein cooperation in neurobiological systems.

  12. Drug Target Protein-Protein Interaction Networks: A Systematic Perspective.

    PubMed

    Feng, Yanghe; Wang, Qi; Wang, Tengjiao

    2017-01-01

    The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target's homologue set containing 102 potential target proteins is predicted in the paper.

  13. Designed Proteins To Modulate Cellular Networks

    PubMed Central

    Cortajarena, Aitziber L.; Liu, Tina Y.; Hochstrasser, Mark; Regan, Lynne

    2012-01-01

    A major challenge of protein design is to create useful new proteins that interact specifically with biological targets in living cells. Such binding modules have many potential applications, including the targeted perturbation of protein networks. As a general approach to create such modules, we designed a library with approximately 109 different binding specificities based on a small 3-tetratricopeptide repeat (TPR) motif framework. We employed a novel strategy, based on split GFP reassembly, to screen the library for modules with the desired binding specificity. Using this approach, we identified modules that bind tightly and specifically to Dss1, a small human protein that interacts with the tumor suppressor protein BRCA2. We showed that these modules also bind the yeast homologue of Dss1, Sem1. Furthermore, we demonstrated that these modules inhibit Sem1 activity in yeast. This strategy will be generally applicable to make novel genetically encoded tools for systems/synthetic biology applications. PMID:20020775

  14. 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

  15. 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.

  16. Dynamics of hydration water and coupled protein sidechains around a polymerase protein surface

    NASA Astrophysics Data System (ADS)

    Qin, Yangzhong; Yang, Yi; Wang, Lijuan; Zhong, Dongping

    2017-09-01

    Water-protein coupled interactions are essential to the protein structural stability, flexibility and dynamic functions. The ultimate effects of the hydration dynamics on the protein fluctuations remain substantially unexplored. Here, we investigated the dynamics of both hydration water and protein sidechains at 13 different sites around the polymerase β protein surface using a tryptophan scan with femtosecond spectroscopy. Three types of hydration-water relaxations and two types of protein sidechain motions were determined, reflecting a highly dynamic water-protein interactions fluctuating on the picosecond time scales. The hydration-water dynamics dominate the coupled interactions with higher flexibility.

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

    PubMed

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

    2016-01-01

    Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. 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. Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. 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. 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.

  18. 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

  19. Fabrication and application of G protein-coupled receptor microarrays.

    PubMed

    Fang, Ye; Webb, Brian; Hong, Yulong; Ferrie, Ann; Lai, Fang; Frutos, Anthony G; Lahiri, Joydeep

    2004-01-01

    The increased number of drug targets and compounds demands novel high-throughput screening technologies that could be used for parallel analysis of many genes and proteins. Protein microarrays are evolving promising technologies for the parallel analysis of many proteins with respect to their abundance, location, modifications, and interactions with other biological and chemical molecules. This chapter specifically describes the fabrication of G protein-coupled receptor (GPCR) microarrays, a unique subset of protein microarrays, using contact-pin printing technology. The bioassays and potential applications of GPCR microarrays for the determination of compound affinities and potencies are also included.

  20. 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.

  1. Detecting effective connectivity in networks of coupled neuronal oscillators.

    PubMed

    Boykin, Erin R; Khargonekar, Pramod P; Carney, Paul R; Ogle, William O; Talathi, Sachin S

    2012-06-01

    The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

  2. Ising models of strongly coupled biological networks with multivariate interactions

    NASA Astrophysics Data System (ADS)

    Merchan, Lina; Nemenman, Ilya

    2013-03-01

    Biological networks consist of a large number of variables that can be coupled by complex multivariate interactions. However, several neuroscience and cell biology experiments have reported that observed statistics of network states can be approximated surprisingly well by maximum entropy models that constrain correlations only within pairs of variables. We would like to verify if this reduction in complexity results from intricacies of biological organization, or if it is a more general attribute of these networks. We generate random networks with p-spin (p > 2) interactions, with N spins and M interaction terms. The probability distribution of the network states is then calculated and approximated with a maximum entropy model based on constraining pairwise spin correlations. Depending on the M/N ratio and the strength of the interaction terms, we observe a transition where the pairwise approximation is very good to a region where it fails. This resembles the sat-unsat transition in constraint satisfaction problems. We argue that the pairwise model works when the number of highly probable states is small. We argue that many biological systems must operate in a strongly constrained regime, and hence we expect the pairwise approximation to be accurate for a wide class of problems. This research has been partially supported by the James S McDonnell Foundation grant No.220020321.

  3. Investigating the validity of current network analysis on static conglomerate networks by protein network stratification.

    PubMed

    Zhang, Minlu; Lu, Long J

    2010-09-16

    A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems. In this study, we employed a network stratification strategy to investigate the validity of the current network analysis on conglomerate PPI networks. Using the genome-scale tissue- and condition-specific proteomics data in Arabidopsis thaliana, we present here the first systematic investigation into this question. We stratified a conglomerate A. thaliana PPI network into three levels of context-dependent subnetworks. We then focused on three types of most commonly conducted network analyses, i.e., topological, functional and modular analyses, and compared the results from these network analyses on the conglomerate network and five stratified context-dependent subnetworks corresponding to specific tissues. We found that the results based on the conglomerate PPI network are often significantly different from those of context-dependent subnetworks corresponding to specific tissues or conditions. This conclusion depends neither on relatively arbitrary cutoffs (such as those defining network hubs or bottlenecks), nor on specific network clustering algorithms for module extraction, nor on the possible high false positive rates of binary interactions in PPI networks. We also found that our conclusions are likely to be valid in human PPI networks. Furthermore, network stratification may help resolve many controversies in current research of systems biology.

  4. Investigating the validity of current network analysis on static conglomerate networks by protein network stratification

    PubMed Central

    2010-01-01

    Background A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems. Results In this study, we employed a network stratification strategy to investigate the validity of the current network analysis on conglomerate PPI networks. Using the genome-scale tissue- and condition-specific proteomics data in Arabidopsis thaliana, we present here the first systematic investigation into this question. We stratified a conglomerate A. thaliana PPI network into three levels of context-dependent subnetworks. We then focused on three types of most commonly conducted network analyses, i.e., topological, functional and modular analyses, and compared the results from these network analyses on the conglomerate network and five stratified context-dependent subnetworks corresponding to specific tissues. Conclusions We found that the results based on the conglomerate PPI network are often significantly different from those of context-dependent subnetworks corresponding to specific tissues or conditions. This conclusion depends neither on relatively arbitrary cutoffs (such as those defining network hubs or bottlenecks), nor on specific network clustering algorithms for module extraction, nor on the possible high false positive rates of binary interactions in PPI networks. We also found that our conclusions are likely to be valid in human PPI networks. Furthermore, network stratification may help resolve many controversies in current research of systems biology. PMID:20846443

  5. Predicting the Fission Yeast Protein Interaction Network

    PubMed Central

    Pancaldi, Vera; Saraç, Ömer S.; Rallis, Charalampos; McLean, Janel R.; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-01-01

    A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt). PMID:22540037

  6. 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

  7. Toponomics: studying protein-protein interactions and protein networks in intact tissue.

    PubMed

    Pierre, Sandra; Scholich, Klaus

    2010-04-01

    The function of a protein is determined on several levels including the genome, transcriptome, proteome, and the recently introduced toponome. The toponome describes the topology of all proteins, protein complexes and protein networks which constitute and influence the microenvironment of a given protein. It has long been known that cellular function or dysfunction of proteins strongly depends on their microenvironment and even small changes in protein arrangements can dramatically alter their activity/function. Thus, deciphering the topology of the multi-dimensional networks which control normal and disease-related pathways will give a better understanding of the mechanisms underlying disease development. While various powerful proteomic tools allow simultaneous quantification of proteins, only a limited number of techniques are available to visualize protein networks in intact cells and tissues. This review discusses a novel approach to map and decipher functional molecular networks of proteins in intact cells or tissues. Multi-epitope-ligand-cartography (MELC) is an imaging technology that identifies and quantifies protein networks at the subcellular level of morphologically-intact specimens. This immunohistochemistry-based method allows serial visualization and biomathematical analysis of up to 100 cellular components using fluorescence-labelled tags. The resulting toponome maps, simultaneously ranging from the subcellular to the supracellular scale, have the potential to provide the basis for a mathematical description of the dynamic topology of protein networks, and will complement current proteomic data to enhance the understanding of physiological and pathophysiological cell functions.

  8. A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

    PubMed

    Birlutiu, Adriana; d'Alché-Buc, Florence; Heskes, Tom

    2015-01-01

    Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

  9. Evolutionary Capacitance and Control of Protein Stability in Protein-Protein Interaction Networks

    PubMed Central

    Dixit, Purushottam D.; Maslov, Sergei

    2013-01-01

    In addition to their biological function, protein complexes reduce the exposure of the constituent proteins to the risk of undesired oligomerization by reducing the concentration of the free monomeric state. We interpret this reduced risk as a stabilization of the functional state of the protein. We estimate that protein-protein interactions can account for of additional stabilization; a substantial contribution to intrinsic stability. We hypothesize that proteins in the interaction network act as evolutionary capacitors which allows their binding partners to explore regions of the sequence space which correspond to less stable proteins. In the interaction network of baker's yeast, we find that statistically proteins that receive higher energetic benefits from the interaction network are more likely to misfold. A simplified fitness landscape wherein the fitness of an organism is inversely proportional to the total concentration of unfolded proteins provides an evolutionary justification for the proposed trends. We conclude by outlining clear biophysical experiments to test our predictions. PMID:23592969

  10. Protein phosphorylation networks in motor neuron death.

    PubMed

    Hu, Jie Hong; Krieger, Charles

    2002-01-01

    The disorder amyotrophic lateral sclerosis (ALS) is characterized by the death of specific groups of neurons, especially motor neurons, which innervate skeletal muscle, and neurons connecting the cerebral cortex with motor neurons, such as corticospinal tract neurons. There have been numerous attempts to elucidate why there is selective involvement of motor neurons in ALS. Recent observations have demonstrated altered activities and protein levels of diverse kinases in the brain and spinal cord of transgenic mice that overexpress a mutant superoxide dismutase (mSOD) gene that is found in patients with the familial form of ALS, as well as in patients who have died with ALS. These results suggest that the alteration of protein phosphorylation may be involved in the pathogenesis of ALS. The changes in protein kinase and phosphatase expression and activity can affect the activation of important neuronal neurotransmitter receptors such as NMDA receptors or other signaling proteins and can trigger, or modify, the process producing neuronal loss in ALS. These various kinases, phosphatases and signaling proteins are involved in many signaling pathways; however, they have close interactions with each other. Therefore, an understanding of the role of protein kinases and protein phosphatases and the molecular organization of protein phosphorylation networks are useful to determine the mechanisms of selective motor neuron death.

  11. 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

  12. 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.

  13. 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

  14. Phase-locked regimes in delay-coupled oscillator networks

    NASA Astrophysics Data System (ADS)

    Punetha, Nirmal; Prasad, Awadhesh; Ramaswamy, Ramakrishna

    2014-12-01

    For an ensemble of globally coupled oscillators with time-delayed interactions, an explicit relation for the frequency of synchronized dynamics corresponding to different phase behaviors is obtained. One class of solutions corresponds to globally synchronized in-phase oscillations. The other class of solutions have mixed phases, and these can be either randomly distributed or can be a splay state, namely with phases distributed uniformly on a circle. In the strong coupling limit and for larger networks, the in-phase synchronized configuration alone remains. Upon variation of the coupling strength or the size of the system, the frequency can change discontinuously, when there is a transition from one class of solutions to another. This can be from the in-phase state to a mixed-phase state, but can also occur between two in-phase configurations of different frequency. Analytical and numerical results are presented for coupled Landau-Stuart oscillators, while numerical results are shown for Rössler and FitzHugh-Nagumo systems.

  15. Dynamics of finite-size networks of coupled oscillators

    NASA Astrophysics Data System (ADS)

    Buice, Michael; Chow, Carson

    2010-03-01

    Mean field models of coupled oscillators do not adequately capture the dynamics of large but finite size networks. For example, the incoherent state of the Kuramoto model of coupled oscillators exhibits marginal modes in mean field theory. We demonstrate that corrections due to finite size effects render these modes stable in the subcritical case, i.e. when the population is not synchronous. This demonstration is facilitated by the construction of a non-equilibrium statistical field theoretic formulation of a generic model of coupled oscillators. This theory is consistent with previous results. In the all-to-all case, the fluctuations in this theory are due completely to finite size corrections, which can be calculated in an expansion in 1/N, where N is the number of oscillators. The N -> infinity limit of this theory is what is traditionally called mean field theory for the Kuramoto model. We also demonstrate this approach with a system of pulse coupled theta neurons and describe the stability of the population activity.

  16. Exploration of the Dynamic Properties of Protein Complexes Predicted from Spatially Constrained Protein-Protein Interaction Networks

    PubMed Central

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

    2014-01-01

    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

  17. 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.

  18. Analog implementation of pulse-coupled neural networks.

    PubMed

    Ota, Y; Wilamowski, B M

    1999-01-01

    This paper presents a compact architecture for analog CMOS hardware implementation of voltage-mode pulse-coupled neural networks (PCNN's). The hardware implementation methods shows inherent fault tolerance specialties and high speed, which is usually more than an order of magnitude over the software counterpart. A computational style described in this article mimics a biological neural network using pulse-stream signaling and analog summation and multiplication. Pulse-stream encoding technique uses pulse streams to carry information and control analog circuitry, while storing further analog information on the time axis. The main feature of the proposed neuron circuit is that the structure is compact, yet exhibiting all the basic properties of natural biological neurons. Functional and structural forms of neural and synaptic functions are presented along with simulation results. Finally, the proposed design is applied to image processing to demonstrate successful restoration of images and their features.

  19. 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.

  20. Data Synchronization in a Network of Coupled Phase Oscillators

    NASA Astrophysics Data System (ADS)

    Miyano, Takaya; Tsutsui, Takako

    2007-01-01

    We devised a new method of data mining for a large-scale database. In the method, a network of locally coupled phase oscillators subject to Kuramoto’s model substitutes for given multivariate data to generate major features through phase locking of the oscillators, i.e., phase transition of the data set. We applied the method to the national database of care needs certification for the Japanese public long-term care insurance program, and found three major patterns in the aging process of the frail elderly. This work revealed the latent utility of Kuramoto’s model for data processing.

  1. Parameter adaptation in a simplified pulse-coupled neural network

    NASA Astrophysics Data System (ADS)

    Szekely, Geza; Lindblad, Thomas

    1999-03-01

    In a general purpose pulse coupled neural network (PCNN) algorithm the following parameters are used: 2 weight matrices, 3 time constants, 3 normalization factors and 2 further parameters. In a given application, one has to determine the near optimal parameter set to achieve the desired goal. Here a simplified PCNN is described which contains a parameter fitting part, in the least squares sense. Given input and a desired output image, the program is able to determine the optimal value of a selected PCNN parameter. This method can be extended to more general PCNN algorithms, because partial derivatives are not required for the fitting. Only the sum of squares of the differences is used.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    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.

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

    PubMed

    Hu, Guang; Di Paola, Luisa; Liang, Zhongjie; Giuliani, Alessandro

    2017-01-01

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

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

    PubMed Central

    Di Paola, Luisa; Liang, Zhongjie; Giuliani, Alessandro

    2017-01-01

    The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology. PMID:28243596

  5. Synchronization in slowly switching networks of coupled oscillators

    PubMed Central

    Zhou, Jie; Zou, Yong; Guan, Shuguang; Liu, Zonghua; Boccaletti, S.

    2016-01-01

    Networks whose structure of connections evolves in time constitute a big challenge in the study of synchronization, in particular when the time scales for the evolution of the graph topology are comparable with (or even longer than) those pertinent to the units’ dynamics. We here focus on networks with a slow-switching structure, and show that the necessary conditions for synchronization, i.e. the conditions for which synchronization is locally stable, are determined by the time average of the largest Lyapunov exponents of transverse modes of the switching topologies. Comparison between fast- and slow-switching networks allows elucidating that slow-switching processes prompt synchronization in the cases where the Master Stability Function is concave, whereas fast-switching schemes facilitate synchronization for convex curves. Moreover, the condition of slow-switching enables the introduction of a control strategy for inducing synchronization in networks with arbitrary structure and coupling strength, which is of evident relevance for broad applications in real world systems. PMID:27779253

  6. Synchronization in slowly switching networks of coupled oscillators.

    PubMed

    Zhou, Jie; Zou, Yong; Guan, Shuguang; Liu, Zonghua; Boccaletti, S

    2016-10-25

    Networks whose structure of connections evolves in time constitute a big challenge in the study of synchronization, in particular when the time scales for the evolution of the graph topology are comparable with (or even longer than) those pertinent to the units' dynamics. We here focus on networks with a slow-switching structure, and show that the necessary conditions for synchronization, i.e. the conditions for which synchronization is locally stable, are determined by the time average of the largest Lyapunov exponents of transverse modes of the switching topologies. Comparison between fast- and slow-switching networks allows elucidating that slow-switching processes prompt synchronization in the cases where the Master Stability Function is concave, whereas fast-switching schemes facilitate synchronization for convex curves. Moreover, the condition of slow-switching enables the introduction of a control strategy for inducing synchronization in networks with arbitrary structure and coupling strength, which is of evident relevance for broad applications in real world systems.

  7. Pulse-coupled neural networks for contour and motion matchings.

    PubMed

    Yu, Bo; Zhang, Liming

    2004-09-01

    Two neural networks based on temporal coding are proposed in this paper to perform contour and motion matchings. Both of the proposed networks are three-dimensional (3-D) pulse-coupled neural networks (PCNNs). They are composed of simplified Eckhorn neurons and mimic the structure of the primary visual cortex. The PCNN for contour matching can segment from the background the object with a particular contour, which has been stored as prior knowledge and controls the network activity in the form of spike series; The PCNN for motion matching not only detects the motion in the visual field, but also extracts the object moving in an arbitrarily specified direction. The basic idea of these two models is to encode information into the timing of spikes and later to decode this information through coincidence detectors and synapse delays to realize the knowledge-controlled object matchings. The simulation results demonstrate that the temporal coding and the decoding mechanisms are powerful enough to perform the contour and motion matchings.

  8. Synchronization in slowly switching networks of coupled oscillators

    NASA Astrophysics Data System (ADS)

    Zhou, Jie; Zou, Yong; Guan, Shuguang; Liu, Zonghua; Boccaletti, S.

    2016-10-01

    Networks whose structure of connections evolves in time constitute a big challenge in the study of synchronization, in particular when the time scales for the evolution of the graph topology are comparable with (or even longer than) those pertinent to the units’ dynamics. We here focus on networks with a slow-switching structure, and show that the necessary conditions for synchronization, i.e. the conditions for which synchronization is locally stable, are determined by the time average of the largest Lyapunov exponents of transverse modes of the switching topologies. Comparison between fast- and slow-switching networks allows elucidating that slow-switching processes prompt synchronization in the cases where the Master Stability Function is concave, whereas fast-switching schemes facilitate synchronization for convex curves. Moreover, the condition of slow-switching enables the introduction of a control strategy for inducing synchronization in networks with arbitrary structure and coupling strength, which is of evident relevance for broad applications in real world systems.

  9. Desynchronization in networks of globally coupled neurons with dendritic dynamics.

    PubMed

    Majtanik, Milan; Dolan, Kevin; Tass, Peter A

    2006-10-01

    Effective desynchronization can be exploited as a tool for probing the functional significance of synchronized neural activity underlying perceptual and cognitive processes or as a mild treatment for neurological disorders like Parkinson's disease. In this article we show that pulse-based desynchronization techniques, originally developed for networks of globally coupled oscillators (Kuramoto model), can be adapted to networks of coupled neurons with dendritic dynamics. Compared to the Kuramoto model, the dendritic dynamics significantly alters the response of the neuron to the stimulation. Under medium stimulation amplitude a bistability of the response of a single neuron is observed. When stimulated at some initial phases, the neuron displays only modulations of its firing, whereas at other initial phases it stops oscillating entirely. Significant alterations in the duration of stimulation-induced transients are also observed. These transients endure after the end of the stimulation and cause maximal desynchronization to occur not during the stimulation, but with some delay after the stimulation has been turned off. To account for this delayed desynchronization effect, we have designed a new calibration procedure for finding the stimulation parameters that result in optimal desynchronization. We have also developed a new desynchronization technique by low frequency entrainment. The stimulation techniques originally developed for the Kuramoto model, when using the new calibration procedure, can also be applied to networks with dendritic dynamics. However, the mechanism by which desynchronization is achieved is substantially different than for the network of Kuramoto oscillators. In particular, the addition of dendritic dynamics significantly changes the timing of the stimulation required to obtain desynchronization. We propose desynchronization stimulation for experimental analysis of synchronized neural processes and for the therapy of movement disorders.

  10. 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

  11. 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.

  12. A monoclonal antibody for G protein-coupled receptor crystallography.

    PubMed

    Day, Peter W; Rasmussen, Søren G F; Parnot, Charles; Fung, Juan José; Masood, Asna; Kobilka, Tong Sun; Yao, Xiao-Jie; Choi, Hee-Jung; Weis, William I; Rohrer, Daniel K; Kobilka, Brian K

    2007-11-01

    G protein-coupled receptors (GPCRs) constitute the largest family of signaling proteins in mammals, mediating responses to hormones, neurotransmitters, and senses of sight, smell and taste. Mechanistic insight into GPCR signal transduction is limited by a paucity of high-resolution structural information. We describe the generation of a monoclonal antibody that recognizes the third intracellular loop (IL3) of the native human beta(2) adrenergic (beta(2)AR) receptor; this antibody was critical for acquiring diffraction-quality crystals.

  13. Bubbling effect in the electro-optic delayed feedback oscillator coupled network

    NASA Astrophysics Data System (ADS)

    Liu, Lingfeng; Lin, Jun; Miao, Suoxia

    2017-03-01

    Synchronization in the optical systems coupled network always suffers from bubbling events. In this paper, we numerically investigate the statistical properties of the synchronization characteristics and bubbling effects in the electro-optic delayed feedback oscillator coupled network with different coupling strength, delay time and gain coefficient. Furthermore, we compare our results with the synchronization properties of semiconductor laser (SL) coupled network, which indicates that the electro-optic delayed feedback oscillator can be better to suppress the bubbling effects in the synchronization of coupled network under the same conditions.

  14. 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.

  15. Ice flood detection based on pulse coupled neural network

    NASA Astrophysics Data System (ADS)

    Liu, Xian-hong; Chen, Zhi-bin; Wang, Wei-ming

    2013-09-01

    When ice run in the river course blocks the waterway severely, swelling will be speeded and of large scope, which will usually cause disasters. To judge the trend of ice flood and its disaster in the future, some data of ice flood, such as area, velocity and density, must be obtained timely. The velocity of ice flood can be got by analyzing the displacement and time interval of a same object in each image. The density of ice flood can be calculated from the ice area in a certain region. A precise area statistic of ice is the most important and difficult thing. In this paper, an edge extraction approach based on pulse coupled neural network is proposed to locate the edge of ice. Then, the area of ice can be obtained by the relativity between the ice and the region. The experimental results indicate that the method based on pulse coupled neural network is feasible. The extracted edge of the ice is distinct and continuous and the influence of noise on the infrared image is effectively eliminated.

  16. 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.

  17. Statistic properties and cascading failures in a coupled transit network consisting of bus and subway systems

    NASA Astrophysics Data System (ADS)

    Yang, Yang; Huang, Ailing; Guan, Wei

    2014-08-01

    A coupled network model consisting of bus and subway systems is proposed, and the statistic properties of the three networks: bus, subway and coupled networks of Beijing are studied with the theory of complex network. The result shows that the three networks have typical properties of small-world. We propose three parameters to depict the coupled network, they are: the coupled parameter β the influence parameter S and the node tolerance parameter γ. We use the binary influence model to simulate a feedback process and cascading failure in the coupled network and we obtain the conclusions: (1) The cascading size grows with β; (2) The cascading size grows with S, but it has a critical point; (3) The cascading size grows with the decrease of γ, when γ≤0.3, the cascading failure will extent to the whole network.

  18. Cell-free expression of G-protein-coupled receptors.

    PubMed

    Orbán, Erika; Proverbio, Davide; Haberstock, Stefan; Dötsch, Volker; Bernhard, Frank

    2015-01-01

    Cell-free expression has emerged as a new standard for the production of membrane proteins. The reduction of expression complexity in cell-free systems eliminates central bottlenecks and allows the reliable and efficient synthesis of many different types of membrane proteins. Furthermore, the open accessibility of cell-free reactions enables the co-translational solubilization of cell-free expressed membrane proteins in a large variety of supplied additives. Hydrophobic environments can therefore be adjusted according to the requirements of individual membrane protein targets. We present different approaches for the preparative scale cell-free production of G-protein-coupled receptors using the extracts of Escherichia coli cells. We exemplify expression conditions implementing detergents, nanodiscs, or liposomes. The generated protein samples could be directly used for further functional characterization.

  19. G-protein-coupled receptors: past, present and future

    PubMed Central

    Hill, Stephen J

    2006-01-01

    The G-protein-coupled receptor (GPCR) family represents the largest and most versatile group of cell surface receptors. Drugs active at these receptors have therapeutic actions across a wide range of human diseases ranging from allergic rhinitis to pain, hypertension and schizophrenia. This review provides a brief historical overview of the properties and signalling characteristics of this important family of receptors. PMID:16402114

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

    PubMed

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

    2016-04-26

    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.

  1. 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

  2. Receptor component protein (RCP): a member of a multi-protein complex required for G-protein-coupled signal transduction.

    PubMed

    Prado, M A; Evans-Bain, B; Dickerson, I M

    2002-08-01

    The calcitonin-gene-related peptide (CGRP) receptor component protein (RCP) is a 148-amino-acid intracellular protein that is required for G-protein-coupled signal transduction at receptors for the neuropeptide CGRP. RCP works in conjunction with two other proteins to constitute a functional CGRP receptor: calcitonin-receptor-like receptor (CRLR) and receptor-activity-modifying protein 1 (RAMP1). CRLR has the stereotypical seven-transmembrane topology of a G-protein-coupled receptor; it requires RAMP1 for trafficking to the cell surface and for ligand specificity, and requires RCP for coupling to the cellular signal transduction pathway. We have made cell lines that expressed an antisense construct of RCP and determined that CGRP-mediated signal transduction was reduced, while CGRP binding was unaffected. Furthermore, signalling at two other endogenous G-protein-coupled receptors was unaffected, suggesting that RCP was specific for a limited subset of receptors.

  3. Cross-frequency coupling in real and virtual brain networks

    PubMed Central

    Jirsa, Viktor; Müller, Viktor

    2013-01-01

    Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed. PMID:23840188

  4. Immunoprecipitation and Mass Spectrometry Defines an Extensive RBM45 Protein-Protein Interaction Network

    PubMed Central

    Li, Yang; Collins, Mahlon; An, Jiyan; Geiser, Rachel; Tegeler, Tony; Tsantilas, Kristine; Garcia, Krystine; Pirrotte, Patrick; Bowser, Robert

    2016-01-01

    The pathological accumulation of RNA-binding proteins (RBPs) within inclusion bodies is a hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). RBP aggregation results in both toxic gain and loss of normal function. Determining the protein binding partners and normal functions of disease-associated RBPs is necessary to fully understand molecular mechanisms of RBPs in disease. Herein, we characterized the protein-protein interactions (PPIs) of RBM45, a RBP that localizes to inclusions in ALS/FTLD. Using immunoprecipitation coupled to mass spectrometry (IP-MS), we identified 132 proteins that specifically interact with RBM45 within HEK293 cells. Select PPIs were validated by immunoblot and immunocytochemistry, demonstrating that RBM45 associates with a number of other RBPs primarily via RNA-dependent interactions in the nucleus. Analysis of the biological processes and pathways associated with RBM45-interacting proteins indicates enrichment for nuclear RNA processing/splicing via association with hnRNP proteins and cytoplasmic RNA translation via eiF2 and eiF4 pathways. Moreover, several other ALS-linked RBPs, including TDP-43, FUS, Matrin-3, and hnRNP-A1, interact with RBM45, consistent with prior observations of these proteins within intracellular inclusions in ALS/FTLD. Taken together, our results define a PPI network for RBM45, suggest novel functions for this protein, and provide new insights into the contributions of RBM45 to neurodegeneration in ALS/FTLD. PMID:26979993

  5. Waves and Oscillations in Networks of Coupled Neurons

    NASA Astrophysics Data System (ADS)

    Ermentrout, B.

    Neural systems are characterized by the interactions of thousands of individual cells called neurons. Individual neurons vary in their properties with some of them spontaneously active and others active only when given a sufficient perturbation. In this note, I will describe work that has been done on the mathematical analysis of waves and synchronous oscillations in spatially distributed networks of neurons. These classes of behavior are observed both in vivo (that is, in the living brain) and in vitro (isolated networks, such as slices of brain tissue.) We focus on these simple behaviors rather than on the possible computations that networks of neurons can do (such as filtering sensory inputs and producing precise motor output) mainly because they are mathematically tractable. The chapter is organized as follows. First, I will introduce the kinds of equations that are of interest and from these abstract some simplified models. I will consider several different types of connectivity - from "all-to-all" to spatially organized. Typically (although not in every case), each individual neuron is represented by a scalar equation for its dynamics. These individuals can be coupled together directly or indirectly and in spatially discrete or continuous arrays.

  6. 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

  7. FunCoup 3.0: database of genome-wide functional coupling networks.

    PubMed

    Schmitt, Thomas; Ogris, Christoph; Sonnhammer, Erik L L

    2014-01-01

    We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction.

  8. Reconstructing network topology and coupling strengths in directed networks of discrete-time dynamics

    NASA Astrophysics Data System (ADS)

    Lai, Pik-Yin

    2017-02-01

    Reconstructing network connection topology and interaction strengths solely from measurement of the dynamics of the nodes is a challenging inverse problem of broad applicability in various areas of science and engineering. For a discrete-time step network under noises whose noise-free dynamics is stationary, we derive general analytic results relating the weighted connection matrix of the network to the correlation functions obtained from time-series measurements of the nodes for networks with one-dimensional intrinsic node dynamics. Information about the intrinsic node dynamics and the noise strengths acting on the nodes can also be obtained. Based on these results, we develop a scheme that can reconstruct the above information of the network using only the time-series measurements of node dynamics as input. Reconstruction formulas for higher-dimensional node dynamics are also derived and illustrated with a two-dimensional node dynamics network system. Furthermore, we extend our results and obtain a reconstruction scheme even for the cases when the noise-free dynamics is periodic. We demonstrate that our method can give accurate reconstruction results for weighted directed networks with linear or nonlinear node dynamics of various connection topologies, and with linear or nonlinear couplings.

  9. Protein Structure Network-based Drug Design.

    PubMed

    Liang, Zhongjie; Hu, Guang

    2016-01-01

    Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structurebased drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

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

    PubMed

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

    2013-01-01

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

  11. Dynamic optimization of metabolic networks coupled with gene expression.

    PubMed

    Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

    2015-01-21

    The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle.

  12. The G protein Gi1 exhibits basal coupling but not preassembly with G protein-coupled receptors.

    PubMed

    Bondar, Alexey; Lazar, Josef

    2017-06-09

    The Gi/o protein family transduces signals from a diverse group of G protein-coupled receptors (GPCRs). The observed specificity of Gi/o-GPCR coupling and the high rate of Gi/o signal transduction have been hypothesized to be enabled by the existence of stable associates between Gi/o proteins and their cognate GPCRs in the inactive state (Gi/o-GPCR preassembly). To test this hypothesis, we applied the recently developed technique of two-photon polarization microscopy (2PPM) to Gαi1 subunits labeled with fluorescent proteins and four GPCRs: the α2A-adrenergic receptor, GABAB, cannabinoid receptor type 1 (CB1R), and dopamine receptor type 2. Our experiments with non-dissociating mutants of fluorescently labeled Gαi1 subunits (exhibiting impaired dissociation from activated GPCRs) showed that 2PPM is capable of detecting GPCR-G protein interactions. 2PPM experiments with non-mutated fluorescently labeled Gαi1 subunits and α2A-adrenergic receptor, GABAB, or dopamine receptor type 2 receptors did not reveal any interaction between the Gi1 protein and the non-stimulated GPCRs. In contrast, non-stimulated CB1R exhibited an interaction with the Gi1 protein. Further experiments revealed that this interaction is caused solely by CB1R basal activity; no preassembly between CB1R and the Gi1 protein could be observed. Our results demonstrate that four diverse GPCRs do not preassemble with non-active Gi1 However, we also show that basal GPCR activity allows interactions between non-stimulated GPCRs and Gi1 (basal coupling). These findings suggest that Gi1 interacts only with active GPCRs and that the well known high speed of GPCR signal transduction does not require preassembly between G proteins and GPCRs. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  13. Do cancer proteins really interact strongly in the human protein-protein interaction network?

    PubMed

    Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

    2011-06-01

    Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. © 2011 Elsevier Ltd. All rights reserved.

  14. Vav Family Proteins Couple to Diverse Cell Surface Receptors

    PubMed Central

    Moores, Sheri L.; Selfors, Laura M.; Fredericks, Jessica; Breit, Timo; Fujikawa, Keiko; Alt, Frederick W.; Brugge, Joan S.; Swat, Wojciech

    2000-01-01

    Vav proteins are guanine nucleotide exchange factors for Rho family GTPases which activate pathways leading to actin cytoskeletal rearrangements and transcriptional alterations. Vav proteins contain several protein binding domains which can link cell surface receptors to downstream signaling proteins. Vav1 is expressed exclusively in hematopoietic cells and tyrosine phosphorylated in response to activation of multiple cell surface receptors. However, it is not known whether the recently identified isoforms Vav2 and Vav3, which are broadly expressed, can couple with similar classes of receptors, nor is it known whether all Vav isoforms possess identical functional activities. We expressed Vav1, Vav2, and Vav3 at equivalent levels to directly compare the responses of the Vav proteins to receptor activation. Although each Vav isoform was tyrosine phosphorylated upon activation of representative receptor tyrosine kinases, integrin, and lymphocyte antigen receptors, we found unique aspects of Vav protein coupling in each receptor pathway. Each Vav protein coprecipitated with activated epidermal growth factor and platelet-derived growth factor (PDGF) receptors, and multiple phosphorylated tyrosine residues on the PDGF receptor were able to mediate Vav2 tyrosine phosphorylation. Integrin-induced tyrosine phosphorylation of Vav proteins was not detected in nonhematopoietic cells unless the protein tyrosine kinase Syk was also expressed, suggesting that integrin activation of Vav proteins may be restricted to cell types that express particular tyrosine kinases. In addition, we found that Vav1, but not Vav2 or Vav3, can efficiently cooperate with T-cell receptor signaling to enhance NFAT-dependent transcription, while Vav1 and Vav3, but not Vav2, can enhance NFκB-dependent transcription. Thus, although each Vav isoform can respond to similar cell surface receptors, there are isoform-specific differences in their activation of downstream signaling pathways. PMID:10938113

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

    PubMed

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

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

  16. Extended protein/water H-bond networks in photosynthetic water oxidation.

    PubMed

    Bondar, Ana-Nicoleta; Dau, Holger

    2012-08-01

    Oxidation of water molecules in the photosystem II (PSII) protein complex proceeds at the manganese-calcium complex, which is buried deeply in the lumenal part of PSII. Understanding the PSII function requires knowledge of the intricate coupling between the water-oxidation chemistry and the dynamic proton management by the PSII protein matrix. Here we assess the structural basis for long-distance proton transfer in the interior of PSII and for proton management at its surface. Using the recent high-resolution crystal structure of PSII, we investigate prominent hydrogen-bonded networks of the lumenal side of PSII. This analysis leads to the identification of clusters of polar groups and hydrogen-bonded networks consisting of amino acid residues and water molecules. We suggest that long-distance proton transfer and conformational coupling is facilitated by hydrogen-bonded networks that often involve more than one protein subunit. Proton-storing Asp/Glu dyads, such as the D1-E65/D2-E312 dyad connected to a complex water-wire network, may be particularly important for coupling protonation states to the protein conformation. Clusters of carboxylic amino acids could participate in proton management at the lumenal surface of PSII. We propose that rather than having a classical hydrophobic protein interior, the lumenal side of PSII resembles a complex polyelectrolyte with evolutionary optimized hydrogen-bonding networks. This article is part of a Special Issue entitled: Photosynthesis Research for Sustainability: from Natural to Artificial.

  17. STITCH: interaction networks of chemicals and proteins

    PubMed Central

    Kuhn, Michael; von Mering, Christian; Campillos, Monica; Jensen, Lars Juhl; Bork, Peer

    2008-01-01

    The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/ PMID:18084021

  18. Complex projective synchronization in drive-response stochastic coupled networks with complex-variable systems and coupling time delays

    NASA Astrophysics Data System (ADS)

    Wu, Xuefei; Xu, Chen; Feng, Jianwen

    2015-03-01

    In this paper, the complex projective synchronization in drive-response stochastic coupled networks with complex-variable systems and linear coupling time delays are considered. The pinning control scheme are adopted to achieve complex projective synchronization and several simple and practical sufficient conditions are obtained in a general drive-response network. In addition, the adaptive feedback algorithms are proposed to adjust the control strength. Several numerical simulations are provided to show the effectiveness and feasibility of the proposed methods.

  19. Digital halftoning using a modified pulse-coupled neural network

    NASA Astrophysics Data System (ADS)

    Duan, Huawei; Chen, Guangxue

    2011-06-01

    We report the application of modified pulse-coupled neural network (PCNN) models as an image processing tool to improve the quality of digital halftoning. Four factors including weight matrice, internal activity computation, type of error diffusion and linking coefficient were researched and optimized in terms of the PSNR metric and visual inspection on halftoning simulations. Experimental results show that the optimized PCNN model is able to yield satisfying halftoning outputs, which has better quality than that obtained by using the traditional order dither algorithm. Moreover, because of the utilization of random function in the modified PCNN model, simulated images generated from that PCNN model eliminate the periodic visual defect that the order dither innately has and therefore can potentially get rid of moiré pattern if used for printing color image. This research, on the one hand, provides a new way to do digital halftoning, on the other hand, expands the application field of the PCNN method.

  20. 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.

  1. 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.

  2. 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.

  3. Foveation by a pulse-coupled neural network.

    PubMed

    Kinser, J M

    1999-01-01

    Humans do not stare at an image, they foveate. Their eyes move about points of interest within the image collecting clues as to the content of the image. Object shape is one of the driving forces of foveation. These foveation points are generally corners and, to a lesser extent, the edges. The pulse-coupled neural network (PCNN) has the inherent ability to segment an image. The corners and edges of the PCNN segments are similar to the foveation points. Thus, it is a natural extension of PCNN technology to use it as a foveation engine. This paper will present theory and examples of foveation through the use of a PCNN, and it will also demonstrate that it can be quite useful in image recognition.

  4. Pulse-coupled neural networks for medical image analysis

    NASA Astrophysics Data System (ADS)

    Keller, Paul E.; McKinnon, A. D.

    1999-03-01

    Pulse-coupled neural networks (PCNNs) have recently become fashionable for image processing. This paper discusses some of the advantages and disadvantages of PCNNs for performing image segmentation in the realm of medical diagnostics. PCNNs were tested with magnetic resonance imagery (MRI) of the brian and abdominal region and nuclear scintigraphic imagery of the lungs (V/Q scans). Our preliminary results show that PCNNs do well at contrast enhancement. They also do well at image segmentation when each segment is approximately uniform in intensity. However, there are limits to what PCNNs can do. For example, when intensity significantly varies across a single segment, that segment does not properly separate from other objects. Another problem with the PCNN is properly setting the various parameters so that a uniform response is achieved over a set of imagery. Sometimes, a set of parameters that properly segment objects in one image fail on a similar image.

  5. 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

  6. 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.

  7. Event-based cluster synchronization of coupled genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

    2017-09-01

    In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

  8. Sustained rhythmic activity in gap-junctionally coupled networks of model neurons depends on the diameter of coupled dendrites

    PubMed Central

    Gansert, Juliane; Golowasch, Jorge; Nadim, Farzan

    2008-01-01

    Gap junctions are known to be important for many network functions such as synchronization of activity and the generation of waves and oscillations. Gap junctions have also been proposed to be essential for the generation of early embryonic activity. We have previously shown that the amplitude of electrical signals propagating across gap-junctionally coupled passive cables is maximized at a unique diameter. This suggests that threshold-dependent signals may propagate through gap junctions for a finite range of diameters around this optimal value. Here we examine the diameter dependence of action potential propagation across model networks of dendro-dendritically coupled neurons. The neurons in these models have passive soma and dendrites and an action potential generating axon. We show that propagation of action potentials across gap junctions occurs only over a finite range of dendritic diameters and that propagation delay depends on this diameter. Additionally, in networks of gap-junctionally coupled neurons, rhythmic activity can emerge when closed loops (re-entrant paths) occur but again only for a finite range of dendrite diameters. The frequency of such rhythmic activity depends on the length of the path and the dendrite diameter. For large networks of randomly coupled neurons, we find that the re-entrant paths that underlie rhythmic activity also depend on dendrite diameter. These results underline the potential importance of dendrite diameter as a determinant of network activity in gap-junctionally coupled networks, such as network rhythms that are observed during early nervous system development. PMID:17913989

  9. Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors.

    PubMed

    Woods, Kristina N; Pfeffer, Jürgen; Dutta, Arpana; Klein-Seetharaman, Judith

    2016-11-16

    G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals.

  10. Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

    NASA Astrophysics Data System (ADS)

    Woods, Kristina N.; Pfeffer, Jürgen; Dutta, Arpana; Klein-Seetharaman, Judith

    2016-11-01

    G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals.

  11. Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

    PubMed Central

    Woods, Kristina N.; Pfeffer, Jürgen; Dutta, Arpana; Klein-Seetharaman, Judith

    2016-01-01

    G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals. PMID:27849063

  12. Differential coupling of visual cortex with default or frontal-parietal network based on goals.

    PubMed

    Chadick, James Z; Gazzaley, Adam

    2011-05-29

    The relationship between top-down enhancement and suppression of sensory cortical activity and large-scale neural networks remains unclear. Functional connectivity analysis of human functional magnetic resonance imaging data revealed that visual cortical areas that selectively process relevant information are functionally connected with the frontal-parietal network, whereas those that process irrelevant information are simultaneously coupled with the default network. This indicates that sensory cortical regions are differentially and dynamically coupled with distinct networks on the basis of task goals.

  13. Inherited diseases involving g proteins and g protein-coupled receptors.

    PubMed

    Spiegel, Allen M; Weinstein, Lee S

    2004-01-01

    Heterotrimeric G proteins couple seven-transmembrane receptors for diverse extracellular signals to effectors that generate intracellular signals altering cell function. Mutations in the gene encoding the alpha subunit of the G protein-coupling receptors to stimulation of adenylyl cyclase cause developmental abnormalities of bone, as well as hormone resistance (pseudohypoparathyroidism caused by loss-of-function mutations) and hormone hypersecretion (McCune-Albright syndrome caused by gain-of-function mutations). Loss- and gain-of-function mutations in genes encoding G protein-coupled receptors (GPCRs) have been identified as the cause of an increasing number of retinal, endocrine, metabolic, and developmental disorders. GPCRs comprise an evolutionarily conserved gene superfamily ( 1 ). By coupling to heterotrimeric G proteins, GPCRs transduce a wide variety of extracellular signals including monoamine, amino acid, and nucleoside neurotransmitters, as well as photons, chemical odorants, divalent cations, hormones, lipids, peptides and proteins. Following a brief overview of G protein-coupled signal transduction, we review the growing body of evidence that mutations in genes encoding GPCRs and G proteins are an important cause of human disease.

  14. Physiologically motivated image fusion using pulse-coupled neural networks

    NASA Astrophysics Data System (ADS)

    Broussard, Randy P.; Rogers, Steven K.

    1996-03-01

    This paper uses a high level vision model to describe the information passing and linking within the primate visual system. Information linking schemes, such as state dependent modulation and temporal synchronization, are presented as methods the vision system uses to combine information using expectation to fill in missing information and remove unneeded information. The possibility of using linking methods derived from physiologically based theoretical models to combine current image processing techniques for pattern recognition purposes is investigated. These image processing techniques are transforms such as (but not limited to) wavelet filters, hit or miss filters, morphological filters, and difference of gausian filters. These particular filters are chosen because they simulate functions that are performed in the primate visual system. To implement the physiologically motivated linking methods, the Pulse Coupled Neural Network (PCNN) is chosen as a basic building block for the vision model which performs linking at the neuronal pulse level. Last, an image fusion network which incorporates information linking based on the PCNN is described, and initial results are presented.

  15. Proteins as networks: usefulness of graph theory in protein science.

    PubMed

    Krishnan, Arun; Zbilut, Joseph P; Tomita, Masaru; Giuliani, Alessandro

    2008-02-01

    The network paradigm is based on the derivation of emerging properties of studied systems by their representation as oriented graphs: any system is traced back to a set of nodes (its constituent elements) linked by edges (arcs) correspondent to the relations existing between the nodes. This allows for a straightforward quantitative formalization of systems by means of the computation of mathematical descriptors of such graphs (graph theory). The network paradigm is particularly useful when it is clear which elements of the modelled system must play the role of nodes and arcs respectively, and when topological constraints have a major role with respect to kinetic ones. In this review we demonstrate how nodes and arcs of protein topology are characterized at different levels of definition: 1. Recurrence matrix of hydrophobicity patterns along the sequence 2. Contact matrix of alpha carbons of 3D structures 3. Correlation matrix of motions of different portion of the molecule in molecular dynamics. These three conditions represent different but potentially correlated reticular systems that can be profitably analysed by means of network analysis tools.

  16. Protein-Protein Interaction Network and Gene Ontology

    NASA Astrophysics Data System (ADS)

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

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

  17. The G Protein-Coupled Receptor Rhodopsin: A Historical Perspective

    PubMed Central

    Hofmann, Lukas; Palczewski, Krzysztof

    2015-01-01

    Rhodopsin is a key light-sensitive protein expressed exclusively in rod photoreceptor cells of the retina. Failure to express this transmembrane protein causes a lack of rod outer segment formation and progressive retinal degeneration, including the loss of cone photoreceptor cells. Molecular studies of rhodopsin have paved the way to understanding a large family of cell-surface membrane proteins called G protein-coupled receptors (GPCRs). Work started on rhodopsin over 100 years ago still continues today with substantial progress made every year. These activities underscore the importance of rhodopsin as a prototypical GPCR and receptor required for visual perception—the fundamental process of translating light energy into a biochemical cascade of events culminating in vision. PMID:25697513

  18. Cross-Pharmacology Analysis of G Protein-Coupled Receptors

    PubMed Central

    Briansó, Ferran; Carrascosa, Maria C.; Oprea, Tudor I.; Mestres, Jordi

    2013-01-01

    The degree of applicability of chemogenomic approaches to protein families depends on the accuracy and completeness of pharmacological data and the corresponding level of pharmacological similarity observed among their protein members. The recent public domain availability of pharmacological data for thousands of small molecules on 204 G protein-coupled receptors (GPCRs) provides a firm basis for an in-depth cross-pharmacology analysis of this superfamily. The number of protein targets included in the cross-pharmacology profile of the different GPCRs changes significantly upon varying the ligand similarity and binding affinity criteria. However, with the exception of muscarinic receptors, aminergic GPCRs distinguish themselves from the rest of the members in the family by their remarkably high levels of pharmacological similarity among them. Clusters of non-GPCR targets related by cross-pharmacology with particular GPCRs are identified and the implications for unwanted side-effects, as well as for repurposing opportunities, discussed. PMID:21851335

  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. Evolution of biomolecular networks: lessons from metabolic and protein interactions.

    PubMed

    Yamada, Takuji; Bork, Peer

    2009-11-01

    Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.

  1. Multiple switches in G protein-coupled receptor activation.

    PubMed

    Ahuja, Shivani; Smith, Steven O

    2009-09-01

    The activation mechanism of G protein-coupled receptors has presented a puzzle that finally may be close to solution. These receptors have a relatively simple architecture consisting of seven transmembrane helices that contain just a handful of highly conserved amino acids, yet they respond to light and a range of chemically diverse ligands. Recent NMR structural studies on the active metarhodopsin II intermediate of the visual receptor rhodopsin, along with the recent crystal structure of the apoprotein opsin, have revealed multiple structural elements or 'switches' that must be simultaneously triggered to achieve full activation. The confluence of several required structural changes is an example of "coincidence counting", which is often used by nature to regulate biological processes. In ligand-activated G protein-coupled receptors, the presence of multiple switches may provide an explanation for the differences between full, partial and inverse agonists.

  2. Coupled Protein Diffusion and Folding in the Cell

    PubMed Central

    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

  3. The structure and function of G-protein-coupled receptors.

    PubMed

    Rosenbaum, Daniel M; Rasmussen, Søren G F; Kobilka, Brian K

    2009-05-21

    G-protein-coupled receptors (GPCRs) mediate most of our physiological responses to hormones, neurotransmitters and environmental stimulants, and so have great potential as therapeutic targets for a broad spectrum of diseases. They are also fascinating molecules from the perspective of membrane-protein structure and biology. Great progress has been made over the past three decades in understanding diverse GPCRs, from pharmacology to functional characterization in vivo. Recent high-resolution structural studies have provided insights into the molecular mechanisms of GPCR activation and constitutive activity.

  4. Protein-induced bilayer perturbations: Lipid ordering and hydrophobic coupling.

    PubMed

    Petersen, Frederic N R; Laursen, Ib; Bohr, Henrik; Nielsen, Claus Hélix

    2009-10-02

    The host lipid bilayer is increasingly being recognized as an important non-specific regulator of membrane protein function. Despite considerable progress the interplay between hydrophobic coupling and lipid ordering is still elusive. We use electron spin resonance (ESR) to study the interaction between the model protein gramicidin and lipid bilayers of varying thickness. The free energy of the interaction is up to -6kJ/mol; thus not strongly favored over lipid-lipid interactions. Incorporation of gramicidin results in increased order parameters with increased protein concentration and hydrophobic mismatch. Our findings also show that at high protein:lipid ratios the lipids are motionally restricted but not completely immobilized. Both exchange on and off rate values for the lipid<-->gramicidin interaction are lowest at optimal hydrophobic matching. Hydrophobic mismatch of few A results in up to 10-fold increased exchange rates as compared to the 'optimal' match situation pointing to the regulatory role of hydrophobic coupling in lipid-protein interactions.

  5. Efficiency of the immunome protein interaction network increases during evolution.

    PubMed

    Ortutay, Csaba; Vihinen, Mauno

    2008-04-22

    Details of the mechanisms and selection pressures that shape the emergence and development of complex biological systems, such as the human immune system, are poorly understood. A recent definition of a reference set of proteins essential for the human immunome, combined with information about protein interaction networks for these proteins, facilitates evolutionary study of this biological machinery. Here, we present a detailed study of the development of the immunome protein interaction network during eight evolutionary steps from Bilateria ancestors to human. New nodes show preferential attachment to high degree proteins. The efficiency of the immunome protein interaction network increases during the evolutionary steps, whereas the vulnerability of the network decreases. Our results shed light on selective forces acting on the emergence of biological networks. It is likely that the high efficiency and low vulnerability are intrinsic properties of many biological networks, which arise from the effects of evolutionary processes yet to be uncovered.

  6. Prediction of Chemical-Protein Interactions Network with Weighted Network-Based Inference Method

    PubMed Central

    Cheng, Feixiong; Zhou, Yadi; Li, Weihua; Liu, Guixia; Tang, Yun

    2012-01-01

    Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via network-based inference (NBI) method, we further developed node- and edge-weighted NBI methods for CPI prediction here. Two comprehensive CPI bipartite networks extracted from ChEMBL database were used to evaluate the methods, one containing 17,111 CPI pairs between 4,741 compounds and 97 G protein-coupled receptors, the other including 13,648 CPI pairs between 2,827 compounds and 206 kinases. The range of the area under receiver operating characteristic curves was 0.73 to 0.83 for the external validation sets, which confirmed the reliability of the prediction. The weak-interaction hypothesis in CPI network was identified by the edge-weighted NBI method. Moreover, to validate the methods, several candidate targets were predicted for five approved drugs, namely imatinib, dasatinib, sertindole, olanzapine and ziprasidone. The molecular hypotheses and experimental evidence for these predictions were further provided. These results confirmed that our methods have potential values in understanding molecular basis of drug polypharmacology and would be helpful for drug repositioning. PMID:22815915

  7. An Approach to Spatiotemporally Resolve Protein Interaction Networks in Living Cells.

    PubMed

    Lobingier, Braden T; Hüttenhain, Ruth; Eichel, Kelsie; Miller, Kenneth B; Ting, Alice Y; von Zastrow, Mark; Krogan, Nevan J

    2017-04-06

    Cells operate through protein interaction networks organized in space and time. Here, we describe an approach to resolve both dimensions simultaneously by using proximity labeling mediated by engineered ascorbic acid peroxidase (APEX). APEX has been used to capture entire organelle proteomes with high temporal resolution, but its breadth of labeling is generally thought to preclude the higher spatial resolution necessary to interrogate specific protein networks. We provide a solution to this problem by combining quantitative proteomics with a system of spatial references. As proof of principle, we apply this approach to interrogate proteins engaged by G-protein-coupled receptors as they dynamically signal and traffic in response to ligand-induced activation. The method resolves known binding partners, as well as previously unidentified network components. Validating its utility as a discovery pipeline, we establish that two of these proteins promote ubiquitin-linked receptor downregulation after prolonged activation.

  8. 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

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

    PubMed

    Lalonde, Sylvie; Sero, Antoinette; Pratelli, Réjane; Pilot, Guillaume; Chen, Jin; Sardi, Maria I; Parsa, Saman A; Kim, Do-Young; Acharya, Biswa R; Stein, Erica V; Hu, Heng-Chen; Villiers, Florent; Takeda, Kouji; Yang, Yingzhen; Han, Yong S; Schwacke, Rainer; Chiang, William; Kato, Naohiro; Loqué, Dominique; Assmann, Sarah M; Kwak, June M; Schroeder, Julian I; Rhee, Seung Y; Frommer, Wolf B

    2010-01-01

    Interactions between membrane proteins and the soluble fraction are essential for signal transduction and for regulating nutrient transport. To gain insights into the membrane-based interactome, 3,852 open reading frames (ORFs) out of a target list of 8,383 representing membrane and signaling proteins from Arabidopsis thaliana were cloned into a Gateway-compatible vector. The mating-based split ubiquitin system was used to screen for potential protein-protein interactions (pPPIs) among 490 Arabidopsis ORFs. A binary robotic screen between 142 receptor-like kinases (RLKs), 72 transporters, 57 soluble protein kinases and phosphatases, 40 glycosyltransferases, 95 proteins of various functions, and 89 proteins with unknown function detected 387 out of 90,370 possible PPIs. A secondary screen confirmed 343 (of 386) pPPIs between 179 proteins, yielding a scale-free network (r(2) = 0.863). Eighty of 142 transmembrane RLKs tested positive, identifying 3 homomers, 63 heteromers, and 80 pPPIs with other proteins. Thirty-one out of 142 RLK interactors (including RLKs) had previously been found to be phosphorylated; thus interactors may be substrates for respective RLKs. None of the pPPIs described here had been reported in the major interactome databases, including potential interactors of G-protein-coupled receptors, phospholipase C, and AMT ammonium transporters. Two RLKs found as putative interactors of AMT1;1 were independently confirmed using a split luciferase assay in Arabidopsis protoplasts. These RLKs may be involved in ammonium-dependent phosphorylation of the C-terminus and regulation of ammonium uptake activity. The robotic screening method established here will enable a systematic analysis of membrane protein interactions in fungi, plants and metazoa.

  10. An information-based network approach for protein classification

    PubMed Central

    Wan, Xiaogeng; Zhao, Xin; Yau, Stephen S. T.

    2017-01-01

    Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method. PMID:28350835

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

    PubMed

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

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

  12. Multiple functions of G protein-coupled receptor kinases.

    PubMed

    Watari, Kenji; Nakaya, Michio; Kurose, Hitoshi

    2014-03-06

    Desensitization is a physiological feedback mechanism that blocks detrimental effects of persistent stimulation. G protein-coupled receptor kinase 2 (GRK2) was originally identified as the kinase that mediates G protein-coupled receptor (GPCR) desensitization. Subsequent studies revealed that GRK is a family composed of seven isoforms (GRK1-GRK7). Each GRK shows a differential expression pattern. GRK1, GRK4, and GRK7 are expressed in limited tissues. In contrast, GRK2, GRK3, GRK5, and GRK6 are ubiquitously expressed throughout the body. The roles of GRKs in GPCR desensitization are well established. When GPCRs are activated by their agonists, GRKs phosphorylate serine/threonine residues in the intracellular loops and the carboxyl-termini of GPCRs. Phosphorylation promotes translocation of β-arrestins to the receptors and inhibits further G protein activation by interrupting receptor-G protein coupling. The binding of β-arrestins to the receptors also helps to promote receptor internalization by clathrin-coated pits. Thus, the GRK-catalyzed phosphorylation and subsequent binding of β-arrestin to GPCRs are believed to be the common mechanism of GPCR desensitization and internalization. Recent studies have revealed that GRKs are also involved in the β-arrestin-mediated signaling pathway. The GRK-mediated phosphorylation of the receptors plays opposite roles in conventional G protein- and β-arrestin-mediated signaling. The GRK-catalyzed phosphorylation of the receptors results in decreased G protein-mediated signaling, but it is necessary for β-arrestin-mediated signaling. Agonists that selectively activate GRK/β-arrestin-dependent signaling without affecting G protein signaling are known as β-arrestin-biased agonists. Biased agonists are expected to have potential therapeutic benefits for various diseases due to their selective activation of favorable physiological responses or avoidance of the side effects of drugs. Furthermore, GRKs are recognized as

  13. 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.

  14. Ankyrin protein networks in membrane formation and stabilization

    PubMed Central

    Cunha, Shane R; Mohler, Peter J

    2009-01-01

    In eukaryotic cells, ankyrins serve as adaptor proteins that link membrane proteins to the underlying cytoskeleton. These adaptor proteins form protein complexes consisting of integral membrane proteins, signalling molecules and cytoskeletal components. With their modular architecture and ability to interact with many proteins, ankyrins organize and stabilize these protein networks, thereby establishing the infrastructure of membrane domains with specialized functions. To this end, ankyrin collaborates with a number of proteins including cytoskeletal proteins, cell adhesion molecules and large structural proteins. This review addresses the targeting and stabilization of protein networks related to ankyrin interactions with the cytoskeletal protein β-spectrin, L1-cell adhesion molecules and the large myofibrillar protein obscurin. The significance of these interactions for differential targeting of cardiac proteins and neuronal membrane formation is also presented. Finally, this review concludes with a discussion about ankyrin dysfunction in human diseases such as haemolytic anaemia, cardiac arrhythmia and neurological disorders. PMID:19840192

  15. 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

  16. Equivalence of coupled networks and networks with multimodal frequency distributions: Conditions for the bimodal and trimodal case

    NASA Astrophysics Data System (ADS)

    Pietras, Bastian; Deschle, Nicolás; Daffertshofer, Andreas

    2016-11-01

    Populations of oscillators can display a variety of synchronization patterns depending on the oscillators' intrinsic coupling and the coupling between them. We consider two coupled symmetric (sub)populations with unimodal frequency distributions. If internal and external coupling strengths are identical, a change of variables transforms the system into a single population of oscillators whose natural frequencies are bimodally distributed. Otherwise an additional bifurcation parameter κ enters the dynamics. By using the Ott-Antonsen ansatz, we rigorously prove that κ does not lead to new bifurcations, but that a symmetric two-coupled-population network and a network with a symmetric bimodal frequency distribution are topologically equivalent. Seeking for generalizations, we further analyze a symmetric trimodal network vis-à-vis three coupled symmetric unimodal populations. Here, however, the equivalence with respect to stability, dynamics, and bifurcations of the two systems no longer holds.

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

    PubMed

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

    2012-07-01

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

  18. 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

  19. Colloidal Aggregation Causes Inhibition of G Protein-Coupled Receptors

    PubMed Central

    2013-01-01

    Colloidal aggregation is the dominant mechanism for artifactual inhibition of soluble proteins, and controls against it are now widely deployed. Conversely, investigating this mechanism for membrane-bound receptors has proven difficult. Here we investigate the activity of four well-characterized aggregators against three G protein-coupled receptors (GPCRs) recognizing peptide and protein ligands. Each of the aggregators was active at micromolar concentrations against the three GPCRs in cell-based assays. This activity could be attenuated by either centrifugation of the inhibitor stock solution or by addition of Tween-80 detergent. In the absence of agonist, the aggregators acted as inverse agonists, consistent with a direct receptor interaction. Meanwhile, several literature GPCR ligands that resemble aggregators themselves formed colloids, by both physical and enzymological tests. These observations suggest that some GPCRs may be artifactually antagonized by colloidal aggregates, an effect that merits the attention of investigators in this field. PMID:23437772

  20. 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

  1. [Regulation of G protein-coupled receptor kinase activity].

    PubMed

    Haga, T; Haga, K; Kameyama, K; Nakata, H

    1994-09-01

    Recent progress on the activation of G protein-coupled receptor kinases is reviewed. beta-Adrenergic receptor kinase (beta ARK) is activated by G protein beta gamma -subunits, which interact with the carboxyl terminal portion of beta ARK. Muscarinic receptor m2-subtypes are phosphorylated by beta ARK1 in the central part of the third intracellular loop (I3). Phosphorylation of I3-GST fusion protein by beta ARK1 is synergistically stimulated by the beta gamma -subunits and mastoparan or a peptide corresponding to portions adjacent to the transmembrane segments of m2-receptors or by beta gamma -subunits and the agonist-bound I3-deleted m2 variant. These results indicate that agonist-bound receptors serve as both substrates and activators of beta ARK.

  2. Interaction of chimera states in a multilayered network of nonlocally coupled oscillators

    NASA Astrophysics Data System (ADS)

    Goremyko, M. V.; Maksimenko, V. A.; Makarov, V. V.; Ghosh, D.; Bera, B.; Dana, S. K.; Hramov, A. E.

    2017-08-01

    The processes of formation and evolution of chimera states in the model of a multilayered network of nonlinear elements with complex coupling topology are studied. A two-layered network of nonlocally intralayer-coupled Kuramoto-Sakaguchi phase oscillators is taken as the object of investigation. Different modes implemented in this system upon variation of the degree of interlayer interaction are demonstrated.

  3. 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.

  4. A Membrane Protein/Signaling Protein Interaction Network for Arabidopsis Version AMPv2

    PubMed Central

    Lalonde, Sylvie; Sero, Antoinette; Pratelli, Réjane; Pilot, Guillaume; Chen, Jin; Sardi, Maria I.; Parsa, Saman A.; Kim, Do-Young; Acharya, Biswa R.; Stein, Erica V.; Hu, Heng-Chen; Villiers, Florent; Takeda, Kouji; Yang, Yingzhen; Han, Yong S.; Schwacke, Rainer; Chiang, William; Kato, Naohiro; Loqué, Dominique; Assmann, Sarah M.; Kwak, June M.; Schroeder, Julian I.; Rhee, Seung Y.; Frommer, Wolf B.

    2010-01-01

    Interactions between membrane proteins and the soluble fraction are essential for signal transduction and for regulating nutrient transport. To gain insights into the membrane-based interactome, 3,852 open reading frames (ORFs) out of a target list of 8,383 representing membrane and signaling proteins from Arabidopsis thaliana were cloned into a Gateway-compatible vector. The mating-based split ubiquitin system was used to screen for potential protein–protein interactions (pPPIs) among 490 Arabidopsis ORFs. A binary robotic screen between 142 receptor-like kinases (RLKs), 72 transporters, 57 soluble protein kinases and phosphatases, 40 glycosyltransferases, 95 proteins of various functions, and 89 proteins with unknown function detected 387 out of 90,370 possible PPIs. A secondary screen confirmed 343 (of 386) pPPIs between 179 proteins, yielding a scale-free network (r2 = 0.863). Eighty of 142 transmembrane RLKs tested positive, identifying 3 homomers, 63 heteromers, and 80 pPPIs with other proteins. Thirty-one out of 142 RLK interactors (including RLKs) had previously been found to be phosphorylated; thus interactors may be substrates for respective RLKs. None of the pPPIs described here had been reported in the major interactome databases, including potential interactors of G-protein-coupled receptors, phospholipase C, and AMT ammonium transporters. Two RLKs found as putative interactors of AMT1;1 were independently confirmed using a split luciferase assay in Arabidopsis protoplasts. These RLKs may be involved in ammonium-dependent phosphorylation of the C-terminus and regulation of ammonium uptake activity. The robotic screening method established here will enable a systematic analysis of membrane protein interactions in fungi, plants and metazoa. PMID:21423366

  5. Breakdown of order preservation in symmetric oscillator networks with pulse-coupling.

    PubMed

    Kielblock, Hinrich; Kirst, Christoph; Timme, Marc

    2011-06-01

    Symmetric networks of coupled dynamical units exhibit invariant subspaces with two or more units synchronized. In time-continuously coupled systems, these invariant sets constitute barriers for the dynamics. For networks of units with local dynamics defined on the real line, this implies that the units' ordering is preserved and that their winding number is identical. Here, we show that in permutation-symmetric networks with pulse-coupling, the order is often no longer preserved. We analytically study a class of pulse-coupled oscillators (characterizing for instance the dynamics of spiking neural networks) and derive quantitative conditions for the breakdown of order preservation. We find that in general pulse-coupling yields additional dimensions to the state space such that units may change their order by avoiding the invariant sets. We identify a system of two symmetrically pulse-coupled identical oscillators where, contrary to intuition, the oscillators' average frequencies and thus their winding numbers are different.

  6. 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.

  7. Fundamentals of protein interaction network mapping.

    PubMed

    Snider, Jamie; Kotlyar, Max; Saraon, Punit; Yao, Zhong; Jurisica, Igor; Stagljar, Igor

    2015-12-17

    Studying protein interaction networks of all proteins in an organism ("interactomes") remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow-up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research. © 2015 The Authors. Published under the terms of the CC BY 4.0 license.

  8. 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.

  9. 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

  10. 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.

  11. Cascading failures coupled model of interdependent double layered public transit network

    NASA Astrophysics Data System (ADS)

    Zhang, Lin; Fu, Bai-Bai; Li, Shu-Bin

    2016-06-01

    Taking urban public transit network as research perspective, this work introduces the influences of adjacent stations on definition of station initial load, the connected edge transit capacity, and the coupled capacity to modify traditional load-capacity cascading failures (CFs) model. Furthermore, we consider the coupled effect of lower layered public transit network on the CFs of upper layered public transit network, and construct CFs coupled model of double layered public transit network with “interdependent relationship”. Finally, taking Jinan city’s public transit network as example, we give the dynamics simulation analysis of CFs under different control parameters based on measurement indicator of station cascading failures ratio (abbreviated as CF) and the scale of time-step cascading failures (abbreviated as TCFl), get the influencing characteristics of various control parameters, and verify the feasibility of CFs coupled model of double layered public transit network.

  12. 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

  13. 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.

  14. 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.

  15. Image shadow removal using pulse coupled neural network.

    PubMed

    Gu, Xiaodong; Yu, Daoheng; Zhang, Liming

    2005-05-01

    This paper introduces an approach for image shadow removal by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. Two shadow-removing criteria are proposed. These two criteria decide how to choose the optimal parameter (the linking strength beta). The computer simulation results of shadow removal based on PCNN show that if these two criteria are satisfied, shadows are removed completely and the shadow-removed images are almost as the same as the original nonshadowed images. The shadow removal results are independent of changes of intensities of shadows in some range and variations of the places of shadows. When the first criterion is satisfied, even if the second criterion is not satisfied, as to natural grey images that have abundant grey levels, shadows also can be removed and PCNN shadow-removed images retain the shapes of the objects in original images. These two criteria also can be used for color images by dividing a color image into three channels (R, G, B). For shadows varying drastically, such as the noisy points in images, these two criteria are still right, but difficult to satisfy. Therefore, this approach can efficiently remove shadows that do not include the random noise.

  16. Recent Advances on the Role of G Protein-Coupled Receptors in Hypoxia-Mediated Signaling.

    PubMed

    Lappano, Rosamaria; Rigiracciolo, Damiano; De Marco, Paola; Avino, Silvia; Cappello, Anna Rita; Rosano, Camillo; Maggiolini, Marcello; De Francesco, Ernestina Marianna

    2016-03-01

    G protein-coupled receptors (GPCRs) are cell surface proteins mainly involved in signal transmission; however, they play a role also in several pathophysiological conditions. Chemically heterogeneous molecules like peptides, hormones, lipids, and neurotransmitters activate second messengers and induce several biological responses by binding to these seven transmembrane receptors, which are coupled to heterotrimeric G proteins. Recently, additional molecular mechanisms have been involved in GPCR-mediated signaling, leading to an intricate network of transduction pathways. In this regard, it should be mentioned that diverse GPCR family members contribute to the adaptive cell responses to low oxygen tension, which is a distinguishing feature of several illnesses like neoplastic and cardiovascular diseases. For instance, the G protein estrogen receptor, namely G protein estrogen receptor (GPER)/GPR30, has been shown to contribute to relevant biological effects induced by hypoxia via the hypoxia-inducible factor (HIF)-1α in diverse cell contexts, including cancer. Likewise, GPER has been found to modulate the biological outcome of hypoxic/ischemic stress in both cardiovascular and central nervous systems. Here, we describe the role exerted by GPCR-mediated signaling in low oxygen conditions, discussing, in particular, the involvement of GPER by a hypoxic microenvironment.

  17. Salience and Default Mode Network Coupling Predicts Cognition in Aging and Parkinson's Disease.

    PubMed

    Putcha, Deepti; Ross, Robert S; Cronin-Golomb, Alice; Janes, Amy C; Stern, Chantal E

    2016-02-01

    Cognitive impairment is common in Parkinson's disease (PD). Three neurocognitive networks support efficient cognition: the salience network, the default mode network, and the central executive network. The salience network is thought to switch between activating and deactivating the default mode and central executive networks. Anti-correlated interactions between the salience and default mode networks in particular are necessary for efficient cognition. Our previous work demonstrated altered functional coupling between the neurocognitive networks in non-demented individuals with PD compared to age-matched control participants. Here, we aim to identify associations between cognition and functional coupling between these neurocognitive networks in the same group of participants. We investigated the extent to which intrinsic functional coupling among these neurocognitive networks is related to cognitive performance across three neuropsychological domains: executive functioning, psychomotor speed, and verbal memory. Twenty-four non-demented individuals with mild to moderate PD and 20 control participants were scanned at rest and evaluated on three neuropsychological domains. PD participants were impaired on tests from all three domains compared to control participants. Our imaging results demonstrated that successful cognition across healthy aging and Parkinson's disease participants was related to anti-correlated coupling between the salience and default mode networks. Individuals with poorer performance scores across groups demonstrated more positive salience network/default-mode network coupling. Successful cognition relies on healthy coupling between the salience and default mode networks, which may become dysfunctional in PD. These results can help inform non-pharmacological interventions (repetitive transcranial magnetic stimulation) targeting these specific networks before they become vulnerable in early stages of Parkinson's disease.

  18. 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

  19. Acupuncture upregulates G protein coupled activity in SAMP8 mice.

    PubMed

    Luo, Benhua; Zhao, Lan; Zhang, Xuezhu; Kan, Bohong; Liu, Yunhe; Jia, Yujie; Han, Jingxian; Yu, Jianchun

    2017-08-01

    Transmembrane and intracellular signal transduction of G protein is closely related to the pathophysiology of Alzheimer's disease (AD). To explore the effects of Sanjiao acupuncture on G protein signal transduction pathways in the pathogenesis of AD. 36 senescence-accelerated (SAM) prone 8 mice were divided into three groups that remained untreated (SAMP8, n=12) or received Sanjiao acupuncture (SAMP8+SA, n=12) or control acupuncture (SAMP8+CA, n=12). An additional control group of SAM resistant 1 mice was included (SAMR1 group, n=12). Morris water maze tests were used to investigate learning and memory abilities. Immunoprecipitation and Western blotting were used to study expression of G protein subunits and their activities in the cortex/hippocampus. Behavioural analysis showed that acupuncture attenuated the severe cognitive deficits observed in untreated/CA-treated SAMP8 mice. The findings of the G protein activation assays via immunoprecipitation and Western blots were that the physiologically coupled activation rate (PCAR) and maximal coupled activation rate (MCAR) of Gαs and Gαi were decreased in the cortex of SAMP8 vs SAMR1 mice. Sanjiao acupuncture induced an upregulation in the PCAR of Gαs and Gαi. In the hippocampus of untreated SAMP8 mice, the PCAR of Gαs and MCAR of both Gαs and Gαi declined, and Sanjiao acupuncture was associated with an upregulation in the MCAR of Gαs and Gαi. There were no significant differences in Gαs and Gαi expression between the groups. Sanjiao acupuncture attenuates cognitive deficits in a mouse model of AD via upregulation of G protein activity and stabilisation of the cellular signal. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  20. 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.

  1. 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.

  2. What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae?

    PubMed Central

    Ekman, Diana; Light, Sara; Björklund, Åsa K; Elofsson, Arne

    2006-01-01

    Background Most proteins interact with only a few other proteins while a small number of proteins (hubs) have many interaction partners. Hub proteins and non-hub proteins differ in several respects; however, understanding is not complete about what properties characterize the hubs and set them apart from proteins of low connectivity. Therefore, we have investigated what differentiates hubs from non-hubs and static hubs (party hubs) from dynamic hubs (date hubs) in the protein-protein interaction network of Saccharomyces cerevisiae. Results The many interactions of hub proteins can only partly be explained by bindings to similar proteins or domains. It is evident that domain repeats, which are associated with binding, are enriched in hubs. Moreover, there is an over representation of multi-domain proteins and long proteins among the hubs. In addition, there are clear differences between party hubs and date hubs. Fewer of the party hubs contain long disordered regions compared to date hubs, indicating that these regions are important for flexible binding but less so for static interactions. Furthermore, party hubs interact to a large extent with each other, supporting the idea of party hubs as the cores of highly clustered functional modules. In addition, hub proteins, and in particular party hubs, are more often ancient. Finally, the more recent paralogs of party hubs are underrepresented. Conclusion Our results indicate that multiple and repeated domains are enriched in hub proteins and, further, that long disordered regions, which are common in date hubs, are particularly important for flexible binding. PMID:16780599

  3. 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.

  4. Equilibrium fluctuation relations for voltage coupling in membrane proteins.

    PubMed

    Kim, Ilsoo; Warshel, Arieh

    2015-11-01

    A general theoretical framework is developed to account for the effects of an external potential on the energetics of membrane proteins. The framework is based on the free energy relation between two (forward/backward) probability densities, which was recently generalized to non-equilibrium processes, culminating in the work-fluctuation theorem. Starting from the probability densities of the conformational states along the "voltage coupling" reaction coordinate, we investigate several interconnected free energy relations between these two conformational states, considering voltage activation of ion channels. The free energy difference between the two conformational states at zero (depolarization) membrane potential (i.e., known as the chemical component of free energy change in ion channels) is shown to be equivalent to the free energy difference between the two "equilibrium" (resting and activated) conformational states along the one-dimensional voltage couplin reaction coordinate. Furthermore, the requirement that the application of linear response approximation to the free energy functionals of voltage coupling should satisfy the general free energy relations, yields a novel closed-form expression for the gating charge in terms of other basic properties of ion channels. This connection is familiar in statistical mechanics, known as the equilibrium fluctuation-response relation. The theory is illustrated by considering the coupling of a unit charge to the external voltage in the two sites near the surface of membrane, representing the activated and resting states. This is done using a coarse-graining (CG) model of membrane proteins, which includes the membrane, the electrolytes and the electrodes. The CG model yields Marcus-type voltage dependent free energy parabolas for the response of the electrostatic environment (electrolytes etc.) to the transition from the initial to the final configuratinal states, leading to equilibrium free energy difference and free

  5. Heterodimerization and Surface Localization of G Protein Coupled Receptors

    PubMed Central

    Minneman, Kenneth P.

    2007-01-01

    G protein coupled receptors (GPCRs) are one of the largest human gene families, and are targets for many important therapeutic drugs. Over the last few years, there has been a major paradigm shift in our understanding of how these receptors function. Formerly, GPCRs were thought to exist as monomers that, upon agonist occupation, activated a heterotrimeric G protein to alter the concentrations of specific second messengers. Until recently, this relatively linear cascade has been the standard paradigm for signaling by these molecules. However, it is now clear that this model is not adequate to explain many aspects of GPCR function. We now know that many, if not most, GPCRs form homo- and/or hetero-oligomeric complexes and interact directly with intracellular proteins in addition to G proteins. It now appears that many GPCRs may not function independently, but might more accurately be described as subunits of large multi-protein signaling complexes. These observations raise many important new questions; some of which include: 1) How many functionally and pharmacologically distinct receptor subtypes exist in vivo? 2) Which GPCRs physically associate, and in what stochiometries? 3) What are the roles of individual subunits in binding ligand and activating responses? 4) Are the pharmacological or signaling properties of GPCR heterodimers different from monomers? Since these receptors are the targets for a large number of clinically useful compounds, such information is likely to be of direct therapeutic importance, both in understanding how existing drugs work, but also in discovering novel compounds to treat disease. PMID:17011524

  6. Global Geometric Affinity for Revealing High Fidelity Protein Interaction Network

    PubMed Central

    Fang, Yi; Benjamin, William; Sun, Mengtian; Ramani, Karthik

    2011-01-01

    Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries. PMID:21559288

  7. Discrimination of coupling structures using causality networks from multivariate time series

    NASA Astrophysics Data System (ADS)

    Koutlis, Christos; Kugiumtzis, Dimitris

    2016-09-01

    Measures of Granger causality on multivariate time series have been used to form the so-called causality networks. A causality network represents the interdependence structure of the underlying dynamical system or coupled dynamical systems, and its properties are quantified by network indices. In this work, it is investigated whether network indices on networks generated by an appropriate Granger causality measure can discriminate different coupling structures. The information based Granger causality measure of partial mutual information from mixed embedding (PMIME) is used to form causality networks, and a large number of network indices are ranked according to their ability to discriminate the different coupling structures. The evaluation of the network indices is done with a simulation study based on two dynamical systems, the coupled Mackey-Glass delay differential equations and the neural mass model, both of 25 variables, and three prototypes of coupling structures, i.e., random, small-world, and scale-free. It is concluded that the setting of PMIME combined with a network index attains high level of discrimination of the coupling structures solely on the basis of the observed multivariate time series. This approach is demonstrated to identify epileptic seizures emerging during electroencephalogram recordings.

  8. 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.

  9. 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.

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

    PubMed

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

    2013-08-23

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

  11. "Fluctuograms" reveal the intermittent intra-protein communication in subtilisin Carlsberg and correlate mechanical coupling with co-evolution.

    PubMed

    Silvestre-Ryan, Jordi; Lin, Yuchun; Chu, Jhih-Wei

    2011-03-01

    The mechanism of intra-protein communication and allosteric coupling is key to understanding the structure-property relationship of protein function. For subtilisin Carlsberg, the Ca²+-binding loop is distal to substrate-binding and active sites, yet the serine protease function depends on Ca²+ binding. The atomic molecular dynamics (MD) simulations of apo and Ca²+-bound subtilisin show similar structures and there is no direct evidence that subtilisin has alternative conformations. To model the intra-protein communication due to Ca²+ binding, we transform the sequential segments of an atomic MD trajectory into separate elastic network models to represent anharmonicity and nonlinearity effectively as the temporal and spatial variation of the mechanical coupling network. In analogy to the spectrogram of sound waves, this transformation is termed the "fluctuogram" of protein dynamics. We illustrate that the Ca²+-bound and apo states of subtilisin have different fluctuograms and that intra-protein communication proceeds intermittently both in space and in time. We found that residues with large mechanical coupling variation due to Ca²+ binding correlate with the reported mutation sites selected by directed evolution for improving the stability of subtilisin and its activity in a non-aqueous environment. Furthermore, we utilize the fluctuograms calculated from MD to capture the highly correlated residues in a multiple sequence alignment. We show that in addition to the magnitude, the variance of coupling strength is also an indicative property for the sequence correlation observed in a statistical coupling analysis. The results of this work illustrate that the mechanical coupling networks calculated from atomic details can be used to correlate with functionally important mutation sites and co-evolution.

  12. Evidence of Probabilistic Behaviour in Protein Interaction Networks

    DTIC Science & Technology

    2008-01-31

    Evidence of degree-weighted connectivity in nine PPI networks. a, Homo sapiens (human); b, Drosophila melanogaster (fruit fly); c-e, Saccharomyces...illustrates maps for the networks of Homo sapiens and Dro- sophila melanogaster, while maps for the remaining net- works are provided in Additional file 2. As...protein-protein interaction networks. a, Homo sapiens ; b, Drosophila melanogaster. Distances shown as average shortest path lengths L(k1, k2) between

  13. Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function: hypotheses and a comprehensive review.

    PubMed

    Csermely, Peter; Sandhu, Kuljeet Singh; Hazai, Eszter; Hoksza, Zsolt; Kiss, Huba J M; Miozzo, Federico; Veres, Dániel V; Piazza, Francesco; Nussinov, Ruth

    2012-02-01

    During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into 'cumulus-type', i.e., those similar to puffy (white) clouds, and 'stratus-type', i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an 'energy transfer' mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by 'multi-trajectories'; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach 'rarely visited' but functionally-related states. We also show the role of disorder in 'spatial games' of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks. © 2012 Bentham Science Publishers

  14. Enhancement of G Protein-Coupled Receptor Surface Expression

    PubMed Central

    Dunham, Jill H.; Hall, Randy A.

    2009-01-01

    G protein-coupled receptors (GPCRs) mediate physiological responses to a diverse array of stimuli and are the molecular targets for numerous therapeutic drugs. GPCRs primarily signal from the plasma membrane, but when expressed in heterologous cells many GPCRs exhibit poor trafficking to the cell surface. Multiple approaches have been taken to enhance GPCR surface expression in heterologous cells, including addition/deletion of receptor sequences, co-expression with interacting proteins, and treatment with pharmacological chaperones. In addition to allowing for enhanced surface expression of certain GPCRs in heterologous cells, these approaches have also shed light on the control of GPCR trafficking in vivo and in some cases have led to new therapeutic approaches for treating human diseases that result from defects in GPCR trafficking. PMID:19679364

  15. 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

  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. G-protein-coupled receptors, Hedgehog signaling and primary cilia.

    PubMed

    Mukhopadhyay, Saikat; Rohatgi, Rajat

    2014-09-01

    The Hedgehog (Hh) pathway has become an important model to study the cell biology of primary cilia, and reciprocally, the study of ciliary processes provides an opportunity to solve longstanding mysteries in the mechanism of vertebrate Hh signal transduction. The cilium is emerging as an unique compartment for G-protein-coupled receptor (GPCR) signaling in many systems. Two members of the GPCR family, Smoothened and Gpr161, play important roles in the Hh pathway. We review the current understanding of how these proteins may function to regulate Hh signaling and also highlight some of the critical unanswered questions being tackled by the field. Uncovering GPCR-regulated mechanisms important in Hh signaling may provide therapeutic strategies against the Hh pathway that plays important roles in development, regeneration and cancer.

  18. Therapeutic antibodies directed at G protein-coupled receptors.

    PubMed

    Hutchings, Catherine J; Koglin, Markus; Marshall, Fiona H

    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 over-expressing 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.

  19. A Social Network Comparison of Low-Income Black and White Newlywed Couples.

    PubMed

    Jackson, Grace L; Kennedy, David; Bradbury, Thomas N; Karney, Benjamin R

    2014-10-01

    Relative to White families, Black families have been described as relying on extended social networks to compensate for other social and economic disadvantages. The presence or absence of supportive social networks should be especially relevant to young couples entering marriage, but to date there has been little effort to describe the social networks of comparable Black and White newlyweds. The current study addressed this gap by drawing on interviews with 57 first-married newlyweds from low-income communities to compare the composition and structure of Black and White couples' duocentric social networks. The results indicated that low-income Black couples entered marriage at a social disadvantage relative to White couples, with more family relationships but fewer positive relationships and fewer sources of emotional support (for wives), fewer connections to married individuals, and fewer shared relationships between spouses. Black couples' relative social disadvantages persisted even when various economic and demographic variables were controlled.

  20. Migration and the Gendered Origin of Migrant Networks among Couples in Mexico.

    PubMed

    Creighton, Mathew J; Riosmena, Fernando

    2013-03-01

    We investigate how the matrilineal vs. patrilineal origin of Mexican couples' migrant networks are associated with the aspirations to migrate and the subsequent migration behavior of each spouse. Using longitudinal data from the Mexican Family Life Survey (2002-2005) on 3,923 married couples across 139 municipalities; we estimate multi-level logistic regressions predicting aspirations to migrate to the United States for each spouse and the subsequent migration behavior of the couple in the inter-wave period. The networks of both ego and spouse are associated with U.S. migration aspirations although they matter more for the person from which they originate. Only matrilineal networks predicted a subsequent move to the U.S. for men and women/couples, who were assessed jointly. Matrilineal networks are instrumental in the migration process, particularly of the couple. As such, they could prove instrumental in helping understand the migration motivations and dynamics of individuals and families.

  1. Synchronized states and multistability in a random network of coupled discontinuous maps

    SciTech Connect

    Nag, Mayurakshi; Poria, Swarup

    2015-08-15

    The synchronization behavior of coupled chaotic discontinuous maps over a ring network with dynamic random connections is reported in this paper. It is observed that random rewiring stabilizes one of the two strongly unstable fixed points of the local map. Depending on initial conditions, the network synchronizes to different unstable fixed points, which signifies the existence of synchronized multistability in the complex network. Moreover, the length of discontinuity of the local map has an important role in generating windows of different synchronized fixed points. Synchronized fixed point and synchronized periodic orbits are found in the network depending on coupling strength and different parameter values of the local map. We have identified the existence of period subtracting bifurcation with respect to coupling strength in the network. The range of coupling strength for the occurrence of synchronized multistable spatiotemporal fixed points is determined. This range strongly depends upon the dynamic rewiring probability and also on the local map.

  2. Synchronization and local convergence analysis of networks with dynamic diffusive coupling

    NASA Astrophysics Data System (ADS)

    Burbano Lombana, Daniel Alberto; di Bernardo, Mario

    2016-11-01

    In this paper, we address the problem of achieving synchronization in networks of nonlinear units coupled by dynamic diffusive terms. We present two types of couplings consisting of a static linear term, corresponding to the diffusive coupling, and a dynamic term which can be either the integral or the derivative of the sum of the mismatches between the states of neighbouring agents. The resulting dynamic coupling strategy is a distributed proportional-integral (PI) or a proportional-derivative (PD) law that is shown to be effective in improving the network synchronization performance, for example, when the dynamics at nodes are nonidentical. We assess the stability of the network by extending the classical Master Stability Function approach to the case where the links are dynamic ones of PI/PD type. We validate our approach via a set of representative examples including networks of chaotic Lorenz and networks of nonlinear mechanical systems.

  3. Observer-based synchronization in complex dynamical networks with nonsymmetric coupling

    NASA Astrophysics Data System (ADS)

    Wu, Jianshe; Jiao, Licheng

    2007-12-01

    Based on a general complex dynamical network model with nonsymmetric coupling, some criteria for synchronization are proposed based on the approach of state observer design. Unlike the nonobserver-based dynamical networks, where the coupling between two connected nodes is defined by an inner coupling matrix and full state coupling is typically needed, in this paper, smaller amount of coupling variables or even only a scalar output signal of each node is needed to synchronize the network. Unlike the commonly researched complex network model, where the coupling between nodes is symmetric, here, in our network model, the coupling configuration matrix is not assumed to be symmetric and may have complex eigenvalues. The matrix Jordan canonical formalization method is used instead of the matrix diagonalization method, so in our synchronization criteria, the coupling configuration matrix is not required to be diagonalizable. Especially, the proposed step-by-step approach is simpler in computation than the existent ones, which usually rely heavily on numerical toolbox, and may be done by hand completely. An example is given to illustrate the step-by-step approach, in which each node is a two-dimensional dynamical limit cycle oscillator system consisting of a two-cell cellular neural network, and numerical simulations are also done to verify the results of design.

  4. Synchronization investigation of the network group constituted by the nearest neighbor networks under inner and outer synchronous couplings

    NASA Astrophysics Data System (ADS)

    Li, Ting-Ting; Li, Cheng-Ren; Wang, Chen; He, Fang-Jun; Zhou, Guang-Ye; Sun, Jing-Chang; Han, Fei

    2016-12-01

    A new synchronization technique of inner and outer couplings is proposed in this work to investigate the synchronization of network group. Some Haken-Lorenz lasers with chaos behaviors are taken as the nodes to construct a few nearest neighbor complex networks and those sub-networks are also connected to form a network group. The effective node controllers are designed based on Lyapunov function and the complete synchronization among the sub-networks is realized perfectly under inner and outer couplings. The work is of potential applications in the cooperation output of lasers and the communication network. Project supported by the National Natural Science Foundation of China (Grant No. 11004092), the Natural Science Foundation of Liaoning Province, China (Grant Nos. 2015020079 and 201602455), and the Foundation of Education Department of Liaoning Province, China (Grant No. L201683665)

  5. 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

  6. 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.

  7. Oligomerization of G protein-coupled receptors: A reality

    PubMed Central

    Ferré, Sergi; Franco, Rafael

    2009-01-01

    As reviewed in the present issue, we have now an important amount of experimental evidence that indicates that G protein-coupled receptor (GPCR) oligomerization, including homo- and heteromerization, is a general phenomenon. Receptor heteromers possess unique biochemical characteristics that are demonstrably different from those of its individual components (protomers). Those properties include allosteric modulations between protomers, changes in ligand recognition, G protein-coupling and trafficking. The discovery of GPCR oligomers have been related to the parallel discovery and application of a variety of resonance energy transfer (RET) techniques, such as bioluminescence, fluorescence and sequential RET (BRET, FRET and SRET, respectively), time resolved FRET (T-FRET) and fluorescence recovery after photobleaching (FRAP) microscopy. However, RET techniques are difficult to implement in native tissues. For receptor heteromers, indirect approaches, such as the determination of a unique biochemical characteristic (‘biochemical fingerprint’), are allowing their identification in native tissues and their use as targets for drug development. Dopamine and opioid receptor heteromers are being the focus of intense research, which is related to the possible multiple applications of their putative ligands in pathological conditions, which include basal ganglia disorders, schizophrenia, drug addiction and pain. PMID:20015687

  8. Evolution: A Guide to Perturb Protein Function and Networks

    PubMed Central

    Lichtarge, Olivier; Wilkins, Angela

    2010-01-01

    Summary Protein interactions give rise to networks that control cell fate in health and disease; selective means to probe these interactions are therefore of wide interest. We discuss here Evolutionary Tracing (ET), a comparative method to identify protein functional sites and to guide experiments that selectively block, recode, or mimic their amino acid determinants. These studies suggest, in principle, a scalable approach to perturb individual links in protein networks. PMID:20444593

  9. Hydrophobic, Hydrophilic, and Charged Amino Acid Networks within Protein

    PubMed Central

    Aftabuddin, Md.; Kundu, S.

    2007-01-01

    The native three-dimensional structure of a single protein is determined by the physicochemical nature of its constituent amino acids. The 20 different types of amino acids, depending on their physicochemical properties, can be grouped into three major classes: hydrophobic, hydrophilic, and charged. The anatomy of the weighted and unweighted networks of hydrophobic, hydrophilic, and charged residues separately for a large number of proteins were studied. Results showed that the average degree of the hydrophobic networks has a significantly larger value than that of hydrophilic and charged networks. The average degree of the hydrophilic networks is slightly higher than that of the charged networks. The average strength of the nodes of hydrophobic networks is nearly equal to that of the charged network, whereas that of hydrophilic networks has a smaller value than that of hydrophobic and charged networks. The average strength for each of the three types of networks varies with its degree. The average strength of a node in a charged network increases more sharply than that of the hydrophobic and hydrophilic networks. Each of the three types of networks exhibits the “small-world” property. Our results further indicate that the all-amino-acids networks and hydrophobic networks are of assortative type. Although most of the hydrophilic and charged networks are of the assortative type, few others have the characteristics of disassortative mixing of the nodes. We have further observed that all-amino-acids networks and hydrophobic networks bear the signature of hierarchy, whereas the hydrophilic and charged networks do not have any hierarchical signature. PMID:17172302

  10. Presynaptic G Protein-Coupled Receptors: Gatekeepers of Addiction?

    PubMed Central

    Johnson, Kari A.; Lovinger, David M.

    2016-01-01

    Drug abuse and addiction cause widespread social and public health problems, and the neurobiology underlying drug actions and drug use and abuse is an area of intensive research. Drugs of abuse alter synaptic transmission, and these actions contribute to acute intoxication as well as the chronic effects of abused substances. Transmission at most mammalian synapses involves neurotransmitter activation of two receptor subtypes, ligand-gated ion channels that mediate fast synaptic responses and G protein-coupled receptors (GPCRs) that have slower neuromodulatory actions. The GPCRs represent a large proportion of neurotransmitter receptors involved in almost all facets of nervous system function. In addition, these receptors are targets for many pharmacotherapeutic agents. Drugs of abuse directly or indirectly affect neuromodulation mediated by GPCRs, with important consequences for intoxication, drug taking and responses to prolonged drug exposure, withdrawal and addiction. Among the GPCRs are several subtypes involved in presynaptic inhibition, most of which are coupled to the Gi/o class of G protein. There is increasing evidence that these presynaptic Gi/o-coupled GPCRs have important roles in the actions of drugs of abuse, as well as behaviors related to these drugs. This topic will be reviewed, with particular emphasis on receptors for three neurotransmitters, Dopamine (DA; D1- and D2-like receptors), Endocannabinoids (eCBs; CB1 receptors) and glutamate (group II metabotropic glutamate (mGlu) receptors). The focus is on recent evidence from laboratory animal models (and some evidence in humans) implicating these receptors in the acute and chronic effects of numerous abused drugs, as well as in the control of drug seeking and taking. The ability of drugs targeting these receptors to modify drug seeking behavior has raised the possibility of using compounds targeting these receptors for addiction pharmacotherapy. This topic is also discussed, with emphasis on

  11. 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.

  12. Synchronization of coupled large-scale Boolean networks

    SciTech Connect

    Li, Fangfei

    2014-03-15

    This paper investigates the complete synchronization and partial synchronization of two large-scale Boolean networks. First, the aggregation algorithm towards large-scale Boolean network is reviewed. Second, the aggregation algorithm is applied to study the complete synchronization and partial synchronization of large-scale Boolean networks. Finally, an illustrative example is presented to show the efficiency of the proposed results.

  13. Synchronization of coupled large-scale Boolean networks

    NASA Astrophysics Data System (ADS)

    Li, Fangfei

    2014-03-01

    This paper investigates the complete synchronization and partial synchronization of two large-scale Boolean networks. First, the aggregation algorithm towards large-scale Boolean network is reviewed. Second, the aggregation algorithm is applied to study the complete synchronization and partial synchronization of large-scale Boolean networks. Finally, an illustrative example is presented to show the efficiency of the proposed results.

  14. Weighted and unweighted network of amino acids within protein

    NASA Astrophysics Data System (ADS)

    Aftabuddin, Md.; Kundu, Sudip

    2006-09-01

    The information regarding the structure of a single protein is encoded in the network of interacting amino acids considered as nodes. If any two atoms from two different amino acids (nodes) are within higher cut-off distance of London-van der Waals forces, the amino acids are considered to be linked or connected. Several atoms of any amino acids in a protein may be within the above prescribed distance of several atoms of another amino acid resulting in possible multiple links between them. These multiple links are the basis of the weight of the connectivity in a protein network. Each protein has been considered as a weighted and an unweighted network of amino acids. A total of forty nine protein structures that covers the three branches of life on earth has been analyzed and several network properties have been studied. The probability degree and strength distributions of network connectivity have been obtained. It has been observed that the average strength of amino acid node depends on its degree. The results show that the average clustering coefficient of weighted network is less than that of unweighted network. It implies that the topological clustering is generated by edges with low weights. The power-law behavior of clustering coefficients of weighted and unweighted networks as a function of degree indicates that they have signatures of hierarchy. It has also been observed that the network is of assortative type.

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

    PubMed

    Barber, Alan E; Babbitt, Patricia C

    2012-11-01

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

  16. Evolutionary pressure on the topology of protein interface interaction networks.

    PubMed

    Johnson, Margaret E; Hummer, Gerhard

    2013-10-24

    The densely connected structure of protein-protein interaction (PPI) networks reflects the functional need of proteins to cooperate in cellular processes. However, PPI networks do not adequately capture the competition in protein binding. By contrast, the interface interaction network (IIN) studied here resolves the modular character of protein-protein binding and distinguishes between simultaneous and exclusive interactions that underlie both cooperation and competition. We show that the topology of the IIN is under evolutionary pressure, and we connect topological features of the IIN to specific biological functions. To reveal the forces shaping the network topology, we use a sequence-based computational model of interface binding along with network analysis. We find that the more fragmented structure of IINs, in contrast to the dense PPI networks, arises in large part from the competition between specific and nonspecific binding. The need to minimize nonspecific binding favors specific network motifs, including a minimal number of cliques (i.e., fully connected subgraphs) and many disconnected fragments. Validating the model, we find that these network characteristics are closely mirrored in the IIN of clathrin-mediated endocytosis. Features unexpected on the basis of our motif analysis are found to indicate either exceptional binding selectivity or important regulatory functions.

  17. 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. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. G protein-coupled receptor mutations and human genetic disease.

    PubMed

    Thompson, Miles D; Hendy, Geoffrey N; Percy, Maire E; Bichet, Daniel G; Cole, David E C

    2014-01-01

    Genetic variations in G protein-coupled receptor genes (GPCRs) disrupt GPCR function in a wide variety of human genetic diseases. In vitro strategies and animal models have been used to identify the molecular pathologies underlying naturally occurring GPCR mutations. Inactive, overactive, or constitutively active receptors have been identified that result in pathology. These receptor variants may alter ligand binding, G protein coupling, receptor desensitization and receptor recycling. Receptor systems discussed include rhodopsin, thyrotropin, parathyroid hormone, melanocortin, follicle-stimulating hormone (FSH), luteinizing hormone, gonadotropin-releasing hormone (GNRHR), adrenocorticotropic hormone, vasopressin, endothelin-β, purinergic, and the G protein associated with asthma (GPRA or neuropeptide S receptor 1 (NPSR1)). The role of activating and inactivating calcium-sensing receptor (CaSR) mutations is discussed in detail with respect to familial hypocalciuric hypercalcemia (FHH) and autosomal dominant hypocalemia (ADH). The CASR mutations have been associated with epilepsy. Diseases caused by the genetic disruption of GPCR functions are discussed in the context of their potential to be selectively targeted by drugs that rescue altered receptors. Examples of drugs developed as a result of targeting GPCRs mutated in disease include: calcimimetics and calcilytics, therapeutics targeting melanocortin receptors in obesity, interventions that alter GNRHR loss from the cell surface in idiopathic hypogonadotropic hypogonadism and novel drugs that might rescue the P2RY12 receptor congenital bleeding phenotype. De-orphanization projects have identified novel disease-associated receptors, such as NPSR1 and GPR35. The identification of variants in these receptors provides genetic reagents useful in drug screens. Discussion of the variety of GPCRs that are disrupted in monogenic Mendelian disorders provides the basis for examining the significance of common

  19. Topology analysis and visualization of Potyvirus protein-protein interaction network.

    PubMed

    Bosque, Gabriel; Folch-Fortuny, Abel; Picó, Jesús; Ferrer, Alberto; Elena, Santiago F

    2014-11-20

    One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses. After collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins. By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects. Our findings confirm that potyvirus protein-protein interaction networks are highly connected, with

  20. Large-scale production and protein engineering of G protein-coupled receptors for structural studies.

    PubMed

    Milić, Dalibor; Veprintsev, Dmitry B

    2015-01-01

    Structural studies of G protein-coupled receptors (GPCRs) gave insights into molecular mechanisms of their action and contributed significantly to molecular pharmacology. This is primarily due to technical advances in protein engineering, production and crystallization of these important receptor targets. On the other hand, NMR spectroscopy of GPCRs, which can provide information about their dynamics, still remains challenging due to difficulties in preparation of isotopically labeled receptors and their low long-term stabilities. In this review, we discuss methods used for expression and purification of GPCRs for crystallographic and NMR studies. We also summarize protein engineering methods that played a crucial role in obtaining GPCR crystal structures.

  1. Large-scale production and protein engineering of G protein-coupled receptors for structural studies

    PubMed Central

    Milić, Dalibor; Veprintsev, Dmitry B.

    2015-01-01

    Structural studies of G protein-coupled receptors (GPCRs) gave insights into molecular mechanisms of their action and contributed significantly to molecular pharmacology. This is primarily due to technical advances in protein engineering, production and crystallization of these important receptor targets. On the other hand, NMR spectroscopy of GPCRs, which can provide information about their dynamics, still remains challenging due to difficulties in preparation of isotopically labeled receptors and their low long-term stabilities. In this review, we discuss methods used for expression and purification of GPCRs for crystallographic and NMR studies. We also summarize protein engineering methods that played a crucial role in obtaining GPCR crystal structures. PMID:25873898

  2. The emerging mutational landscape of G proteins and G-protein-coupled receptors in cancer.

    PubMed

    O'Hayre, Morgan; Vázquez-Prado, José; Kufareva, Irina; Stawiski, Eric W; Handel, Tracy M; Seshagiri, Somasekar; Gutkind, J Silvio

    2013-06-01

    Aberrant expression and activity of G proteins and G-protein-coupled receptors (GPCRs) are frequently associated with tumorigenesis. Deep sequencing studies show that 4.2% of tumours carry activating mutations in GNAS (encoding Gαs), and that oncogenic activating mutations in genes encoding Gαq family members (GNAQ or GNA11) are present in ~66% and ~6% of melanomas arising in the eye and skin, respectively. Furthermore, nearly 20% of human tumours harbour mutations in GPCRs. Many human cancer-associated viruses also express constitutively active viral GPCRs. These studies indicate that G proteins, GPCRs and their linked signalling circuitry represent novel therapeutic targets for cancer prevention and treatment.

  3. G protein βγ subunits: Central mediators of G protein-coupled receptor signaling

    PubMed Central

    Smrcka, A. V.

    2008-01-01

    G protein βγ subunits are central participants in G protein-coupled receptor signaling pathways. They interact with receptors, G protein α subunits and downstream targets to coordinate multiple, different GPCR functions. Much is known about the biology of Gβγ subunits but mysteries remain. Here, we will review what is known about general aspects of structure and function of Gβγ as well as discuss emerging mechanisms for regulation of Gβγ signaling. Recent data suggest that Gβγ is a potential therapeutic drug target. Thus, a thorough understanding of the molecular and physiological functions of Gβγ has significant implications. PMID:18488142

  4. Inferring structural connectivity using Ising couplings in models of neuronal networks.

    PubMed

    Kadirvelu, Balasundaram; Hayashi, Yoshikatsu; Nasuto, Slawomir J

    2017-08-15

    Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity.

  5. Hormone resistance caused by mutations in G proteins and G protein-coupled receptors.

    PubMed

    Spiegel, A M

    1999-04-01

    G proteins couple receptors for many hormones to effectors that regulate second messenger metabolism. Several endocrine disorders have been shown to be caused by either loss or gain of function mutations in G proteins or G protein-coupled receptors. Pseudohypoparathyroidism (PHP), the first described example of a hormone resistance disorder, is characterized by renal resistance to parathyroid hormone (PTH) proximal to generation of the second messenger, cAMP. In PHP Ia there is more generalized hormone resistance (PTH, TSH, gonadotropins) and associated abnormal physical features, Albright hereditary osteodystrophy (AHO). Subjects with PHP Ib are normal in appearance and resistant exclusively to PTH. Germline loss of function mutations have been identified in the Gs-alpha gene in PHP Ia, and recent evidence suggests that the Gs-alpha gene is paternally imprinted in a tissue-specific manner. In PHP Ib, several studies have excluded PTH receptor gene mutations, and the molecular basis has not yet been defined.

  6. 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

  7. Topological effects on dynamics in complex pulse-coupled networks of integrate-and-fire type

    NASA Astrophysics Data System (ADS)

    Shkarayev, Maxim S.; Kovačič, Gregor; Cai, David

    2012-03-01

    For a class of integrate-and-fire, pulse-coupled networks with complex topology, we study the dependence of the pulse rate on the underlying architectural connectivity statistics. We derive the distribution of the pulse rate from this dependence and determine when the underlying scale-free architectural connectivity gives rise to a scale-free pulse-rate distribution. We identify the scaling of the pairwise coupling between the dynamical units in this network class that keeps their pulse rates bounded in the infinite-network limit. In the process, we determine the connectivity statistics for a specific scale-free network grown by preferential attachment.

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

    PubMed

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

    2017-07-25

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

  9. 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.

  10. G protein-coupled receptors participate in cytokinesis.

    PubMed

    Zhang, Xin; Bedigian, Anne V; Wang, Wenchao; Eggert, Ulrike S

    2012-10-01

    Cytokinesis, the last step during cell division, is a highly coordinated process that involves the relay of signals from both the outside and inside of the cell. We have a basic understanding of how cells regulate internal events, but how cells respond to extracellular cues is less explored. In a systematic RNAi screen of G protein-coupled receptors (GPCRs) and their effectors, we found that some GPCRs are involved in cytokinesis. RNAi knockdown of these GPCRs caused increased binucleated cell formation, and live cell imaging showed that most formed midbodies but failed at the abscission stage. OR2A4 (olfactory receptor, family 2, subfamily A, member 4) localized to cytokinetic structures in cells and its knockdown caused cytokinesis failure at an earlier stage, likely due to effects on the actin cytoskeleton. Identifying the downstream components that transmit GPCR signals during cytokinesis will be the next step and we show that GIPC1 (GIPC PDZ domain containing family, member 1), an adaptor protein for GPCRs, may play a part. RNAi knockdown of GIPC1 significantly increased binucleated cell formation. Understanding the molecular details of GPCRs and their interaction proteins in cytokinesis regulation will give us important clues about GPCRs signaling as well as how cells communicate with their environment during division. Copyright © 2012 Wiley Periodicals, Inc.

  11. 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

  12. Equilibrium Fluctuation Relations for Voltage Coupling in Membrane Proteins

    PubMed Central

    Kim, Ilsoo; Warshel, Arieh

    2015-01-01

    A general theoretical framework is developed to account for the effects of an external potential on the energetics of membrane proteins. The framework is based on the free energy relation between two (forward/backward) probability densities, which was recently generalized to non-equilibrium processes, culminating in the work-fluctuation theorem. Starting from the probability densities of the conformational states along the reaction coordinate of “voltage coupling”, we investigate several interconnected free energy relations between these two conformational states, considering voltage activation of ion channels. The free energy difference at zero membrane potential (i.e., between the two “non-equilibrium” conformational states) is shown to be equivalent to the free energy difference between the two “equilibrium” conformational states along the one-dimensional reaction coordinate of voltage coupling. Furthermore, the requirement that the application of linear response approximation to the free energy functions (free energies) of voltage coupling should satisfy the general free energy relations, yields a novel expression for the gating charge in terms of other experimentally measurable quantities. This connection is familiar in statistical mechanics, known as the equilibrium fluctuation-response relation. The theory is illustrated by considering the movement of a unit charge within the membrane under the influence of an external potential, using a coarse-graining (CG) model of membrane proteins, which includes the membrane, the electrolytes and the electrodes. The CG model yields Marcus–type voltage dependent free energy parabolas for the two conformational states, which allow for quantitative estimations of an equilibrium free energy difference, a free energy of barrier, and the voltage dependency of channel activation (Q-V curve) for the unit charge movement. In addition, our analysis offers a quantitative rationale for the correlation between the free

  13. Strong effects of network architecture in the entrainment of coupled oscillator systems

    NASA Astrophysics Data System (ADS)

    Kori, Hiroshi; Mikhailov, Alexander S.

    2006-12-01

    Random networks of coupled phase oscillators, representing an approximation for systems of coupled limit-cycle oscillators, are considered. Entrainment of such networks by periodic external forcing applied to a subset of their elements is numerically and analytically investigated. For a large class of interaction functions, we find that the entrainment window with a tongue shape becomes exponentially narrow for networks with higher hierarchical organization. However, the entrainment is significantly facilitated if the networks are directionally biased—i.e., closer to the feedforward networks. Furthermore, we show that the networks with high entrainment ability can be constructed by evolutionary optimization processes. The neural network structure of the master clock of the circadian rhythm in mammals is discussed from the viewpoint of our results.

  14. Percolation-cascading in multilayer heterogeneous network with different coupling preference

    NASA Astrophysics Data System (ADS)

    Juan, Wang Xiao; Ze, Guo Shi; Lei, Jin; Zhen, Wang

    2017-04-01

    Multilayer network with different coupling preference is often used to study complex systems. In this paper we mainly focus on percolation-cascading process in multilayer heterogeneous networks including BA-BA, ER-ER, BA-ER. We put forward our percolation-cascading model and from calculation of Xn and Zn we can get resulting size of mutual largest component in different layers. Refer to Newman's definition of assortativity we design a stochastic structural algorithm to generate dependency edges between layers from which we get networks with different coupling coefficient. Simulation shows that assortative network in percolation-cascading process performs better and when the layers of multilayer network meet with BA network the robustness of the network increases.

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

    PubMed

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

    2017-03-27

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

  16. Signalling-Dependent Interactions Between the Kinase-Coupling Protein CheW and Chemoreceptors in Living Cells

    PubMed Central

    Pedetta, Andrea; Parkinson, John S.; Studdert, Claudia A.

    2014-01-01

    Summary Chemical signals sensed on the periplasmic side of bacterial cells by transmembrane chemoreceptors are transmitted to the flagellar motors via the histidine kinase CheA, which controls the phosphorylation level of the effector protein CheY. Chemoreceptor arrays comprise remarkably stable supramolecular structures in which thousands of chemoreceptors are networked through interactions between their cytoplasmic tips, CheA, and the small coupling protein CheW. To explore the conformational changes that occur within this protein assembly during signalling, we used in vivo crosslinking methods to detect close interactions between the coupling protein CheW and the serine receptor Tsr in intact E. coli cells. We identified two signal-sensitive contacts between CheW and the cytoplasmic tip of Tsr. Our results suggest that ligand binding triggers changes in the receptor that alter its signalling contacts with CheW (and/or CheA). PMID:25060668

  17. 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.

  18. Protein-Protein Interaction (PPI) Network: Recent Advances in Drug Discovery.

    PubMed

    Athanasios, Alexiou; Charalampos, Vairaktarakis; Vasileios, Tsiamis; Ashraf, Ghulam Md

    2017-01-01

    The investigation of the cellular components, their interactions and related functions constitute the major conditions in order to understand the cell as an integrated system. More specifically, the Protein-Protein Interactions and the obtained networks are very important in the majority of biological functions and processes, while most of the proteins appear to activate their functionalities through their interaction. Our in depth review analysis, include Sixty-five peer-reviewed research and review studies from several bibliographic databases. The most significant components were fully described, filtered, combined and analyzed in order to provide documented proofs on the Protein-Protein Interaction Network' applications in biomedicine. The Protein-Protein Interaction Network' alignment and mapping give the opportunity of further knowledge extraction concerning the evolutionary relationships between the species through conserved pathways and protein complexes. Additionally, Protein-Protein Interaction Network information has been demonstrated to be able to predict functionally orthologous proteins within sequence homology clusters. Our review analysis concluded that, while Protein- Protein Interaction was used to be characterized just by their large and plain interacting surfaces, they were considered inapplicable for drug discovery studies for a long time. The present review explores multiple technologies implicated in Protein-Protein Interaction Networks, implicating their potential role in drug discovery mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Evolution of a protein domain interaction network

    NASA Astrophysics Data System (ADS)

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

    2010-01-01

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

  20. Practical approximation method for firing-rate models of coupled neural networks with correlated inputs

    NASA Astrophysics Data System (ADS)

    Barreiro, Andrea K.; Ly, Cheng

    2017-08-01

    Rapid experimental advances now enable simultaneous electrophysiological recording of neural activity at single-cell resolution across large regions of the nervous system. Models of this neural network activity will necessarily increase in size and complexity, thus increasing the computational cost of simulating them and the challenge of analyzing them. Here we present a method to approximate the activity and firing statistics of a general firing rate network model (of the Wilson-Cowan type) subject to noisy correlated background inputs. The method requires solving a system of transcendental equations and is fast compared to Monte Carlo simulations of coupled stochastic differential equations. We implement the method with several examples of coupled neural networks and show that the results are quantitatively accurate even with moderate coupling strengths and an appreciable amount of heterogeneity in many parameters. This work should be useful for investigating how various neural attributes qualitatively affect the spiking statistics of coupled neural networks.

  1. Core and periphery structures in protein interaction networks

    PubMed Central

    Luo, Feng; Li, Bo; Wan, Xiu-Feng; Scheuermann, Richard H

    2009-01-01

    Background Characterizing the structural properties of protein interaction networks will help illuminate the organizational and functional relationships among elements in biological systems. Results In this paper, we present a systematic exploration of the core/periphery structures in protein interaction networks (PINs). First, the concepts of cores and peripheries in PINs are defined. Then, computational methods are proposed to identify two types of cores, k-plex cores and star cores, from PINs. Application of these methods to a yeast protein interaction network has identified 110 k-plex cores and 109 star cores. We find that the k-plex cores consist of either "party" proteins, "date" proteins, or both. We also reveal that there are two classes of 1-peripheral proteins: "party" peripheries, which are more likely to be part of protein complex, and "connector" peripheries, which are more likely connected to different proteins or protein complexes. Our results also show that, besides connectivity, other variations in structural properties are related to the variation in biological properties. Furthermore, the negative correlation between evolutionary rate and connectivity are shown toysis. Moreover, the core/periphery structures help to reveal the existence of multiple levels of protein expression dynamics. Conclusion Our results show that both the structure and connectivity can be used to characterize topological properties in protein interaction networks, illuminating the functional organization of cellular systems. PMID:19426456

  2. 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 integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

  3. Solvated dissipative electro-elastic network model of hydrated proteins.

    PubMed

    Martin, Daniel R; Matyushov, Dmitry V

    2012-10-28

    Elastic network models coarse grain proteins into a network of residue beads connected by springs. We add dissipative dynamics to this mechanical system by applying overdamped Langevin equations of motion to normal-mode vibrations of the network. In addition, the network is made heterogeneous and softened at the protein surface by accounting for hydration of the ionized residues. Solvation changes the network Hessian in two ways. Diagonal solvation terms soften the spring constants and off-diagonal dipole-dipole terms correlate displacements of the ionized residues. The model is used to formulate the response functions of the electrostatic potential and electric field appearing in theories of redox reactions and spectroscopy. We also formulate the dielectric response of the protein and find that solvation of the surface ionized residues leads to a slow relaxation peak in the dielectric loss spectrum, about two orders of magnitude slower than the main peak of protein relaxation. Finally, the solvated network is used to formulate the allosteric response of the protein to ion binding. The global thermodynamics of ion binding is not strongly affected by the network solvation, but it dramatically enhances conformational changes in response to placing a charge at the active site of the protein.

  4. A protein trisulfide couples dissimilatory sulfate reduction to energy conservation

    NASA Astrophysics Data System (ADS)

    Santos, André A.; Venceslau, Sofia S.; Grein, Fabian; Leavitt, William D.; Dahl, Christiane; Johnston, David T.; Pereira, Inês A. C.

    2015-12-01

    Microbial sulfate reduction has governed Earth’s biogeochemical sulfur cycle for at least 2.5 billion years. However, the enzymatic mechanisms behind this pathway are incompletely understood, particularly for the reduction of sulfite—a key intermediate in the pathway. This critical reaction is performed by DsrAB, a widespread enzyme also involved in other dissimilatory sulfur metabolisms. Using in vitro assays with an archaeal DsrAB, supported with genetic experiments in a bacterial system, we show that the product of sulfite reduction by DsrAB is a protein-based trisulfide, in which a sulfite-derived sulfur is bridging two conserved cysteines of DsrC. Physiological studies also reveal that sulfate reduction rates are determined by cellular levels of DsrC. Dissimilatory sulfate reduction couples the four-electron reduction of the DsrC trisulfide to energy conservation.

  5. Lysophospholipids and their G protein-coupled receptors in atherosclerosis

    PubMed Central

    Li, Ya-Feng; Li, Rong-Shan; Samuel, Sonia B.; Cueto, Ramon; Li, Xin-Yuan; Wang, Hong; Yang, Xiao-Feng

    2015-01-01

    Lysophospholipids (LPLs) are bioactive lipid-derived signaling molecules generated by the enzymatic and chemical processes of regiospecific phospholipases on substrates such as membrane phospholipids (PLs) and sphingolipids (SLs). They play a major role as extracellular mediators by activating G-protein coupled receptors (GPCRs) and stimulating diverse cellular responses from their signaling pathways. LPLs are involved in various pathologies of the vasculature system including coronary heart disease and hypertension. Many studies suggest the importance of LPLs in their association with the development of atherosclerosis, a chronic and severe vascular disease. This paper focuses on the pathophysiological effects of different lysophospholipids on atherosclerosis, which may promote the pathogenesis of myocardial infarction and strokes. Their atherogenic biological activities take place in vascular endothelial cells, vascular smooth muscle cells, fibroblasts, monocytes and macrophages, dendritic cells, T-lymphocytes, platelets, etc. PMID:26594106

  6. Anatomical profiling of G protein-coupled receptor expression

    PubMed Central

    Regard, Jean B.; Sato, Isaac T.; Coughlin, Shaun R.

    2008-01-01

    Summary G protein-coupled receptors (GPCRs) comprise the largest family of transmembrane signaling molecules and regulate a host of physiological and disease processes. To better understand the functions of GPCRs in vivo, we quantified transcript levels of 353 non-odorant GPCRs in 41 adult mouse tissues. Cluster analysis placed many GPCRs into anticipated anatomical and functional groups and predicted novel roles for less studied receptors. From one such prediction, we showed that the Gpr91 ligand succinate can regulate lipolysis in white adipose tissue suggesting that signaling by this citric acid cycle intermediate may regulate energy homeostasis. We also showed that pairwise analysis of GPCR expression across tissues may help predict drug side effects. This resource will aid studies to understand GPCR function in vivo and may assist in the identification of therapeutic targets. PMID:18984166

  7. Peptide drugs to target G protein-coupled receptors.

    PubMed

    Bellmann-Sickert, Kathrin; Beck-Sickinger, Annette G

    2010-09-01

    Major indications for use of peptide-based therapeutics include endocrine functions (especially diabetes mellitus and obesity), infectious diseases, and cancer. Whereas some peptide pharmaceuticals are drugs, acting as agonists or antagonists to directly treat cancer, others (including peptide diagnostics and tumour-targeting pharmaceuticals) use peptides to 'shuttle' a chemotherapeutic agent or a tracer to the tumour and allow sensitive imaging or targeted therapy. Significant progress has been made in the last few years to overcome disadvantages in peptide design such as short half-life, fast proteolytic cleavage, and low oral bioavailability. These advances include peptide PEGylation, lipidisation or multimerisation; the introduction of peptidomimetic elements into the sequences; and innovative uptake strategies such as liposomal, capsule or subcutaneous formulations. This review focuses on peptides targeting G protein-coupled receptors that are promising drug candidates or that have recently entered the pharmaceutical market.

  8. Lysophospholipid activation of G protein-coupled receptors.

    PubMed

    Mutoh, Tetsuji; Chun, Jerold

    2008-01-01

    One of the major lipid biology discoveries in last decade was the broad range of physiological activities of lysophospholipids that have been attributed to the actions of lysophospholipid receptors. The most well characterized lysophospholipids are lysophosphatidic acid (LPA) and sphingosine 1-phosphate (S1P). Documented cellular effects of these lipid mediators include growth-factor-like effects on cells, such as proliferation, survival, migration, adhesion, and differentiation. The mechanisms for these actions are attributed to a growing family of 7-transmembrane, G protein-coupled receptors (GPCRs). Their pathophysiological actions include immune modulation, neuropathic pain modulation, platelet aggregation, wound healing, vasopressor activity, and angiogenesis. Here we provide a brief introduction to receptor-mediated lysophospholipid signaling and physiology, and then discuss potential therapeutic roles in human diseases.

  9. Lysophospholipids and their G protein-coupled receptors in atherosclerosis.

    PubMed

    Li, Ya-Feng; Li, Rong-Shan; Samuel, Sonia B; Cueto, Ramon; Li, Xin-Yuan; Wang, Hong; Yang, Xiao-Feng

    2016-01-01

    Lysophospholipids (LPLs) are bioactive lipid-derived signaling molecules generated by the enzymatic and chemical processes of regiospecific phospholipases on substrates such as membrane phospholipids (PLs) and sphingolipids (SLs). They play a major role as extracellular mediators by activating G-protein coupled receptors (GPCRs) and stimulating diverse cellular responses from their signaling pathways. LPLs are involved in various pathologies of the vasculature system including coronary heart disease and hypertension. Many studies suggest the importance of LPLs in their association with the development of atherosclerosis, a chronic and severe vascular disease. This paper focuses on the pathophysiological effects of different lysophospholipids on atherosclerosis, which may promote the pathogenesis of myocardial infarction and strokes. Their atherogenic biological activities take place in vascular endothelial cells, vascular smooth muscle cells, fibroblasts, monocytes and macrophages, dendritic cells, T-lymphocytes, platelets, etc.

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

    PubMed Central

    Horn, F; Weare, J; Beukers, M W; Hörsch, S; Bairoch, A; Chen, W; Edvardsen, O; Campagne, F; Vriend, G

    1998-01-01

    The GPCRDB is a G protein-coupled receptor (GPCR) database system aimed at the collection and dissemination of GPCR related data. It holds sequences, mutant data and ligand binding constants as primary (experimental) data. Computationally derived data such as multiple sequence alignments, three dimensional models, phylogenetic trees and two dimensional visualization tools are added to enhance the database's usefulness. The GPCRDB is an EU sponsored project aimed at building a generic molecular class specific database capable of dealing with highly heterogeneous data. GPCRs were chosen as test molecules because of their enormous importance for medical sciences and due to the availability of so much highly heterogeneous data. The GPCRDB is available via the WWW at http://www.gpcr.org/7tm PMID:9399852

  11. 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.

  12. Engineering therapeutic antibodies targeting G-protein-coupled receptors.

    PubMed

    Jo, Migyeong; Jung, Sang Taek

    2016-02-05

    G-protein-coupled receptors (GPCRs) are one of the most attractive therapeutic target classes because of their critical roles in intracellular signaling and their clinical relevance to a variety of diseases, including cancer, infection and inflammation. However, high conformational variability, the small exposed area of extracellular epitopes and difficulty in the preparation of GPCR antigens have delayed both the isolation of therapeutic anti-GPCR antibodies as well as studies on the structure, function and biochemical mechanisms of GPCRs. To overcome the challenges in generating highly specific anti-GPCR antibodies with enhanced efficacy and safety, various forms of antigens have been successfully designed and employed for screening with newly emerged systems based on laboratory animal immunization and high-throughput-directed evolution.

  13. Membrane cholesterol access into a G-protein-coupled receptor

    NASA Astrophysics Data System (ADS)

    Guixà-González, Ramon; Albasanz, José L.; Rodriguez-Espigares, Ismael; Pastor, Manuel; Sanz, Ferran; Martí-Solano, Maria; Manna, Moutusi; Martinez-Seara, Hector; Hildebrand, Peter W.; Martín, Mairena; Selent, Jana

    2017-02-01

    Cholesterol is a key component of cell membranes with a proven modulatory role on the function and ligand-binding properties of G-protein-coupled receptors (GPCRs). Crystal structures of prototypical GPCRs such as the adenosine A2A receptor (A2AR) have confirmed that cholesterol finds stable binding sites at the receptor surface suggesting an allosteric role of this lipid. Here we combine experimental and computational approaches to show that cholesterol can spontaneously enter the A2AR-binding pocket from the membrane milieu using the same portal gate previously suggested for opsin ligands. We confirm the presence of cholesterol inside the receptor by chemical modification of the A2AR interior in a biotinylation assay. Overall, we show that cholesterol's impact on A2AR-binding affinity goes beyond pure allosteric modulation and unveils a new interaction mode between cholesterol and the A2AR that could potentially apply to other GPCRs.

  14. How Can Mutations Thermostabilize G-Protein-Coupled Receptors?

    PubMed

    Vaidehi, Nagarajan; Grisshammer, Reinhard; Tate, Christopher G

    2016-01-01

    Structures of over 30 different G-protein-coupled receptors (GPCRs) have advanced our understanding of cell signaling and have provided a foundation for structure-guided drug design. This exciting progress has required the development of three complementary methods to facilitate GPCR crystallization, one of which is the thermostabilization of receptors by systematic mutagenesis. However, the reason why a particular mutation, or combination of mutations, stabilizes the receptor is not always evident from a static crystal structure. Molecular dynamics (MD) simulations have been used to identify and estimate the energetic factors that affect thermostability through comparing the dynamics of the thermostabilized receptors with structure-based models of the wild-type receptor. The data indicate that receptors are stabilized through a combination of factors, including an increase in receptor rigidity, a decrease in collective motion, reduced stress at specific residues, and the presence of ordered water molecules. Predicting thermostabilizing mutations computationally represents a major challenge for the field.

  15. [G-protein-coupled receptors targeting: the allosteric approach].

    PubMed

    Sebag, Julien A; Pantel, Jacques

    2012-10-01

    G-protein-coupled receptors (GPCR) are a major family of drug targets. Essentially all drugs targeting these receptors on the market compete with the endogenous ligand (agonists or antagonists) for binding the receptor. Recently, non-competitive compounds binding to distinct sites from the cognate ligand were documented in various classes of these receptors. These compounds, called allosteric modulators, generally endowed of a better selectivity are able to modulate specifically the endogenous signaling of the receptor. To better understand the promising potential of this class of GPCRs targeting compounds, this review highlights the properties of allosteric modulators, the strategies used to identify them and the challenges associated with the development of these compounds.

  16. A G protein-coupled receptor for UDP-glucose.

    PubMed

    Chambers, J K; Macdonald, L E; Sarau, H M; Ames, R S; Freeman, K; Foley, J J; Zhu, Y; McLaughlin, M M; Murdock, P; McMillan, L; Trill, J; Swift, A; Aiyar, N; Taylor, P; Vawter, L; Naheed, S; Szekeres, P; Hervieu, G; Scott, C; Watson, J M; Murphy, A J; Duzic, E; Klein, C; Bergsma, D J; Wilson, S; Livi, G P

    2000-04-14

    Uridine 5'-diphosphoglucose (UDP-glucose) has a well established biochemical role as a glycosyl donor in the enzymatic biosynthesis of carbohydrates. It is less well known that UDP-glucose may possess pharmacological activity, suggesting that a receptor for this molecule may exist. Here, we show that UDP-glucose, and some closely related molecules, potently activate the orphan G protein-coupled receptor KIAA0001 heterologously expressed in yeast or mammalian cells. Nucleotides known to activate P2Y receptors were inactive, indicating the distinctly novel pharmacology of this receptor. The receptor is expressed in a wide variety of human tissues, including many regions of the brain. These data suggest that some sugar-nucleotides may serve important physiological roles as extracellular signaling molecules in addition to their familiar role in intermediary metabolism.

  17. Membrane cholesterol access into a G-protein-coupled receptor

    PubMed Central

    Guixà-González, Ramon; Albasanz, José L.; Rodriguez-Espigares, Ismael; Pastor, Manuel; Sanz, Ferran; Martí-Solano, Maria; Manna, Moutusi; Martinez-Seara, Hector; Hildebrand, Peter W.; Martín, Mairena; Selent, Jana

    2017-01-01

    Cholesterol is a key component of cell membranes with a proven modulatory role on the function and ligand-binding properties of G-protein-coupled receptors (GPCRs). Crystal structures of prototypical GPCRs such as the adenosine A2A receptor (A2AR) have confirmed that cholesterol finds stable binding sites at the receptor surface suggesting an allosteric role of this lipid. Here we combine experimental and computational approaches to show that cholesterol can spontaneously enter the A2AR-binding pocket from the membrane milieu using the same portal gate previously suggested for opsin ligands. We confirm the presence of cholesterol inside the receptor by chemical modification of the A2AR interior in a biotinylation assay. Overall, we show that cholesterol's impact on A2AR-binding affinity goes beyond pure allosteric modulation and unveils a new interaction mode between cholesterol and the A2AR that could potentially apply to other GPCRs. PMID:28220900

  18. A protein trisulfide couples dissimilatory sulfate reduction to energy conservation.

    PubMed

    Santos, André A; Venceslau, Sofia S; Grein, Fabian; Leavitt, William D; Dahl, Christiane; Johnston, David T; Pereira, Inês A C

    2015-12-18

    Microbial sulfate reduction has governed Earth's biogeochemical sulfur cycle for at least 2.5 billion years. However, the enzymatic mechanisms behind this pathway are incompletely understood, particularly for the reduction of sulfite-a key intermediate in the pathway. This critical reaction is performed by DsrAB, a widespread enzyme also involved in other dissimilatory sulfur metabolisms. Using in vitro assays with an archaeal DsrAB, supported with genetic experiments in a bacterial system, we show that the product of sulfite reduction by DsrAB is a protein-based trisulfide, in which a sulfite-derived sulfur is bridging two conserved cysteines of DsrC. Physiological studies also reveal that sulfate reduction rates are determined by cellular levels of DsrC. Dissimilatory sulfate reduction couples the four-electron reduction of the DsrC trisulfide to energy conservation.

  19. Protein sorting at the trans-Golgi network.

    PubMed

    Guo, Yusong; Sirkis, Daniel W; Schekman, Randy

    2014-01-01

    The trans-Golgi network (TGN) is an important cargo sorting station within the cell where newly synthesized proteins are packaged into distinct transport carriers that are targeted to various destinations. To maintain the fidelity of protein transport, elaborate protein sorting machinery is employed to mediate sorting of specific cargo proteins into distinct transport carriers. Protein sorting requires assembly of the cytosolic sorting machinery onto the TGN membrane and capture of cargo proteins. We review the cytosolic and transmembrane sorting machinery that function at the TGN and describe molecular interactions and regulatory mechanisms that enable accurate protein sorting. In addition, we highlight the importance of TGN sorting in physiology and disease.

  20. Recent advances in clustering methods for protein interaction networks

    PubMed Central

    2010-01-01

    The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed. PMID:21143777

  1. FunMod: a Cytoscape plugin for identifying functional modules in undirected protein-protein networks.

    PubMed

    Natale, Massimo; Benso, Alfredo; Di Carlo, Stefano; Ficarra, Elisa

    2014-08-01

    The characterization of the interacting behaviors of complex biological systems is a primary objective in protein-protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein-protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.

  2. Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.

    PubMed

    Qu, Hong; Yi, Zhang; Yang, Simon X

    2013-06-01

    Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

  3. RINGdb: an integrated database for G protein-coupled receptors and regulators of G protein signaling.

    PubMed

    Fang, Yu-Ching; Sun, Wei-Hsin; Wu, Li-Cheng; Huang, Hsien-Da; Juan, Hsueh-Fen; Horng, Jorng-Tzong

    2006-12-16

    Many marketed therapeutic agents have been developed to modulate the function of G protein-coupled receptors (GPCRs). The regulators of G-protein signaling (RGS proteins) are also being examined as potential drug targets. To facilitate clinical and pharmacological research, we have developed a novel integrated biological database called RINGdb to provide comprehensive and organized RGS protein and GPCR information. RINGdb contains information on mutations, tissue distributions, protein-protein interactions, diseases/disorders and other features, which has been automatically collected from the Internet and manually extracted from the literature. In addition, RINGdb offers various user-friendly query functions to answer different questions about RGS proteins and GPCRs such as their possible contribution to disease processes, the putative direct or indirect relationship between RGS proteins and GPCRs. RINGdb also integrates organized database cross-references to allow users direct access to detailed information. The database is now available at http://ringdb.csie.ncu.edu.tw/ringdb/. RINGdb is the only integrated database on the Internet to provide comprehensive RGS protein and GPCR information. This knowledge base will be useful for clinical research, drug discovery and GPCR signaling pathway research.

  4. Free-Energy Landscape of Protein-Ligand Interactions Coupled with Protein Structural Changes.

    PubMed

    Moritsugu, Kei; Terada, Tohru; Kidera, Akinori

    2017-02-02

    Protein-ligand interactions are frequently coupled with protein structural changes. Focusing on the coupling, we present the free-energy surface (FES) of the ligand-binding process for glutamine-binding protein (GlnBP) and its ligand, glutamine, in which glutamine binding accompanies large-scale domain closure. All-atom simulations were performed in explicit solvents by multiscale enhanced sampling (MSES), which adopts a multicopy and multiscale scheme to achieve enhanced sampling of systems with a large number of degrees of freedom. The structural ensemble derived from the MSES simulation yielded the FES of the coupling, described in terms of both the ligand's and protein's degrees of freedom at atomic resolution, and revealed the tight coupling between the two degrees of freedom. The derived FES led to the determination of definite structural states, which suggested the dominant pathways of glutamine binding to GlnBP: first, glutamine migrates via diffusion to form a dominant encounter complex with Arg75 on the large domain of GlnBP, through strong polar interactions. Subsequently, the closing motion of GlnBP occurs to form ligand interactions with the small domain, finally completing the native-specific complex structure. The formation of hydrogen bonds between glutamine and the small domain is considered to be a rate-limiting step, inducing desolvation of the protein-ligand interface to form the specific native complex. The key interactions to attain high specificity for glutamine, the "door keeper" existing between the two domains (Asp10-Lys115) and the "hydrophobic sandwich" formed between the ligand glutamine and Phe13/Phe50, have been successfully mapped on the pathway derived from the FES.

  5. Alignment of protein interaction networks by integer quadratic programming.

    PubMed

    Li, Zhenping; Wang, Yong; Zhang, Shihua; Zhang, Xiang-Sun; Chen, Luonan

    2006-01-01

    With more and more data on protein-protein interaction (PPI) network available, the discovery of conserved patterns in these networks becomes an increasingly important problem. In this paper, to find the conserved substructures, we develop an efficient algorithm for aligning PPI networks based on both the protein sequence similarity and the network architecture similarity, by using integer quadratic programming (IQP). Such an IQP can be relaxed into the corresponding quadratic programming (QP) which in the case of biological data sets almost always ensures the integer solution. Therefore, a QP algorithm can be adopted to efficiently solve this IQP with out any approximation, thereby making PPI network alignment tractable. From the viewpoint of graph theory, the proposed method can identify similar subsets between two graphs, which allow gaps for nodes and edges.

  6. G-protein-coupled receptor kinase 2 terminates G-protein-coupled receptor function in steroid hormone 20-hydroxyecdysone signaling

    PubMed Central

    Zhao, Wen-Li; Wang, Di; Liu, Chun-Yan; Zhao, Xiao-Fan

    2016-01-01

    G-protein-coupled receptors (GPCRs) transmit extracellular signals across the cell membrane. GPCR kinases (GRKs) desensitize GPCR signals in the cell membrane. However, the role and mechanism of GRKs in the desensitization of steroid hormone signaling are unclear. In this study, we propose that GRK2 is phosphorylated by protein kinase C (PKC) in response to induction by the steroid hormone 20-hydroxyecdysone (20E), which determines its translocation to the cell membrane of the lepidopteran Helicoverpa armigera. GRK2 protein expression is increased during the metamorphic stage because of induction by 20E. Knockdown of GRK2 in larvae causes accelerated pupation, an increase in 20E-response gene expression, and advanced apoptosis and metamorphosis. 20E induces translocation of GRK2 from the cytoplasm to the cell membrane via steroid hormone ecdysone-responsive GPCR (ErGPCR-2). GRK2 is phosphorylated by PKC on serine 680 after induction by 20E, which leads to the translocation of GRK2 to the cell membrane. GRK2 interacts with ErGPCR-2. These data indicate that GRK2 terminates the ErGPCR-2 function in 20E signaling in the cell membrane by a negative feedback mechanism. PMID:27412951

  7. Topological Influence On Network Of Coupled Chemical Oscillators

    NASA Astrophysics Data System (ADS)

    Zhao, Jie; Rericha, Erin; Vanderbilt Biophysics Collaboration

    2013-03-01

    Networks of interacting nodes are ubiquitous in biological and communication systems. Recently the manner of the network connections, be it through of activator or inhibitor signals, and the topology of the network has received theoretical attention with the goal of finding networks with optimal synchronization and information transmission properties. In preparation for building an experimental system to examine these predictions, we numerically explore networks of Belousov-Zhabotinsky oscillatory nodes connected through unidirectional links of activator species. We measure the time required for the nodes to synchronize as a function of the network topology. While we observe a trend of smaller synchronization times with increasing first non-zero eigen values, we find that the most important factor in determining synchronization time is the initial phase difference between the oscillators. We find that the synchronization times for a given network topology, as determined from a uniform distribution of initial phase differences, is best described with a skewed Gaussian. To better understand the factors underlying this distribution, we look at the synchronization times in a three-node network as a function of both initial conditions and model parameters.

  8. 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.

  9. Linear stability and the Braess paradox in coupled-oscillator networks and electric power grids.

    PubMed

    Coletta, Tommaso; Jacquod, Philippe

    2016-03-01

    We investigate the influence that adding a new coupling has on the linear stability of the synchronous state in coupled-oscillator networks. Using a simple model, we show that, depending on its location, the new coupling can lead to enhanced or reduced stability. We extend these results to electric power grids where a new line can lead to four different scenarios corresponding to enhanced or reduced grid stability as well as increased or decreased power flows. Our analysis shows that the Braess paradox may occur in any complex coupled system, where the synchronous state may be weakened and sometimes even destroyed by additional couplings.

  10. 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.

  11. Three-dimensional visualization of protein interaction networks.

    PubMed

    Han, Kyungsook; Byun, Yanga

    2004-03-01

    Protein interaction networks provide us with contextual information within which protein function can be interpreted and will assist many biomedical studies. We have developed a new force-directed layout algorithm for visualizing protein interactions in three-dimensional space. Our algorithm divides nodes into three groups based on their interacting properties: bi-connected sub-graph in the center, terminal nodes at the outermost region, and the rest in between them. Experimental results show that our algorithm efficiently generates a clear and aesthetically pleasing drawing of large-scale protein interaction networks and that it is an order of magnitude faster than other force-directed layouts.

  12. Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

    PubMed Central

    Peterson, G. Jack; Pressé, Steve; Peterson, Kristin S.; Dill, Ken A.

    2012-01-01

    We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein’s neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution. PMID:22768057

  13. Construction and analysis of the protein-protein interaction network related to essential hypertension

    PubMed Central

    2013-01-01

    Background Essential hypertension (EH) is a complex disease as a consequence of interaction between environmental factors and genetic background, but the pathogenesis of EH remains elusive. The emerging tools of network medicine offer a platform to explore a complex disease at system level. In this study, we aimed to identify the key proteins and the biological regulatory pathways involving in EH and further to explore the molecular connectivities between these pathways by the topological analysis of the Protein-protein interaction (PPI) network. Result The extended network including one giant network consisted of 535 nodes connected via 2572 edges and two separated small networks. 27 proteins with high BC and 28 proteins with large degree have been identified. NOS3 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for blood pressure (BP) and the crosstalk between them. Finally, the robustness of NOS3 as central protein and accuracy of backbone were validated by 287 test networks. Conclusion Our finding suggests that blood pressure variation is orchestrated by an integrated PPI network centered on NOS3. PMID:23587307

  14. Chimera patterns induced by distance-dependent power-law coupling in ecological networks.

    PubMed

    Banerjee, Tanmoy; Dutta, Partha Sharathi; Zakharova, Anna; Schöll, Eckehard

    2016-09-01

    This paper reports the occurrence of several chimera patterns and the associated transitions among them in a network of coupled oscillators, which are connected by a long-range interaction that obeys a distance-dependent power law. This type of interaction is common in physics and biology and constitutes a general form of coupling scheme, where by tuning the power-law exponent of the long-range interaction the coupling topology can be varied from local via nonlocal to global coupling. To explore the effect of the power-law coupling on collective dynamics, we consider a network consisting of a realistic ecological model of oscillating populations, namely the Rosenzweig-MacArthur model, and show that the variation of the power-law exponent mediates transitions between spatial synchrony and various chimera patterns. We map the possible spatiotemporal states and their scenarios that arise due to the interplay between the coupling strength and the power-law exponent.

  15. Chimera patterns induced by distance-dependent power-law coupling in ecological networks

    NASA Astrophysics Data System (ADS)

    Banerjee, Tanmoy; Dutta, Partha Sharathi; Zakharova, Anna; Schöll, Eckehard

    2016-09-01

    This paper reports the occurrence of several chimera patterns and the associated transitions among them in a network of coupled oscillators, which are connected by a long-range interaction that obeys a distance-dependent power law. This type of interaction is common in physics and biology and constitutes a general form of coupling scheme, where by tuning the power-law exponent of the long-range interaction the coupling topology can be varied from local via nonlocal to global coupling. To explore the effect of the power-law coupling on collective dynamics, we consider a network consisting of a realistic ecological model of oscillating populations, namely the Rosenzweig-MacArthur model, and show that the variation of the power-law exponent mediates transitions between spatial synchrony and various chimera patterns. We map the possible spatiotemporal states and their scenarios that arise due to the interplay between the coupling strength and the power-law exponent.

  16. G-Protein-Coupled Receptors in Adult Neurogenesis

    PubMed Central

    Doze, Van A.

    2012-01-01

    The importance of adult neurogenesis has only recently been accepted, resulting in a completely new field of investigation within stem cell biology. The regulation and functional significance of adult neurogenesis is currently an area of highly active research. G-protein-coupled receptors (GPCRs) have emerged as potential modulators of adult neurogenesis. GPCRs represent a class of proteins with significant clinical importance, because approximately 30% of all modern therapeutic treatments target these receptors. GPCRs bind to a large class of neurotransmitters and neuromodulators such as norepinephrine, dopamine, and serotonin. Besides their typical role in cellular communication, GPCRs are expressed on adult neural stem cells and their progenitors that relay specific signals to regulate the neurogenic process. This review summarizes the field of adult neurogenesis and its methods and specifies the roles of various GPCRs and their signal transduction pathways that are involved in the regulation of adult neural stem cells and their progenitors. Current evidence supporting adult neurogenesis as a model for self-repair in neuropathologic conditions, adult neural stem cell therapeutic strategies, and potential avenues for GPCR-based therapeutics are also discussed. PMID:22611178

  17. Modeling G Protein-Coupled Receptors: a Concrete Possibility.

    PubMed

    Costanzi, Stefano

    2010-01-01

    G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that are involved in the regulation of a wide range of physiological functions and constitute the most common target for therapeutic intervention. Due to the paucity of crystal structures, homology modeling has become a widespread technique for the construction of GPCR models, which have been applied to the study of their structure-function relationships and to the identification of lead ligands through virtual screening. Rhodopsin has been for years the only available template. However, recent breakthroughs in GPCR crystallography have led to the solution of the structures of a few additional receptors. In light of these newly elucidated crystal structures, we have been able to produce a substantial amount of data to demonstrate that accurate models of GPCRs in complex with their ligands can be constructed through homology modeling followed by fully flexible molecular docking. These results have been confirmed by our success in the first blind assessment of GPCR modeling and docking, organized in coordination with the solution of the X-ray structure of the adenosine A(2A) receptor. Taken together, these data indicate that: a) the transmembrane helical bundle can be modeled with considerable accuracy; b) predicting the binding mode of a ligand, although doable, is challenging; c) modeling of the extracellular and intracellular loops is still problematic.

  18. Homology Modeling of Class A G Protein-Coupled Receptors

    PubMed Central

    Costanzi, Stefano

    2012-01-01

    G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that hold great pharmaceutical interest. Since experimentally elucidated structures are available only for a very limited number of receptors, homology modeling has become a widespread technique for the construction of GPCR models intended to study the structure-function relationships of the receptors and aid the discovery and development of ligands capable of modulating their activity. Through this chapter, various aspects involved in the constructions of homology models of the serpentine domain of the largest class of GPCRs, known as class A or rhodopsin family, are illustrated. In particular, the chapter provides suggestions, guidelines and critical thoughts on some of the most crucial aspect of GPCR modeling, including: collection of candidate templates and a structure-based alignment of their sequences; identification and alignment of the transmembrane helices of the query receptor to the corresponding domains of the candidate templates; selection of one or more templates receptor; election of homology or de novo modeling for the construction of specific extracellular and intracellular domains; construction of the three-dimensional models, with special consideration to extracellular regions, disulfide bridges, and interhelical cavity; validation of the models through controlled virtual screening experiments. PMID:22323225

  19. Multifactorial Regulation of G Protein-Coupled Receptor Endocytosis

    PubMed Central

    Zhang, Xiaohan; Kim, Kyeong-Man

    2017-01-01

    Endocytosis is a process by which cells absorb extracellular materials via the inward budding of vesicles formed from the plasma membrane. Receptor-mediated endocytosis is a highly selective process where receptors with specific binding sites for extracellular molecules internalize via vesicles. G protein-coupled receptors (GPCRs) are the largest single family of plasma-membrane receptors with more than 1000 family members. But the molecular mechanisms involved in the regulation of GPCRs are believed to be highly conserved. For example, receptor phosphorylation in collaboration with β-arrestins plays major roles in desensitization and endocytosis of most GPCRs. Nevertheless, a number of subsequent studies showed that GPCR regulation, such as that by endocytosis, occurs through various pathways with a multitude of cellular components and processes. This review focused on i) functional interactions between homologous and heterologous pathways, ii) methodologies applied for determining receptor endocytosis, iii) experimental tools to determine specific endocytic routes, iv) roles of small guanosine triphosphate-binding proteins in GPCR endocytosis, and v) role of post-translational modification of the receptors in endocytosis. PMID:28035080

  20. Scalable rule-based modelling of allosteric proteins and biochemical networks.

    PubMed

    Ollivier, Julien F; Shahrezaei, Vahid; Swain, Peter S

    2010-11-04

    Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.

  1. Mutual synchronization and clustering in randomly coupled chaotic dynamical networks.

    PubMed

    Manrubia, S C; Mikhailov, A S

    1999-08-01

    We introduce and study systems of randomly coupled maps where the relevant parameter is the degree of connectivity in the system. Global (almost-) synchronized states are found (equivalent to the synchronization observed in globally coupled maps) until a certain critical threshold for the connectivity is reached. We further show that not only the average connectivity, but also the architecture of the couplings is responsible for the cluster structure observed. We analyze the different phases of the system and use various correlation measures in order to detect ordered nonsynchronized states. Finally, it is shown that the system displays a dynamical hierarchical clustering which allows the definition of emerging graphs.

  2. Disease Gene Prioritization Based on Topological Similarity in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Erten, Sinan; Bebek, Gurkan; Koyutürk, Mehmet

    In recent years, many algorithms have been developed to narrow down the set of candidate disease genes implicated by genome wide association studies (GWAS), using knowledge on protein-protein interactions (PPIs). All of these algorithms are based on a common principle; functional association between proteins is correlated with their connectivity/proximity in the PPI network. However, recent research also reveals that networks are organized into recurrent network schemes that underlie the mechanisms of cooperation among proteins with different function, as well as the crosstalk between different cellular processes. In this paper, we hypothesize that proteins that are associated with similar diseases may exhibit patterns of "topological similarity" in PPI networks. Motivated by these observations, we introduce the notion of "topological profile", which represents the location of a protein in the network with respect to other proteins. Based on this notion, we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to develop algorithms that prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. Systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance (OMIM) database show that the proposed algorithm, Vavien, clearly outperforms state-of-the-art network based prioritization algorithms. Vavien is available as a web service at http://www.diseasegenes.org .

  3. Analyzing inner and outer synchronization between two coupled discrete-time networks with time delays

    PubMed Central

    Wang, Rubin; Wang, Weixiang; Cao, Jianting

    2010-01-01

    This paper studies two kinds of synchronization between two discrete-time networks with time delays, including inner synchronization within each network and outer synchronization between two networks. Based on Lyapunov stability theory and linear matrix inequality (LMI), sufficient conditions for two discrete-time networks to be asymptotic stability are derived in terms of LMI. Finally numerical examples are given to illustrate the effectiveness of our derived results. The theoretical understanding provides insights into the dynamics of two or more neural networks with appropriate couplings. PMID:21886675

  4. Coexistence of Regular and Irregular Dynamics in Complex Networks of Pulse-Coupled Oscillators

    NASA Astrophysics Data System (ADS)

    Timme, Marc; Wolf, Fred; Geisel, Theo

    2002-11-01

    For general networks of pulse-coupled oscillators, including regular, random, and more complex networks, we develop an exact stability analysis of synchronous states. As opposed to conventional stability analysis, here stability is determined by a multitude of linear operators. We treat this multioperator problem exactly and show that for inhibitory interactions the synchronous state is stable, independent of the parameters and the network connectivity. In randomly connected networks with strong interactions this synchronous state, displaying regular dynamics, coexists with a balanced state exhibiting irregular dynamics. External signals may switch the network between qualitatively distinct states.

  5. 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

  6. 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

    2016-05-19

    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.

  7. Mechanisms of disease: Mutations of G proteins and G-protein-coupled receptors in endocrine diseases.

    PubMed

    Lania, Andrea G; Mantovani, Giovanna; Spada, Anna

    2006-12-01

    G proteins and G-protein-coupled receptors (GPCRs) mediate the effects of a number of hormones. Genes that encode these molecules are subject to loss-of function or gain-of-function mutations that result in endocrine disorders. Loss-of-function mutations prevent signaling in response to the corresponding agonist and cause resistance to hormone actions, which mimics hormone deficiency. Gain-of-function mutations lead to constitutive, agonist-independent activation of signaling, which mimics hormone excess. Disease-causing mutations of GPCRs have been identified in patients with various disorders of the pituitary-thyroid, pituitary-gonadal and pituitary-adrenal axes, and in those with abnormalities in food intake, growth, water balance and mineral-ion turnover. The only mutational changes in G proteins unequivocally associated with endocrine disorders occur in GNAS (guanine nucleotide-binding protein G-stimulatory subunit alpha, or G(s)alpha). Heterozygous loss-of-function mutations of GNAS in the active, maternal allele cause resistance to hormones that act through G(s)alpha-coupled GPCRs, whereas somatic gain-of-function mutations cause proliferation of endocrine cells that recognize cyclic AMP as a mitogen. The study of mutations in G proteins and GPCRs has already had major implications for understanding the molecular basis of rare endocrine diseases, as well as susceptibility to multifactorial disorders that are associated with polymorphisms in these genes.

  8. Stability of the synchronous state of an arbitrary network of coupled elements

    NASA Astrophysics Data System (ADS)

    Boccaletti, S.; Koronovsky, A. A.; Trubetskov, D. I.; Khramov, A. E.; Khramova, A. E.

    2006-10-01

    We propose a method for determining the range of the coupling parameter for which the network of slightly nonidentical chaotic oscillators demonstrates stable synchronous behavior. As an example of using this method, we study the complete-synchronization regime of a network of nonidentical Rossler oscillators.

  9. Social Networks and Structural Holes: Parent-School Relationships as Loosely Coupled Systems

    ERIC Educational Resources Information Center

    Wanat, Carolyn Louise; Zieglowsky, Laura Thudium

    2010-01-01

    This article describes parent groups as social networks that are loosely coupled to schools. The study investigated parent groups that work together to support schools by networking, responding to change, seeking input on policy decisions, and communicating with school leaders. Parents from one elementary school who participated in two focus group…

  10. Synchronization analysis of delayed complex networks with time-varying couplings

    NASA Astrophysics Data System (ADS)

    Li, Ping; Yi, Zhang

    2008-06-01

    In this paper, a new method is presented to analyze the linear stability of the synchronized state in arbitrarily coupled complex dynamical systems with time delays. The coupling configurations are not restricted to the symmetric and irreducible connections or the non-negative off-diagonal links. The stability criteria are obtained by using Lyapunov-Krasovskii functional method and subspace projection method. These criteria reveal the relationship between coupling matrices and stability of the dynamical networks.

  11. The Drosophila Clock Neuron Network Features Diverse Coupling Modes and Requires Network-wide Coherence for Robust Circadian Rhythms.

    PubMed

    Yao, Zepeng; Bennett, Amelia J; Clem, Jenna L; Shafer, Orie T

    2016-12-13

    In animals, networks of clock neurons containing molecular clocks orchestrate daily rhythms in physiology and behavior. However, how various types of clock neurons communicate and coordinate with one another to produce coherent circadian rhythms is not well understood. Here, we investigate clock neuron coupling in the brain of Drosophila and demonstrate that the fly's various groups of clock neurons display unique and complex coupling relationships to core pacemaker neurons. Furthermore, we find that coordinated free-running rhythms require molecular clock synchrony not only within the well-characterized lateral clock neuron classes but also between lateral clock neurons and dorsal clock neurons. These results uncover unexpected patterns of coupling in the clock neuron network and reveal that robust free-running behavioral rhythms require a coherence of molecular oscillations across most of the fly's clock neuron network.

  12. Variety of alternative stable phase-locking in networks of electrically coupled relaxation oscillators.

    PubMed

    Meyrand, Pierre; Bem, Tiaza

    2014-01-01

    We studied the dynamics of a large-scale model network comprised of oscillating electrically coupled neurons. Cells are modeled as relaxation oscillators with short duty cycle, so they can be considered either as models of pacemaker cells, spiking cells with fast regenerative and slow recovery variables or firing rate models of excitatory cells with synaptic depression or cellular adaptation. It was already shown that electrically coupled relaxation oscillators exhibit not only synchrony but also anti-phase behavior if electrical coupling is weak. We show that a much wider spectrum of spatiotemporal patterns of activity can emerge in a network of electrically coupled cells as a result of switching from synchrony, produced by short external signals of different spatial profiles. The variety of patterns increases with decreasing rate of neuronal firing (or duty cycle) and with decreasing strength of electrical coupling. We study also the effect of network topology--from all-to-all--to pure ring connectivity, where only the closest neighbors are coupled. We show that the ring topology promotes anti-phase behavior as compared to all-to-all coupling. It also gives rise to a hierarchical organization of activity: during each of the main phases of a given pattern cells fire in a particular sequence determined by the local connectivity. We have analyzed the behavior of the network using geometric phase plane methods and we give heuristic explanations of our findings. Our results show that complex spatiotemporal activity patterns can emerge due to the action of stochastic or sensory stimuli in neural networks without chemical synapses, where each cell is equally coupled to others via gap junctions. This suggests that in developing nervous systems where only electrical coupling is present such a mechanism can lead to the establishment of proto-networks generating premature multiphase oscillations whereas the subsequent emergence of chemical synapses would later stabilize

  13. A self-consistent structural perturbation approach for determining the magnitude and extent of allosteric coupling in proteins.

    PubMed

    Rajasekaran, Nandakumar; Naganathan, Athi N

    2017-07-06

    Elucidating the extent of energetic coupling between residues in single-domain proteins, which is a fundamental determinant of allostery, information transfer and folding cooperativity, has remained a grand challenge. While several sequence- and structure-based approaches have been proposed, a self-consistent description that is simultaneously compatible with unfolding thermodynamics is lacking. We recently developed a simple structural perturbation protocol that captures the changes in thermodynamic stabilities induced by point mutations within the protein interior. Here, we show that a fundamental residue-specific component of this perturbation approach, the coupling distance, is uniquely sensitive to the environment of a residue in the protein to a distance of ∼15 Å. With just the protein contact map as an input, we reproduce the extent of percolation of perturbations within the structure as observed in network analysis of intra-protein interactions, molecular dynamics simulations and NMR-observed changes in chemical shifts. Using this rapid protocol that relies on a single structure, we explain the results of statistical coupling analysis (SCA) that requires hundreds of sequences to identify functionally critical sectors, the propagation and dissipation of perturbations within proteins and the higher-order couplings deduced from detailed NMR experiments. Our results thus shed light on the possible mechanistic origins of signaling through the interaction network within proteins, the likely distance dependence of perturbations induced by ligands and post-translational modifications and the origins of folding cooperativity through many-body interactions. © 2017 The Author(s); published by Portland Press Limited on behalf of the Biochemical Society.

  14. Introduction of inflammatory bowel disease biomarkers panel using protein-protein interaction (PPI) network analysis.

    PubMed

    Asadzadeh-Aghdaee, Hamid; Shahrokh, Shabnam; Norouzinia, Mohsen; Hosseini, Mostafa; Keramatinia, Aliasghar; Jamalan, Mostafa; Naghibzadeh, Bijan; Sadeghi, Ali; Jahani Sherafat, Somayeh; Zali, Mohammad Reza

    2016-12-01

    In the present study, a protein-protein interaction network construction is conducted for IBD. Inflammatory bowel diseases as serious chronic gastrointestinal disorders attracted many molecular investigations. Diverse molecular information is present for IBD. However, these molecular findings are not highlighted based on interactome analysis. On the other hand, PPI network analysis is a powerful method for study of molecular interactions in the protein level that provide useful information for highlighting the desired key proteins. Cytoscape is the used software with its plug-ins for detailed analysis. Two centrality parameters including degree and betweenness are determined and the crucial proteins based on these parameters are introduced. The 75 proteins among 100 initial proteins are included in the network of IBD. Seventy-five nodes and 260 edges constructed the network as a scale free network. The findings indicate that there are seven hub-bottleneck proteins in the IBD network. More examination revealed the essential roles of these key proteins in the integrity of the network. Finally, the indicator panel including NFKB1, CD40, TNFA, TYK2, NOD2, IL23R, and STAT3 is presented as a possible molecular index for IBD.

  15. Introduction of inflammatory bowel disease biomarkers panel using protein-protein interaction (PPI) network analysis

    PubMed Central

    Asadzadeh-Aghdaee, Hamid; Shahrokh, Shabnam; Norouzinia, Mohsen; Hosseini, Mostafa; Keramatinia, Aliasghar; Jamalan, Mostafa; Naghibzadeh, Bijan; Sadeghi, Ali; Jahani Sherafat, Somayeh; Zali, Mohammad Reza

    2016-01-01

    Aim: In the present study, a protein-protein interaction network construction is conducted for IBD. Background: Inflammatory bowel diseases as serious chronic gastrointestinal disorders attracted many molecular investigations. Diverse molecular information is present for IBD. However, these molecular findings are not highlighted based on interactome analysis. On the other hand, PPI network analysis is a powerful method for study of molecular interactions in the protein level that provide useful information for highlighting the desired key proteins. Methods: Cytoscape is the used software with its plug-ins for detailed analysis. Two centrality parameters including degree and betweenness are determined and the crucial proteins based on these parameters are introduced. Results: The 75 proteins among 100 initial proteins are included in the network of IBD. Seventy-five nodes and 260 edges constructed the network as a scale free network. The findings indicate that there are seven hub-bottleneck proteins in the IBD network. Conclusion: More examination revealed the essential roles of these key proteins in the integrity of the network. Finally, the indicator panel including NFKB1, CD40, TNFA, TYK2, NOD2, IL23R, and STAT3 is presented as a possible molecular index for IBD. PMID:28224022

  16. Pin-Align: a new dynamic programming approach to align protein-protein interaction networks.

    PubMed

    Amir-Ghiasvand, Farid; Nowzari-Dalini, Abbas; Momenzadeh, Vida

    2014-01-01

    To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein interaction networks from IntAct, DIP, and the Stanford Network Database and the results are compared with other well-known algorithms. It is shown that Pin-Align has higher sensitivity and specificity in terms of KEGG Ortholog groups.

  17. Multimode dynamics in a network with resource mediated coupling

    NASA Astrophysics Data System (ADS)

    Postnov, D. E.; Sosnovtseva, O. V.; Scherbakov, P.; Mosekilde, E.

    2008-03-01

    The purpose of this paper is to study the special forms of multimode dynamics that one can observe in systems with resource-mediated coupling, i.e., systems of self-sustained oscillators in which the coupling takes place via the distribution of primary resources that controls the oscillatory state of the individual unit. With this coupling, a spatially inhomogenous state with mixed high and low-amplitude oscillations in the individual units can arise. To examine generic phenomena associated with this type of interaction we consider a chain of resistively coupled electronic oscillators connected to a common power supply. The two-oscillator system displays antiphase synchronization, and it is interesting to note that two-mode oscillations continue to exist outside of the parameter range in which oscillations occur for the individual unit. At low coupling strengths, the multi-oscillator system shows high dimensional quasiperiodicity with little tendency for synchronization. At higher coupling strengths, one typically observes spatial clustering involving a few oscillating units. We describe three different scenarios according to which the cluster can slide along the chain as the bias voltage changes.

  18. Does hyperbolicity impede emergence of chimera states in networks of nonlocally coupled chaotic oscillators?

    NASA Astrophysics Data System (ADS)

    Semenova, N.; Zakharova, A.; Schöll, E.; Anishchenko, V.

    2015-11-01

    We analyze nonlocally coupled networks of identical chaotic oscillators with either time-discrete or time-continuous dynamics (Henon map, Lozi map, Lorenz system). We hypothesize that chimera states, in which spatial domains of coherent (synchronous) and incoherent (desynchronized) dynamics coexist, can be obtained only in networks of oscillators with nonhyperbolic chaotic attractors and cannot be found in networks of systems with hyperbolic chaotic attractors. This hypothesis is supported by analytical results and numerical simulations for hyperbolic and nonhyperbolic cases.

  19. Topology identification of the complex networks with non-delayed and delayed coupling

    NASA Astrophysics Data System (ADS)

    Guo, Wanli; Chen, Shihua; Sun, Wen

    2009-10-01

    In practical situation, there exists many uncertain information in complex networks, such as the topological structures. So the topology identification is an important issue in the research of the complex networks. Based on LaSalle's invariance principle, in this Letter, an adaptive controlling method is proposed to identify the topology of a weighted general complex network model with non-delayed and delayed coupling. Finally, simulation results show that the method is effective.

  20. The complex G protein-coupled receptor kinase 2 (GRK2) interactome unveils new physiopathological targets

    PubMed Central

    Penela, Petronila; Murga, Cristina; Ribas, Catalina; Lafarga, Vanesa; Mayor, Federico

    2010-01-01

    GRK2 is a ubiquitous member of the G protein-coupled receptor kinase (GRK) family that appears to play a central, integrative role in signal transduction cascades. GRKs participate together with arrestins in the regulation of G protein-coupled receptors (GPCR), a family of hundreds of membrane proteins of key physiological and pharmacological importance, by triggering receptor desensitization from G proteins and GPCR internalization, and also by helping assemble macromolecular signalosomes in the receptor environment acting as agonist-regulated adaptor scaffolds, thus contributing to signal propagation. In addition, emerging evidence indicates that GRK2 can phosphorylate a growing number of non-GPCR substrates and associate with a variety of proteins related to signal transduction, thus suggesting that this kinase could also have diverse ‘effector’ functions. We discuss herein the increasing complexity of such GRK2 ‘interactome’, with emphasis on the recently reported roles of this kinase in cell migration and cell cycle progression and on the functional impact of the altered GRK2 levels observed in several relevant cardiovascular, inflammatory or tumour pathologies. Deciphering how the different networks of potential GRK2 functional interactions are orchestrated in a stimulus, cell type or context-specific way is critical to unveil the contribution of GRK2 to basic cellular processes, to understand how alterations in GRK2 levels or functionality may participate in the onset or development of several cardiovascular, tumour or inflammatory diseases, and to assess the feasibility of new therapeutic strategies based on the modulation of the activity, levels or specific interactions of GRK2. PMID:20590581

  1. 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.

  2. Protein complexes and functional modules in molecular networks.

    PubMed

    Spirin, Victor; Mirny, Leonid A

    2003-10-14

    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.

  3. Synchronization of fluctuating delay-coupled chaotic networks

    NASA Astrophysics Data System (ADS)

    Jiménez-Martín, Manuel; Rodríguez-Laguna, Javier; D'Huys, Otti; de la Rubia, Javier; Korutcheva, Elka

    2017-05-01

    We study the synchronization of chaotic units connected through time-delayed fluctuating interactions. Focusing on small-world networks of Bernoulli and Logistic units with a fixed chiral backbone, we compare the synchronization properties of static and fluctuating networks in the regime of large delays. We find that random network switching may enhance the stability of synchronized states. Synchronization appears to be maximally stable when fluctuations are much faster than the time-delay, whereas it disappears for very slow fluctuations. For fluctuation time scales of the order of the time-delay, we report a resynchronizing effect in finite-size networks. Moreover, we observe characteristic oscillations in all regimes, with a periodicity related to the time-delay, as the system approaches or drifts away from the synchronized state.

  4. Identification of Trans-Golgi Network Proteins in Arabidopsis thaliana Root Tissue

    PubMed Central

    2013-01-01

    Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins

  5. Identifying folding nucleus based on residue contact networks of proteins.

    PubMed

    Li, Jie; Wang, Jun; Wang, Wei

    2008-06-01

    In the native structure of a protein, all the residues are tightly parked together in a specific order following its folding and every residue contacts with some spatially neighbor residues. A residue contact network can be constructed by defining the residues as nodes and the native contacts as edges. During the folding of small single-domain proteins, there is a set of contacts (or bonds), defined as the folding nucleus (FN), which is formed around the transition state, i.e., a rate-limiting barrier located at about the middle between the unfolded states and the native state on the free energy landscape. Such a FN plays an essential role in the folding dynamics and the residues, which form the related contacts called as folding nucleus residues (FNRs). In this work, the FNRs in proteins are identified by using quantities which characterize the topology of residue contact networks of proteins. By comparing the specificities of residues with the network quantities K(R), L(R), and D(R), up to 90% FNRs of six typical proteins found experimentally are identified. It is found that the FNRs behave the full-closeness centrals rather than degree or closeness centers in the residue contact network, implying that they are important to the folding cooperativity of proteins. Our study shows that the FNRs can be identified solely from the native structures of proteins based on the analysis of residue contact network without any knowledge of the transition state ensemble. (c) 2008 Wiley-Liss, Inc.

  6. Edge Event-Triggered Synchronization in Networks of Coupled Harmonic Oscillators.

    PubMed

    Wei, Bo; Xiao, Feng; Dai, Ming-Zhe

    2016-08-30

    The synchronization problems of networks of coupled harmonic oscillators are addressed by the edge event-triggered approach in this paper. The network dynamics with respect to edge states are presented and a new edge event-triggered control protocol is designed. Combined with the periodic event-detecting and edge event-triggered approach, sufficient conditions that guarantee the synchronization of coupled harmonic oscillators are presented. Two event-detecting rules are given to achieve the synchronization of coupled harmonic oscillators with low resource consumption. Finally, simulations are conducted to illustrate the effectiveness of the edge event-triggered control algorithm.

  7. Chaotic Griffiths Phase with Anomalous Lyapunov Spectra in Coupled Map Networks.

    PubMed

    Shinoda, Kenji; Kaneko, Kunihiko

    2016-12-16

    Dynamics of coupled chaotic oscillators on a network are studied using coupled maps. Within a broad range of parameter values representing the coupling strength or the degree of elements, the system repeats formation and split of coherent clusters. The distribution of the cluster size follows a power law with the exponent α, which changes with the parameter values. The number of positive Lyapunov exponents and their spectra are scaled anomalously with the power of the system size with the exponent β, which also changes with the parameters. The scaling relation α∼2(β+1) is uncovered, which is universal independent of parameters and among random networks.

  8. Chaotic Griffiths Phase with Anomalous Lyapunov Spectra in Coupled Map Networks

    NASA Astrophysics Data System (ADS)

    Shinoda, Kenji; Kaneko, Kunihiko

    2016-12-01

    Dynamics of coupled chaotic oscillators on a network are studied using coupled maps. Within a broad range of parameter values representing the coupling strength or the degree of elements, the system repeats formation and split of coherent clusters. The distribution of the cluster size follows a power law with the exponent α , which changes with the parameter values. The number of positive Lyapunov exponents and their spectra are scaled anomalously with the power of the system size with the exponent β , which also changes with the parameters. The scaling relation α ˜2 (β +1 ) is uncovered, which is universal independent of parameters and among random networks.

  9. Adaptive global synchronization of a general complex dynamical network with non-delayed and delayed coupling

    NASA Astrophysics Data System (ADS)

    Wen, Sun; Chen, Shihua; Guo, Wanli

    2008-10-01

    This Letter investigates the global synchronization of a general complex dynamical network with non-delayed and delayed coupling. Based on Lasalle's invariance principle, adaptive global synchronization criteria is obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-delayed and delayed coupling can globally asymptotically synchronize to a given trajectory. What is more, the node dynamic need not satisfy the very strong and conservative uniformly Lipschitz condition and the coupling matrix is not assumed to be symmetric or irreducible. Finally, numerical simulations are presented to verify the effectiveness of the proposed synchronization criteria.

  10. Adaptive synchronization of the complex dynamical network with non-derivative and derivative coupling

    NASA Astrophysics Data System (ADS)

    Xu, Yuhua; Zhou, Wuneng; Fang, Jian'an; Sun, Wen

    2010-04-01

    This Letter investigates the synchronization of a general complex dynamical network with non-derivative and derivative coupling. Based on LaSalle's invariance principle, adaptive synchronization criteria are obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-derivative and derivative coupling can asymptotically synchronize to a given trajectory, and several useful criteria for synchronization are given. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, simulations results show the method is effective.

  11. Pinning-controlled synchronization of delayed neural networks with distributed-delay coupling via impulsive control.

    PubMed

    He, Wangli; Qian, Feng; Cao, Jinde

    2017-01-01

    This paper investigates pinning synchronization of coupled neural networks with both current-state coupling and distributed-delay coupling via impulsive control. A novel impulse pinning strategy involving pinning ratio is proposed and a general criterion is derived to ensure an array of neural networks with two different topologies synchronizes with the desired trajectory. In order to handle the difficulties of high-dimension criteria, some inequality techniques and matrix decomposition methods through simultaneous diagonalization of two matrices are introduced and low-dimensional criteria are obtained. Finally, an illustrative example is given to show the effectiveness of the proposed method. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching.

    PubMed

    Wu, Yuanyuan; Cao, Jinde; Li, Qingbo; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-01-01

    This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Synchronization, stickiness effects and intermittent oscillations in coupled nonlinear stochastic networks

    NASA Astrophysics Data System (ADS)

    Kouvaris, N.; Provata, A.

    2009-08-01

    Long distance reactive and diffusive coupling is introduced in a spatially extended nonlinear stochastic network of interacting particles. The network serves as a substrate for Lotka-Volterra dynamics with 3rd order nonlinearities. If the network includes only local nearest neighbour interactions, the system organizes into a number of local asynchronous oscillators. It is shown that (a) Introduction of all-to-all coupling in the network leads the system into global, center-type, conservative oscillations as dictated by the mean-field dynamics. (b) Introduction of reactive coupling to the network leads the system to intermittent oscillations where the trajectories stick for short times in bounded regions of space, with subsequent jumps between different bounded regions. (c) Introduction of diffusive coupling to the system does not alter the dynamics for small values of the diffusive coupling pdiff, while after a critical value pdiff c the system synchronizes into a limit cycle with specific frequency, deviating non-trivially from the mean-field center-type behaviour. The frequency of the limit cycle oscillations depends on the reaction rates and on the diffusion coupling. The amplitude σ of the limit cycle depends on the control parameter pdiff.

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

    NASA Astrophysics Data System (ADS)

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

    2004-03-01

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

  15. Characterization of Protein-Protein Interfaces through a Protein Contact Network Approach.

    PubMed

    Di Paola, Luisa; Platania, Chiara Bianca Maria; Oliva, Gabriele; Setola, Roberto; Pascucci, Federica; Giuliani, Alessandro

    2015-01-01

    Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein-protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node's ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein-protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.

  16. A novel framework of classical and quantum prisoner’s dilemma games on coupled networks

    NASA Astrophysics Data System (ADS)

    Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

    2016-03-01

    Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner’s dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner’s dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner’s dilemma is greatly impacted by the combined effect of entanglement and coupling.

  17. A novel framework of classical and quantum prisoner's dilemma games on coupled networks.

    PubMed

    Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

    2016-03-15

    Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner's dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner's dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner's dilemma is greatly impacted by the combined effect of entanglement and coupling.

  18. Transport of organelles by elastically coupled motor proteins.

    PubMed

    Bhat, Deepak; Gopalakrishnan, Manoj

    2016-07-01

    Motor-driven intracellular transport is a complex phenomenon where multiple motor proteins simultaneously attached on to a cargo engage in pulling activity, often leading to tug-of-war, displaying bidirectional motion. However, most mathematical and computational models ignore the details of the motor-cargo interaction. A few studies have focused on more realistic models of cargo transport by including elastic motor-cargo coupling, but either restrict the number of motors and/or use purely phenomenological forms for force-dependent hopping rates. Here, we study a generic model in which N motors are elastically coupled to a cargo, which itself is subjected to thermal noise in the cytoplasm and to an additional external applied force. The motor-hopping rates are chosen to satisfy detailed balance with respect to the energy of elastic stretching. With these assumptions, an (N + 1) -variable master equation is constructed for dynamics of the motor-cargo complex. By expanding the hopping rates to linear order in fluctuations in motor positions, we obtain a linear Fokker-Planck equation. The deterministic equations governing the average quantities are separated out and explicit analytical expressions are obtained for the mean velocity and diffusion coefficient of the cargo. We also study the statistical features of the force experienced by an individual motor and quantitatively characterize the load-sharing among the cargo-bound motors. The mean cargo velocity and the effective diffusion coefficient are found to be decreasing functions of the stiffness. While the increase in the number of motors N does not increase the velocity substantially, it decreases the effective diffusion coefficient which falls as 1/N asymptotically. We further show that the cargo-bound motors share the force exerted on the cargo equally only in the limit of vanishing elastic stiffness; as stiffness is increased, deviations from equal load sharing are observed. Numerical simulations agree with

  19. Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks.

    PubMed

    Cao, Renzhi; Cheng, Jianlin

    2016-01-15

    Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein-protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene-gene interaction networks generated from chromosomal conformation data together to improve protein function prediction. In this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein-protein interaction and spatial gene-gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein-protein interaction and spatial gene-gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile-sequence comparison, profile-profile comparison, and domain co-occurrence networks according to the maximum F-measure. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Exploring Function Prediction in Protein Interaction Networks via Clustering Methods

    PubMed Central

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

    Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach. PMID:24972109

  1. A Social Network Comparison of Low-Income Black and White Newlywed Couples

    PubMed Central

    Jackson, Grace L.; Kennedy, David; Bradbury, Thomas N.; Karney, Benjamin R.

    2014-01-01

    Relative to White families, Black families have been described as relying on extended social networks to compensate for other social and economic disadvantages. The presence or absence of supportive social networks should be especially relevant to young couples entering marriage, but to date there has been little effort to describe the social networks of comparable Black and White newlyweds. The current study addressed this gap by drawing on interviews with 57 first-married newlyweds from low-income communities to compare the composition and structure of Black and White couples’ duocentric social networks. The results indicated that low-income Black couples entered marriage at a social disadvantage relative to White couples, with more family relationships but fewer positive relationships and fewer sources of emotional support (for wives), fewer connections to married individuals, and fewer shared relationships between spouses. Black couples’ relative social disadvantages persisted even when various economic and demographic variables were controlled. PMID:25214673

  2. Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

    SciTech Connect

    Zhang, Jianbao; Ma, Zhongjun; Zhang, Gang

    2013-12-15

    This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network.

  3. The role of coupling-frequency weighting exponent on synchronization of a power network

    NASA Astrophysics Data System (ADS)

    Yang, Li-xin; Jiang, Jun

    2016-12-01

    Second-order Kuramoto-like oscillators with dissimilar natural frequencies are used as a coarse-scale model for an electrical power network that contains generators and consumers. This paper proposes a new power network model with coupling-frequency weighting exponent. Furthermore, the influence of the weighting exponent on synchronization of a power network is investigated through numerical simulations. It is observed that the synchronizability is significantly influenced by the coupling-frequency weighting coefficient with different magnitude categories. Furthermore, the synchronization cost caused by phase differences of power plants on the synchronization of the proposed power network model is studied. Numerical simulation shows that the synchronization cost will get larger with the coupling-frequency weighting exponent increasing further.

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

    PubMed

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

    2015-07-28

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

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

    PubMed Central

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

    2015-01-01

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

  6. An analysis pipeline for the inferenceof protein-protein interaction networks

    SciTech Connect

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason; Cannon, Bill; Domico, Kelly; White, Amanda M.; Auberry, Deanna L; Auberry, Kenneth J; Hooker, Brian; Hurst, Gregory {Greg} B; McDermott, Jason; McDonald, W Hayes; Pelletier, Dale A; Schmoyer, Denise D; Wiley, Steven

    2009-09-01

    We present an integrated platform that is used for the reconstruction and analysis of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey experiment data. At the heart of this pipeline is the Software Environment for Biological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. For integration, co