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Sample records for protein network coupled

  1. 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

  2. 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.

  3. 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

  4. 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

  5. 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.

  6. 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/.

  7. 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.

  8. 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

  9. 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.

  10. 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…

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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)

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. Design of coupling resistor networks for neural network hardware

    NASA Astrophysics Data System (ADS)

    Barkan, Ozdal; Smith, W. R.; Persky, George

    1990-06-01

    The specification of an artificial neural network includes (1) the transformation relating each neuron's output voltage to its input voltage, and (2) a set of coupling weight factors expressing the input voltage of any neuron as a linear combination of the output voltages of other neurons. In analog VLSI chips for direct hardware implementation of these networks, neurons are often represented by amplifier elements (e.g. operational amplifiers or opamps), and resistors or active transconductances are used to couple signals from the outputs of certain neurons to the inputs of other neurons. Each coupling conductance is proportional to a single, corresponding coupling weight only under the following 'ideal' conditions: (1) each opamp has negligible output impedance, and (2) the input voltage of each opamp is developed across a low-resistance sampling resistor that is not loaded by the opamp itself. By contrast, the output impedance of a practical opamp may not be negligible in comparison to that of the high-fan network that it drives, and the sampling resistances on the opamp inputs cannot be arbitrarily low lest the input voltages be corrupted by unavoidable opamp input voltage offsets.

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  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. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

    PubMed

    Middendorf, Manuel; Ziv, Etay; Wiggins, Chris H

    2005-03-01

    Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and a duplication-mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. Robustness and fragility in coupled oscillator networks under targeted attacks

    NASA Astrophysics Data System (ADS)

    Yuan, Tianyu; Aihara, Kazuyuki; Tanaka, Gouhei

    2017-01-01

    The dynamical tolerance of coupled oscillator networks against local failures is studied. As the fraction of failed oscillator nodes gradually increases, the mean oscillation amplitude in the entire network decreases and then suddenly vanishes at a critical fraction as a phase transition. This critical fraction, widely used as a measure of the network robustness, was analytically derived for random failures but not for targeted attacks so far. Here we derive the general formula for the critical fraction, which can be applied to both random failures and targeted attacks. We consider the effects of targeting oscillator nodes based on their degrees. First we deal with coupled identical oscillators with homogeneous edge weights. Then our theory is applied to networks with heterogeneous edge weights and to those with nonidentical oscillators. The analytical results are validated by numerical experiments. Our results reveal the key factors governing the robustness and fragility of oscillator networks.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  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. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-20

    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.

  7. 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

  8. 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.

  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. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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).

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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

    PubMed Central

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

    2016-01-01

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

  8. Graphical models of residue coupling in protein families.

    PubMed

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

    2008-01-01

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

  9. Network measures for protein folding state discrimination

    PubMed Central

    Menichetti, Giulia; Fariselli, Piero; Remondini, Daniel

    2016-01-01

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

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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).

  1. 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

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

    PubMed

    Li, Fan; Ma, Jun

    2016-01-01

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

  3. 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

  4. 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.

  5. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  7. 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. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Experimental multistable states for small network of coupled pendula

    PubMed Central

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

    2016-01-01

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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  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

    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

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  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. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

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

    PubMed Central

    2015-01-01

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

  10. 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.

  11. 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.

  12. 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

  13. [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.

  14. 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.

  15. 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.

  16. Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics

    PubMed Central

    Sardiu, Mihaela E.; Cai, Yong; Jin, Jingji; Swanson, Selene K.; Conaway, Ronald C.; Conaway, Joan W.; Florens, Laurence; Washburn, Michael P.

    2008-01-01

    Large-scale affinity purification and mass spectrometry studies have played important roles in the assembly and analysis of comprehensive protein interaction networks for lower eukaryotes. However, the development of such networks for human proteins has been slowed by the high cost and significant technical challenges associated with systematic studies of protein interactions. To address this challenge, we have developed a method for building local and focused networks. This approach couples vector algebra and statistical methods with normalized spectral counting (NSAF) derived from the analysis of affinity purifications via chromatography-based proteomics. After mathematical removal of contaminant proteins, the core components of multiprotein complexes are determined by singular value decomposition analysis and clustering. The probability of interactions within and between complexes is computed solely based upon NSAFs using Bayes' approach. To demonstrate the application of this method to small-scale datasets, we analyzed an expanded human TIP49a and TIP49b dataset. This dataset contained proteins affinity-purified with 27 different epitope-tagged components of the chromatin remodeling SRCAP, hINO80, and TRRAP/TIP60 complexes, and the nutrient sensing complex Uri/Prefoldin. Within a core network of 65 unique proteins, we captured all known components of these complexes and novel protein associations, especially in the Uri/Prefoldin complex. Finally, we constructed a probabilistic human interaction network composed of 557 protein pairs. PMID:18218781

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

    PubMed

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

    2014-01-01

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

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. 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

  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. 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.

  6. 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.

  7. 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.

  8. 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

  9. Serial Femtosecond Crystallography of G Protein-Coupled Receptors

    PubMed Central

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

    2014-01-01

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

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. "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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. Alpha-Bulges in G Protein-Coupled Receptors

    PubMed Central

    van der Kant, Rob; Vriend, Gert

    2014-01-01

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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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

  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. 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

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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)

  3. 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

  4. 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.

  5. 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.

  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. 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.

  8. 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.

  9. [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.

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

    SciTech Connect

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

    2012-03-01

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

  11. 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

  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

    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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  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. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  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. 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

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. The Emerging Mutational Landscape of G-proteins and G-protein Coupled Receptors in Cancer

    PubMed Central

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

    2014-01-01

    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 tumors carry activating mutations in GNAS (encoding Gαs), and that oncogenic activating mutants 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 tumors harbor mutations in GPCRs. Many human cancer-associated viruses also express constitutively active viral GPCRs. These studies indicate that G proteins, GPCRs and their linked signaling circuitry represent novel therapeutic targets for cancer prevention and treatment. PMID:23640210

  13. ATPase-coupled release control from polyion complex capsules encapsulating muscle proteins.

    PubMed

    Sugiura, Kousuke; Ohkawa, Kousaku; Hirai, Toshihiro; Fujii, Toshihiro

    2007-04-10

    In the present study, a muscle contractile protein complex, actomyosin, has been successfully encapsulated into gellan-chitosan polyion complex (PIC) capsules. The recovery of the myosin-ATPase activity is approximately 50% and the Mg2+-ATPase activity is stimulated by the presence of F-actin, which implies the formation of the actomyosin complex inside the capsule. Furthermore, encapsulation could protect the myosin, F-actin, and actomyosin inside from hydrolysis by proteases. Two small proteins, myoglobin and cytochrome c, have been used in the release tests. The release of myoglobin is not affected by the ionic strength of the external solution, while the release of cytochrome c increases with increasing ionic strength. The maximal releases are found in the external pH solution close to the isoelectric points of each protein. The Mg2+-ATP complex itself reduces the release percentages of the small proteins from the PIC capsule. The release amounts further decrease when coexisting with Mg2+-ATP and the encapsulated actomyosin, which indicates the release regulation by actomyosin. The present study suggests that the ATPase-coupled sliding motion of the myosin-F-actin filaments modifies the pore size of the polymer networks in the PIC capsule membranes.

  14. 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, comparison and analysis of the inferred protein-protein interactions with interaction evidence obtained from multiple public sources, the pipeline connects to the Collective Analysis of Biological Interaction Networks (CABIN) software. Incorporating BEPro3 into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of MS bait-prey experiments.

  15. 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.

  16. 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.

  17. 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…

  18. 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.

  19. 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

  20. Interaction of G protein coupled receptors and cholesterol.

    PubMed

    Gimpl, Gerald

    2016-09-01

    G protein coupled receptors (GPCRs) form the largest receptor superfamily in eukaryotic cells. Owing to their seven transmembrane helices, large parts of these proteins are embedded in the cholesterol-rich plasma membrane bilayer. Thus, GPCRs are always in proximity to cholesterol. Some of them are functionally dependent on the specific presence of cholesterol. Over the last years, enormous progress on receptor structures has been achieved. While lipophilic ligands other than cholesterol have been shown to bind either inside the helix bundle or at the receptor-lipid interface, the binding site of cholesterol was either a single transmembrane helix or a groove between two or more transmembrane helices. A clear preference for one of the two membrane leaflets has not been observed. Not surprisingly, many hydrophobic residues (primarily leucine and isoleucine) were found to be involved in cholesterol binding. In most cases, the rough β-face of cholesterol contacted the transmembrane helix bundle rather than the surrounding lipid matrix. The polar hydroxy group of cholesterol was localized near the water-membrane interface with potential hydrogen bonding to residues in receptor loop regions. Although a canonical motif, designated as CCM site, was detected as a specific cholesterol binding site in case of the β2AR, this site was not found to be occupied by cholesterol in other GPCRs possessing the same motif. Cholesterol-receptor interactions can increase the compactness of the receptor structure and are able to enhance the conformational stability towards active or inactive receptor states. Overall, all current data suggest a high plasticity of cholesterol interaction sites in GPCRs.

  1. G-protein coupled receptor expression patterns delineate medulloblastoma subgroups

    PubMed Central

    2013-01-01

    Background Medulloblastoma is the most common malignant brain tumor in children. Genetic profiling has identified four principle tumor subgroups; each subgroup is characterized by different initiating mutations, genetic and clinical profiles, and prognoses. The two most well-defined subgroups are caused by overactive signaling in the WNT and SHH mitogenic pathways; less is understood about Groups 3 and 4 medulloblastoma. Identification of tumor subgroup using molecular classification is set to become an important component of medulloblastoma diagnosis and staging, and will likely guide therapeutic options. However, thus far, few druggable targets have emerged. G-protein coupled receptors (GPCRs) possess characteristics that make them ideal targets for molecular imaging and therapeutics; drugs targeting GPCRs account for 30-40% of all current pharmaceuticals. While expression patterns of many proteins in human medulloblastoma subgroups have been discerned, the expression pattern of GPCRs in medulloblastoma has not been investigated. We hypothesized that analysis of GPCR expression would identify clear subsets of medulloblastoma and suggest distinct GPCRs that might serve as molecular targets for both imaging and therapy. Results Our study found that medulloblastoma tumors fall into distinct clusters based solely on GPCR expression patterns. Normal cerebellum clustered separately from the tumor samples. Further, two of the tumor clusters correspond with high fidelity to the WNT and SHH subgroups of medulloblastoma. Distinct over-expressed GPCRs emerge; for example, LGR5 and GPR64 are significantly and uniquely over-expressed in the WNT subgroup of tumors, while PTGER4 is over-expressed in the SHH subgroup. Uniquely under-expressed GPCRs were also observed. Our key findings were independently validated using a large international dataset. Conclusions Our results identify GPCRs with potential to act as imaging and therapeutic targets. Elucidating tumorigenic pathways

  2. 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.

  3. PROSNET: INTEGRATING HOMOLOGY WITH MOLECULAR NETWORKS FOR PROTEIN FUNCTION PREDICTION

    PubMed Central

    Wang, Sheng; Qu, Meng

    2016-01-01

    Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet. An integrated heterogeneous network is first built to include molecular networks of multiple species and link together homologous proteins across multiple species. Based on this integrated network, a dimensionality reduction algorithm is introduced to obtain compact low-dimensional vectors to encode proteins in the network. Finally, we develop machine learning classification algorithms that take the vectors as input and make predictions by transferring annotations both within each species and across different species. Extensive experiments on five major species demonstrate that our integration of homology with molecular networks substantially improves the predictive performance over existing approaches. PMID:27896959

  4. 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.

  5. Isolation of Drosophila genes encoding G protein-coupled receptor kinases.

    PubMed Central

    Cassill, J A; Whitney, M; Joazeiro, C A; Becker, A; Zuker, C S

    1991-01-01

    G protein-coupled receptors are regulated via phosphorylation by a variety of protein kinases. Recently, termination of the active state of two such receptors, the beta-adrenergic receptor and rhodopsin, has been shown to be mediated by agonist- or light-dependent phosphorylation of the receptor by members of a family of protein-serine/threonine kinases (here referred to as G protein-coupled receptor kinases). We now report the isolation of a family of genes encoding a set of Drosophila protein kinases that appear to code for G protein-coupled receptor kinases. These proteins share a high degree of sequence homology with the bovine beta-adrenergic receptor kinase. The presence of a conserved family of G protein-coupled receptor kinases in vertebrates and invertebrates points to the central role of these kinases in signal transduction cascades. Images PMID:1662381

  6. G-Protein-Coupled Receptor Kinase 2 as a Potential Modulator of the Hallmarks of Cancer.

    PubMed

    Nogués, Laura; Reglero, Clara; Rivas, Verónica; Neves, María; Penela, Petronila; Mayor, Federico

    2017-03-01

    Malignant features-such as sustained proliferation, refractoriness to growth suppressors, resistance to cell death or aberrant motility, and metastasis-can be triggered by a variety of mutations and signaling adaptations. Signaling nodes can act as cancer-associated factors by cooperating with oncogene-governed pathways or participating in compensatory transduction networks to strengthen tumor properties. G-protein-coupled receptor kinase 2 (GRK2) is arising as one of such nodes. Via its complex network of connections with other cellular proteins, GRK2 contributes to the modulation of basic cellular functions-such as cell proliferation, survival, or motility-and is involved in metabolic homeostasis, inflammation, or angiogenic processes. Moreover, altered GRK2 levels are starting to be reported in different tumoral contexts and shown to promote breast tumorigenesis or to trigger the tumoral angiogenic switch. The ability to modulate several of the hallmarks of cancer puts forward GRK2 as an oncomodifier, able to modulate carcinogenesis in a cell-type specific way.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    Chen, Chen; Shen, Hong; Zhang, Li-Guo; Liu, Jian; Cao, Xiao-Ge; Yao, An-Liang; Kang, Shao-San; Gao, Wei-Xing; Han, Hui; Cao, Feng-Hong; Li, Zhi-Guo

    2016-06-01

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

  9. G protein-coupled estrogen receptor protects from atherosclerosis.

    PubMed

    Meyer, Matthias R; Fredette, Natalie C; Howard, Tamara A; Hu, Chelin; Ramesh, Chinnasamy; Daniel, Christoph; Amann, Kerstin; Arterburn, Jeffrey B; Barton, Matthias; Prossnitz, Eric R

    2014-12-23

    Coronary atherosclerosis and myocardial infarction in postmenopausal women have been linked to inflammation and reduced nitric oxide (NO) formation. Natural estrogen exerts protective effects on both processes, yet also displays uterotrophic activity. Here, we used genetic and pharmacologic approaches to investigate the role of the G protein-coupled estrogen receptor (GPER) in atherosclerosis. In ovary-intact mice, deletion of gper increased atherosclerosis progression, total and LDL cholesterol levels and inflammation while reducing vascular NO bioactivity, effects that were in some cases aggravated by surgical menopause. In human endothelial cells, GPER was expressed on intracellular membranes and mediated eNOS activation and NO formation, partially accounting for estrogen-mediated effects. Chronic treatment with G-1, a synthetic, highly selective small molecule agonist of GPER, reduced postmenopausal atherosclerosis and inflammation without uterotrophic effects. In summary, this study reveals an atheroprotective function of GPER and introduces selective GPER activation as a novel therapeutic approach to inhibit postmenopausal atherosclerosis and inflammation in the absence of uterotrophic activity.

  10. Peptide ligand recognition by G protein-coupled receptors

    PubMed Central

    Krumm, Brian E.

    2015-01-01

    The past few years have seen spectacular progress in the structure determination of G protein-coupled receptors (GPCRs). We now have structural representatives from classes A, B, C, and F. Within the rhodopsin-like class A, most structures belong to the α group, whereas fewer GPCR structures are available from the β, γ, and δ groups, which include peptide GPCRs such as the receptors for neurotensin (β group), opioids, chemokines (γ group), and protease-activated receptors (δ group). Structural information on peptide GPCRs is restricted to complexes with non-peptidic drug-like antagonists with the exception of the chemokine receptor CXCR4 that has been crystallized in the presence of a cyclic peptide antagonist. Notably, the neurotensin receptor 1 is to date the only peptide GPCR whose structure has been solved in the presence of a peptide agonist. Although limited in number, the current peptide GPCR structures reveal great diversity in shape and electrostatic properties of the ligand binding pockets, features that play key roles in the discrimination of ligands. Here, we review these aspects of peptide GPCRs in view of possible models for peptide agonist binding. PMID:25852552

  11. Structure and Function of Serotonin G protein Coupled Receptors

    PubMed Central

    McCorvy, John D.; Roth, Bryan L.

    2015-01-01

    Serotonin receptors are prevalent throughout the nervous system and the periphery, and remain one of the most lucrative and promising drug discovery targets for disorders ranging from migraine headaches to neuropsychiatric disorders such as schizophrenia and depression. There are 14 distinct serotonin receptors, of which 13 are G protein coupled receptors (GPCRs), which are targets for approximately 40% of the approved medicines. Recent crystallographic and biochemical evidence has provided a converging understanding of the basic structure and functional mechanics of GPCR activation. Currently, two GPCR crystal structures exist for the serotonin family, the 5-HT1B and 5-HT2B receptor, with the antimigraine and valvulopathic drug ergotamine bound. The first serotonin crystal structures not only provide the first evidence of serotonin receptor topography but also provide mechanistic explanations into functional selectivity or biased agonism. This review will detail the findings of these crystal structures from a molecular and mutagenesis perspective for driving rational drug design for novel therapeutics incorporating biased signaling. PMID:25601315

  12. G Protein-coupled Estrogen Receptor Protects from Atherosclerosis

    PubMed Central

    Meyer, Matthias R.; Fredette, Natalie C.; Howard, Tamara A.; Hu, Chelin; Ramesh, Chinnasamy; Daniel, Christoph; Amann, Kerstin; Arterburn, Jeffrey B.; Barton, Matthias; Prossnitz, Eric R.

    2014-01-01

    Coronary atherosclerosis and myocardial infarction in postmenopausal women have been linked to inflammation and reduced nitric oxide (NO) formation. Natural estrogen exerts protective effects on both processes, yet also displays uterotrophic activity. Here, we used genetic and pharmacologic approaches to investigate the role of the G protein-coupled estrogen receptor (GPER) in atherosclerosis. In ovary-intact mice, deletion of gper increased atherosclerosis progression, total and LDL cholesterol levels and inflammation while reducing vascular NO bioactivity, effects that were in some cases aggravated by surgical menopause. In human endothelial cells, GPER was expressed on intracellular membranes and mediated eNOS activation and NO formation, partially accounting for estrogen-mediated effects. Chronic treatment with G-1, a synthetic, highly selective small molecule agonist of GPER, reduced postmenopausal atherosclerosis and inflammation without uterotrophic effects. In summary, this study reveals an atheroprotective function of GPER and introduces selective GPER activation as a novel therapeutic approach to inhibit postmenopausal atherosclerosis and inflammation in the absence of uterotrophic activity. PMID:25532911

  13. Adhesion family of G protein-coupled receptors and cancer.

    PubMed

    Lin, Hsi-Hsien

    2012-01-01

    The adhesion-class G protein-coupled receptors (adhesion-GPCRs) constitute the second largest GPCR sub-family in humans. Adhesion-GPCRs are defined by the chimeric structure of an unusually large extracellular cell-adhesion domain and a GPCR-like seven-pass transmembrane domain. Adhesion-GPCRs are hence expected to display both cellular adhesion and signaling functions in many biological systems. Adhesion-GPCRs are normally expressed in the central nervous, immune, and reproductive systems in a cell type- or tissue-restricted fashion. However, aberrant expression of distinct adhesion-GPCR molecules has been identified in various human cancers with some of the receptors closely associated with cancer development. Tumor-associated adhesion-GPCRs are thought to involve in tumorigenesis by affecting the growth of tumor cells, angiogenesis, tumor cell migration, invasion and metastasis either positively or negatively. Furthermore, some adhesion-GPCRs are considered potential biomarkers for specific types of cancers. In this review article, the expressional characteristics and functional role of cancer-associated adhesion-GPCRs are discussed in depth.

  14. Harnessing the genome for characterization of G-protein coupled receptors in cancer pathogenesis.

    PubMed

    Feigin, Michael E

    2013-10-01

    G-protein coupled receptors (GPCRs) mediate numerous physiological processes and represent the targets for a vast array of therapeutics for diseases ranging from depression to hypertension to reflux. Despite the recognition that GPCRs can act as oncogenes and tumour suppressors by regulating oncogenic signalling networks, few drugs targeting GPCRs are utilized in cancer therapy. Recent large-scale genome-wide analyses of multiple human tumours have uncovered novel GPCRs altered in cancer. However, work aiming to determine which GPCRs from these lists are the drivers of tumourigenesis, and hence valid therapeutic targets, comprises a formidable challenge. The present review highlights recent studies providing evidence that GPCRs are relevant targets for cancer therapy through their effects on known cancer signalling pathways, tumour progression, invasion and metastasis, and the microenvironment. Furthermore, the review also explores how genomic analysis is beginning to highlight GPCRs as therapeutic targets in the age of personalized medicine.

  15. 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.

  16. 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.

  17. Quantifying agonist activity at G protein-coupled receptors.

    PubMed

    Ehlert, Frederick J; Suga, Hinako; Griffin, Michael T

    2011-12-26

    When an agonist activates a population of G protein-coupled receptors (GPCRs), it elicits a signaling pathway that culminates in the response of the cell or tissue. This process can be analyzed at the level of a single receptor, a population of receptors, or a downstream response. Here we describe how to analyze the downstream response to obtain an estimate of the agonist affinity constant for the active state of single receptors. Receptors behave as quantal switches that alternate between active and inactive states (Figure 1). The active state interacts with specific G proteins or other signaling partners. In the absence of ligands, the inactive state predominates. The binding of agonist increases the probability that the receptor will switch into the active state because its affinity constant for the active state (K(b)) is much greater than that for the inactive state (K(a)). The summation of the random outputs of all of the receptors in the population yields a constant level of receptor activation in time. The reciprocal of the concentration of agonist eliciting half-maximal receptor activation is equivalent to the observed affinity constant (K(obs)), and the fraction of agonist-receptor complexes in the active state is defined as efficacy (ε) (Figure 2). Methods for analyzing the downstream responses of GPCRs have been developed that enable the estimation of the K(obs) and relative efficacy of an agonist. In this report, we show how to modify this analysis to estimate the agonist K(b) value relative to that of another agonist. For assays that exhibit constitutive activity, we show how to estimate K(b) in absolute units of M(-1). Our method of analyzing agonist concentration-response curves consists of global nonlinear regression using the operational model. We describe a procedure using the software application, Prism (GraphPad Software, Inc., San Diego, CA). The analysis yields an estimate of the product of K(obs) and a parameter proportional to efficacy (

  18. Dynamic rheology of food protein networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Small amplitude oscillatory shear analyses (SAOSA) of samples containing protein are useful for determining the nature of the protein matrix without damaging it. The Dairy Processing and Products Research Unit of the Agricultural Research Service, USDA has pioneered the use of SAOSA in understandin...

  19. Dynamic rheology of food protein networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Small amplitude oscillatory shear analyses of samples containing protein are useful for determining the nature of the protein matrix without damaging it. Elastic modulus, viscous modulus, and loss tangent (the ratio of viscous modulus to elastic modulus) give information on the strength of the netw...

  20. Protein-protein interaction networks in the spinocerebellar ataxias

    PubMed Central

    Rubinsztein, David C

    2006-01-01

    A large yeast two-hybrid study investigating whether the proteins mutated in different forms of spinocerebellar ataxia have interacting protein partners in common suggests that some forms do share common pathways, and will provide a valuable resource for future work on these diseases. PMID:16904001

  1. Predicting protein function by frequent functional association pattern mining in protein interaction networks.

    PubMed

    Cho, Young-Rae; Zhang, Aidong

    2010-01-01

    Predicting protein function from protein interaction networks has been challenging because of the complexity of functional relationships among proteins. Most previous function prediction methods depend on the neighborhood of or the connected paths to known proteins. However, their accuracy has been limited due to the functional inconsistency of interacting proteins. In this paper, we propose a novel approach for function prediction by identifying frequent patterns of functional associations in a protein interaction network. A set of functions that a protein performs is assigned into the corresponding node as a label. A functional association pattern is then represented as a labeled subgraph. Our frequent labeled subgraph mining algorithm efficiently searches the functional association patterns that occur frequently in the network. It iteratively increases the size of frequent patterns by one node at a time by selective joining, and simplifies the network by a priori pruning. Using the yeast protein interaction network, our algorithm found more than 1400 frequent functional association patterns. The function prediction is performed by matching the subgraph, including the unknown protein, with the frequent patterns analogous to it. By leave-one-out cross validation, we show that our approach has better performance than previous link-based methods in terms of prediction accuracy. The frequent functional association patterns generated in this study might become the foundations of advanced analysis for functional behaviors of proteins in a system level.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

    PubMed Central

    Vinayagam, Arunachalam; Zirin, Jonathan; Roesel, Charles; Hu, Yanhui; Yilmazel, Bahar; Samsonova, Anastasia A.; Neumüller, Ralph A.; Mohr, Stephanie E.; Perrimon, Norbert

    2013-01-01

    A major objective of systems biology is to organize molecular interactions as networks and to characterize information-flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the “signs” of interactions (i.e. activation/inhibition relationships). We constructed a Drosophila melanogaster signed PPI network, consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes Enolase and Aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation/inhibition relationships between physically interacting proteins within signaling pathways will impact our understanding of many biological functions, including signal transduction and mechanisms of disease. PMID:24240319

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

    SciTech Connect

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

    2009-12-01

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

  9. Optimizing information flow in small genetic networks. IV. Spatial coupling

    NASA Astrophysics Data System (ADS)

    Sokolowski, Thomas R.; Tkačik, Gašper

    2015-06-01

    We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.

  10. Optimizing information flow in small genetic networks. IV. Spatial coupling.

    PubMed

    Sokolowski, Thomas R; Tkačik, Gašper

    2015-06-01

    We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.

  11. Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

    PubMed Central

    Tan, Dan; Li, Qiang; Zhang, Mei-Jun; Liu, Chao; Ma, Chengying; Zhang, Pan; Ding, Yue-He; Fan, Sheng-Bo; Tao, Li; Yang, Bing; Li, Xiangke; Ma, Shoucai; Liu, Junjie; Feng, Boya; Liu, Xiaohui; Wang, Hong-Wei; He, Si-Min; Gao, Ning; Ye, Keqiong; Dong, Meng-Qiu; Lei, Xiaoguang

    2016-01-01

    To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction. DOI: http://dx.doi.org/10.7554/eLife.12509.001 PMID:26952210

  12. Protein ranking: From local to global structure in the protein similarity network

    PubMed Central

    Weston, Jason; Elisseeff, Andre; Zhou, Dengyong; Leslie, Christina S.; Noble, William Stafford

    2004-01-01

    Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a precomputed, weighted network. The resulting ranking algorithm, evaluated by using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as psi-blast. PMID:15087500

  13. Interaction networks: from protein functions to drug discovery. A review.

    PubMed

    Chautard, E; Thierry-Mieg, N; Ricard-Blum, S

    2009-06-01

    Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.

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

    PubMed Central

    Du, Chunguang; Xiong, Wenwei; Chen, Xinjian; Deng, Fei; Ma, Zhiyan; Qiao, Dahe; Hu, Chunhui; Ren, Yangliu; Li, Yuling

    2015-01-01

    The formation and development of maize kernel is a complex dynamic physiological and biochemical process that involves the temporal and spatial expression of many proteins and the regulation of metabolic pathways. In this study, the protein profiles of the endosperm and pericarp at three important developmental stages were analyzed by isobaric tags for relative and absolute quantification (iTRAQ) labeling coupled with LC-MS/MS in popcorn inbred N04. Comparative quantitative proteomic analyses among developmental stages and between tissues were performed, and the protein networks were integrated. A total of 6,876 proteins were identified, of which 1,396 were nonredundant. Specific proteins and different expression patterns were observed across developmental stages and tissues. The functional annotation of the identified proteins revealed the importance of metabolic and cellular processes, and binding and catalytic activities for the development of the tissues. The whole, endosperm-specific and pericarp-specific protein networks integrated 125, 9 and 77 proteins, respectively, which were involved in 54 KEGG pathways and reflected their complex metabolic interactions. Confirmation for the iTRAQ endosperm proteins by two-dimensional gel electrophoresis showed that 44.44% proteins were commonly found. However, the concordance between mRNA level and the protein abundance varied across different proteins, stages, tissues and inbred lines, according to the gene cloning and expression analyses of four relevant proteins with important functions and different expression levels. But the result by western blot showed their same expression tendency for the four proteins as by iTRAQ. These results could provide new insights into the developmental mechanisms of endosperm and pericarp, and grain formation in maize. PMID:26587848

  15. A novel approach to synchronization of nonlinearly coupled network systems with delays

    NASA Astrophysics Data System (ADS)

    Tseng, Jui-Pin

    2016-06-01

    In this investigation, a novel approach to establishing the global synchronization of coupled network systems is presented. Under this approach, individual subsystems can be non-autonomous, and the coupling configuration is rather general. The coupling terms can be non-diffusive, nonlinear, time-dependent, asymmetric, and with time delays. With an iteration scheme, the problem of synchronization is transformed into solving a corresponding linear system of algebraic equations. Subsequently, delay-dependent and delay-independent criteria for global synchronization can be established. We implement the present approach to analyze synchronization of the FitzHugh-Nagumo systems under delayed and nonlinear sigmoidal coupling. Two examples are presented to demonstrate new dynamical scenarios, where oscillatory behavior and multistability emerge or are suppressed as the coupled neurons synchronize under the synchronization criterion. In addition, asynchrony induced by the coupling strength or coupling delay occurs while the synchronization criterion is violated.

  16. Methods for Mapping of Interaction Networks Involving Membrane Proteins

    SciTech Connect

    Hooker, Brian S.; Bigelow, Diana J.; Lin, Chiann Tso

    2007-11-23

    Numerous approaches have been taken to study protein interactions, such as tagged protein complex isolation followed by mass spectrometry, yeast two-hybrid methods, fluorescence resonance energy transfer, surface plasmon resonance, site-directed mutagenesis, and crystallography. Membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is not suitable for the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.

  17. Bio-basis function neural networks in protein data mining.

    PubMed

    Yang, Zheng Rong; Hamer, Rebecca

    2007-01-01

    Accurately identifying functional sites in proteins is one of the most important topics in bioinformatics and systems biology. In bioinformatics, identifying protease cleavage sites in protein sequences can aid drug/inhibitor design. In systems biology, post-translational protein-protein interaction activity is one of the major components for analyzing signaling pathway activities. Determining functional sites using laboratory experiments are normally time consuming and expensive. Computer programs have therefore been widely used for this kind of task. Mining protein sequence data using computer programs covers two major issues: 1) discovering how amino acid specificity affects functional sites and 2) discovering what amino acid specificity is. Both need a proper coding mechanism prior to using a proper machine learning algorithm. The development of the bio-basis function neural network (BBFNN) has made a new way for protein sequence data mining. The bio-basis function used in BBFNN is biologically sound in well coding biological information in protein sequences, i.e. well measuring the similarity between protein sequences. BBFNN has therefore been outperforming conventional neural networks in many subjects of protein sequence data mining from protease cleavage site prediction to disordered protein identification. This review focuses on the variants of BBFNN and their applications in mining protein sequence data.

  18. Identification of G protein-coupled receptor signaling pathway proteins in marine diatoms using comparative genomics

    PubMed Central

    2013-01-01

    Background The G protein-coupled receptor (GPCR) signaling pathway plays an essential role in signal transmission and response to external stimuli in mammalian cells. Protein components of this pathway have been characterized in plants and simpler eukaryotes such as yeast, but their presence and role in unicellular photosynthetic eukaryotes have not been determined. We use a comparative genomics approach using whole genome sequences and gene expression libraries of four diatoms (Pseudo-nitzschia multiseries, Thalassiosira pseudonana, Phaeodactylum tricornutum and Fragilariopsis cylindrus) to search for evidence of GPCR signaling pathway proteins that share sequence conservation to known GPCR pathway proteins. Results The majority of the core components of GPCR signaling were well conserved in all four diatoms, with protein sequence similarity to GPCRs, human G protein α- and β-subunits and downstream effectors. There was evidence for the Gγ-subunit and thus a full heterotrimeric G protein only in T. pseudonana. Phylogenetic analysis of putative diatom GPCRs indicated similarity but deep divergence to the class C GPCRs, with branches basal to the GABAB receptor subfamily. The extracellular and intracellular regions of these putative diatom GPCR sequences exhibited large variation in sequence length, and seven of these sequences contained the necessary ligand binding domain for class C GPCR activation. Transcriptional data indicated that a number of the putative GPCR sequences are expressed in diatoms under various stress conditions in culture, and that many of the GPCR-activated signaling proteins, including the G protein, are also expressed. Conclusions The presence of sequences in all four diatoms that code for the proteins required for a functional mammalian GPCR pathway highlights the highly conserved nature of this pathway and suggests a complex signaling machinery related to environmental perception and response in these unicellular organisms. The lack of

  19. Network analysis of perception-action coupling in infants

    PubMed Central

    Rotem-Kohavi, Naama; Hilderman, Courtney G. E.; Liu, Aiping; Makan, Nadia; Wang, Jane Z.; Virji-Babul, Naznin

    2014-01-01

    The functional networks that support action observation are of great interest in understanding the development of social cognition and motor learning. How infants learn to represent and understand the world around them remains one of the most intriguing questions in developmental cognitive neuroscience. Recently, mathematical measures derived from graph theory have been used to study connectivity networks in the developing brain. Thus far, this type of analysis in infancy has only been applied to the resting state. In this study, we recorded electroencephalography (EEG) from infants (ages 4–11 months of age) and adults while they observed three types of actions: (a) reaching for an object; (b) walking; and (c) object motion. Graph theory based analysis was applied to these data to evaluate changes in brain networks. Global metrics that provide measures of the structural properties of the network (characteristic path, density, global efficiency, and modularity) were calculated for each group and for each condition. We found statistically significant differences in measures for the observation of walking condition only. Specifically, in comparison to adults, infants showed increased density and global efficiency in combination with decreased modularity during observation of an action that is not within their motor repertoire (i.e., independent walking), suggesting a less structured organization. There were no group differences in global metric measures for observation of object motion or for observation of actions that are within the repertoire of infants (i.e., reaching). These preliminary results suggest that infants and adults may share a basic functional network for action observation that is sculpted by experience. Motor experience may lead to a shift towards a more efficient functional network. PMID:24778612

  20. Network analysis of perception-action coupling in infants.

    PubMed

    Rotem-Kohavi, Naama; Hilderman, Courtney G E; Liu, Aiping; Makan, Nadia; Wang, Jane Z; Virji-Babul, Naznin

    2014-01-01

    The functional networks that support action observation are of great interest in understanding the development of social cognition and motor learning. How infants learn to represent and understand the world around them remains one of the most intriguing questions in developmental cognitive neuroscience. Recently, mathematical measures derived from graph theory have been used to study connectivity networks in the developing brain. Thus far, this type of analysis in infancy has only been applied to the resting state. In this study, we recorded electroencephalography (EEG) from infants (ages 4-11 months of age) and adults while they observed three types of actions: (a) reaching for an object; (b) walking; and (c) object motion. Graph theory based analysis was applied to these data to evaluate changes in brain networks. Global metrics that provide measures of the structural properties of the network (characteristic path, density, global efficiency, and modularity) were calculated for each group and for each condition. We found statistically significant differences in measures for the observation of walking condition only. Specifically, in comparison to adults, infants showed increased density and global efficiency in combination with decreased modularity during observation of an action that is not within their motor repertoire (i.e., independent walking), suggesting a less structured organization. There were no group differences in global metric measures for observation of object motion or for observation of actions that are within the repertoire of infants (i.e., reaching). These preliminary results suggest that infants and adults may share a basic functional network for action observation that is sculpted by experience. Motor experience may lead to a shift towards a more efficient functional network.

  1. Dissecting the Human Protein-Protein Interaction Network via Phylogenetic Decomposition

    PubMed Central

    Chen, Cho-Yi; Ho, Andy; Huang, Hsin-Yuan; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-01-01

    The protein-protein interaction (PPI) network offers a conceptual framework for better understanding the functional organization of the proteome. However, the intricacy of network complexity complicates comprehensive analysis. Here, we adopted a phylogenic grouping method combined with force-directed graph simulation to decompose the human PPI network in a multi-dimensional manner. This network model enabled us to associate the network topological properties with evolutionary and biological implications. First, we found that ancient proteins occupy the core of the network, whereas young proteins tend to reside on the periphery. Second, the presence of age homophily suggests a possible selection pressure may have acted on the duplication and divergence process during the PPI network evolution. Lastly, functional analysis revealed that each age group possesses high specificity of enriched biological processes and pathway engagements, which could correspond to their evolutionary roles in eukaryotic cells. More interestingly, the network landscape closely coincides with the subcellular localization of proteins. Together, these findings suggest the potential of using conceptual frameworks to mimic the true functional organization in a living cell. PMID:25412639

  2. An entropic characterization of protein interaction networks and cellular robustness.

    PubMed

    Manke, Thomas; Demetrius, Lloyd; Vingron, Martin

    2006-12-22

    The structure of molecular networks is believed to determine important aspects of their cellular function, such as the organismal resilience against random perturbations. Ultimately, however, cellular behaviour is determined by the dynamical processes, which are constrained by network topology. The present work is based on a fundamental relation from dynamical systems theory, which states that the macroscopic resilience of a steady state is correlated with the uncertainty in the underlying microscopic processes, a property that can be measured by entropy. Here, we use recent network data from large-scale protein interaction screens to characterize the diversity of possible pathways in terms of network entropy. This measure has its origin in statistical mechanics and amounts to a global characterization of both structural and dynamical resilience in terms of microscopic elements. We demonstrate how this approach can be used to rank network elements according to their contribution to network entropy and also investigate how this suggested ranking reflects on the functional data provided by gene knockouts and RNAi experiments in yeast and Caenorhabditis elegans. Our analysis shows that knockouts of proteins with large contribution to network entropy are preferentially lethal. This observation is robust with respect to several possible errors and biases in the experimental data. It underscores the significance of entropy as a fundamental invariant of the dynamical system, and as a measure of structural and dynamical properties of networks. Our analytical approach goes beyond the phenomenological studies of cellular robustness based on local network observables, such as connectivity. One of its principal achievements is to provide a rationale to study proxies of cellular resilience and rank proteins according to their importance within the global network context.

  3. Synergistic plasticity of intrinsic conductance and electrical coupling restores synchrony in an intact motor network.

    PubMed

    Lane, Brian J; Samarth, Pranit; Ransdell, Joseph L; Nair, Satish S; Schulz, David J

    2016-08-23

    Motor neurons of the crustacean cardiac ganglion generate virtually identical, synchronized output despite the fact that each neuron uses distinct conductance magnitudes. As a result of this variability, manipulations that target ionic conductances have distinct effects on neurons within the same ganglion, disrupting synchronized motor neuron output that is necessary for proper cardiac function. We hypothesized that robustness in network output is accomplished via plasticity that counters such destabilizing influences. By blocking high-threshold K(+) conductances in motor neurons within the ongoing cardiac network, we discovered that compensation both resynchronized the network and helped restore excitability. Using model findings to guide experimentation, we determined that compensatory increases of both GA and electrical coupling restored function in the network. This is one of the first direct demonstrations of the physiological regulation of coupling conductance in a compensatory context, and of synergistic plasticity across cell- and network-level mechanisms in the restoration of output.

  4. Contagion processes on the static and activity-driven coupling networks.

    PubMed

    Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

    2016-03-01

    The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

  5. Contagion processes on the static and activity-driven coupling networks

    NASA Astrophysics Data System (ADS)

    Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

    2016-03-01

    The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

  6. Novel insights into G protein and G protein-coupled receptor signaling in cancer.

    PubMed

    O'Hayre, Morgan; Degese, Maria S; Gutkind, J Silvio

    2014-04-01

    G protein-coupled receptors (GPCRs) play a central role in signal transmission, thereby controlling many facets of cellular function. Overwhelming evidence now implicates GPCRs, G proteins and their downstream signaling targets in cancer initiation and progression, where they can influence aberrant cell growth and survival, largely through activation of AKT/mTOR, MAPKs, and Hippo signaling pathways. GPCRs also play critical roles in the invasion and metastasis of cancer cells via activation of Rho GTPases and cytoskeletal changes, and angiogenesis to supply the tumor with nutrients and provide routes for metastasis. Lastly, GPCRs contribute to the establishment and maintenance of a permissive tumor microenvironment. Understanding GPCR involvement in cancer malignancy may help identify novel therapeutic opportunities for cancer prevention and treatment.

  7. Switching dynamics of single and coupled VO2-based oscillators as elements of neural networks

    NASA Astrophysics Data System (ADS)

    Velichko, Andrey; Belyaev, Maksim; Putrolaynen, Vadim; Pergament, Alexander; Perminov, Valentin

    2017-01-01

    In the present paper, we report on the switching dynamics of both single and coupled VO2-based oscillators, with resistive and capacitive coupling, and explore the capability of their application in oscillatory neural networks. Based on these results, we further select an adequate SPICE model to describe the modes of operation of coupled oscillator circuits. Physical mechanisms influencing the time of forward and reverse electrical switching, that determine the applicability limits of the proposed model, are identified. For the resistive coupling, it is shown that synchronization takes place at a certain value of the coupling resistance, though it is unstable and a synchronization failure occurs periodically. For the capacitive coupling, two synchronization modes, with weak and strong coupling, are found. The transition between these modes is accompanied by chaotic oscillations. A decrease in the width of the spectrum harmonics in the weak-coupling mode, and its increase in the strong-coupling one, is detected. The dependences of frequencies and phase differences of the coupled oscillatory circuits on the coupling capacitance are found. Examples of operation of coupled VO2 oscillators as a central pattern generator are demonstrated.

  8. Topology-free querying of protein interaction networks.

    PubMed

    Bruckner, Sharon; Hüffner, Falk; Karp, Richard M; Shamir, Ron; Sharan, Roded

    2010-03-01

    In the network querying problem, one is given a protein complex or pathway of species A and a protein-protein interaction network of species B; the goal is to identify subnetworks of B that are similar to the query in terms of sequence, topology, or both. Existing approaches mostly depend on knowledge of the interaction topology of the query in the network of species A; however, in practice, this topology is often not known. To address this problem, we develop a topology-free querying algorithm, which we call Torque. Given a query, represented as a set of proteins, Torque seeks a matching set of proteins that are sequence-similar to the query proteins and span a connected region of the network, while allowing both insertions and deletions. The algorithm uses alternatively dynamic programming and integer linear programming for the search task. We test Torque with queries from yeast, fly, and human, where we compare it to the QNet topology-based approach, and with queries from less studied species, where only topology-free algorithms apply. Torque detects many more matches than QNet, while giving results that are highly functionally coherent.

  9. Proteins and an Inflammatory Network Expressed in Colon Tumors

    PubMed Central

    Zhu, Wenhong; Fang, Changming; Gramatikoff, Kosi; Niemeyer, Christina C.; Smith, Jeffrey W.

    2011-01-01

    The adenomatous polyposis coli (APC) protein is crucial to homeostasis of normal intestinal epithelia because it suppresses the β-catenin/TCF pathway. Consequently, loss or mutation of the APC gene causes colorectal tumors in humans and mice. Here, we describe our use of Multidimensional Protein Identification Technology (MudPIT) to compare protein expression in colon tumors to that of adjacent healthy colon tissue from ApcMin/+ mice. Twenty-seven proteins were found to be up-regulated in colon tumors and twenty-five down-regulated. As an extension of the proteomic analysis, the differentially expressed proteins were used as “seeds” to search for co-expressed genes. This approach revealed a co-expression network of 45 genes that is up-regulated in colon tumors. Members of the network include the antibacterial peptide cathelicidin (CAMP), Toll-like receptors (TLRs), IL-8, and triggering receptor expressed on myeloid cells 1 (TREM1). The co-expression network is associated with innate immunity and inflammation, and there is significant concordance between its connectivity in humans versus mice (Friedman: p value = 0.0056). This study provides new insights into the proteins and networks that are likely to drive the onset and progression of colon cancer. PMID:21366352

  10. Reconfiguration of Intrinsic Functional Coupling Patterns Following Circumscribed Network Lesions.

    PubMed

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

    2016-05-25

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

  11. Workshop: Theory an Applications of Coupled Cell Networks

    DTIC Science & Technology

    2006-03-22

    work is joint with Peter Ashwin (Exeter). The impact of architecture on the structure of solutions in networks of phase oscillators Kresimir Josic...Golubitsky, Kresimir Josic (University of Houston) Periodic bursting in fast-slow systems can be viewed as closed paths through the unfolding parameters

  12. Engineering the Dynamic Properties of Protein Networks through Sequence Variation

    PubMed Central

    2016-01-01

    The dynamic behavior of macromolecular networks dominates the mechanical properties of soft materials and influences biological processes at multiple length scales. In hydrogels prepared from self-assembling artificial proteins, stress relaxation and energy dissipation arise from the transient character of physical network junctions. Here we show that subtle changes in sequence can be used to program the relaxation behavior of end-linked networks of engineered coiled-coil proteins. Single-site substitutions in the coiled-coil domains caused shifts in relaxation time over 5 orders of magnitude as demonstrated by dynamic oscillatory shear rheometry and stress relaxation measurements. Networks with multiple relaxation time scales were also engineered. This work demonstrates how time-dependent mechanical responses of macromolecular materials can be encoded in genetic information. PMID:27924309

  13. Droplet networks with incorporated protein diodes show collective properties.

    PubMed

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

    2009-07-01

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

  14. Response of the mosquito protein interaction network to dengue infection

    PubMed Central

    2010-01-01

    Background Two fifths of the world's population is at risk from dengue. The absence of effective drugs and vaccines leaves vector control as the primary intervention tool. Understanding dengue virus (DENV) host interactions is essential for the development of novel control strategies. The availability of genome sequences for both human and mosquito host greatly facilitates genome-wide studies of DENV-host interactions. Results We developed the first draft of the mosquito protein interaction network using a computational approach. The weighted network includes 4,214 Aedes aegypti proteins with 10,209 interactions, among which 3,500 proteins are connected into an interconnected scale-free network. We demonstrated the application of this network for the further annotation of mosquito proteins and dissection of pathway crosstalk. Using three datasets based on physical interaction assays, genome-wide RNA interference (RNAi) screens and microarray assays, we identified 714 putative DENV-associated mosquito proteins. An integrated analysis of these proteins in the network highlighted four regions consisting of highly interconnected proteins with closely related functions in each of replication/transcription/translation (RTT), immunity, transport and metabolism. Putative DENV-associated proteins were further selected for validation by RNAi-mediated gene silencing, and dengue viral titer in mosquito midguts was significantly reduced for five out of ten (50.0%) randomly selected genes. Conclusions Our results indicate the presence of common host requirements for DENV in mosquitoes and humans. We discuss the significance of our findings for pharmacological intervention and genetic modification of mosquitoes for blocking dengue transmission. PMID:20553610

  15. Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks.

    PubMed

    Barranca, Victor J; Zhou, Douglas; Cai, David

    2016-06-01

    Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.

  16. Predicting synchronous and asynchronous network groupings of hippocampal interneurons coupled with dendritic gap junctions.

    PubMed

    Zahid, Tariq; Skinner, Frances K

    2009-03-25

    Direct electrical communication between central nervous system (CNS) neurons including those in the hippocampus is well-established. This form of communication is mediated by gap junctions and it is known that this coupling is important for brain rhythms such as gamma (20-80 Hz) which occur during active behavioural states. It is also known that gap junctions are present at several locations along the dendrites of hippocampal interneurons including parvalbumin-positive basket cell types. Weakly coupled oscillator theory, which uses phase response curves (PRCs), has been used to understand and predict the dynamics of electrically coupled networks. Here we use compartmental models of hippocampal basket cells with different levels of basal and apical spike attenuation together with the theory to show that network output can be broken down into three groupings: synchronous, asynchronous and antiphase-like patterns. Moreover, quantified PRCs can be used as a rule of thumb to determine the occurrence of a particular grouping under weak coupling conditions, which in turn implies that spike delays are critical factors in determining network output. In moving beyond weak coupling to encompass the full physiological regime of coupling strengths with network simulations, we note that it is important to be able to differentiate between these different groupings as it affects how the network responds with modulation. Specifically, an asynchronous grouping provides more dynamic richness as a larger range of phase-locked states can be expressed with strength changes. From a functional viewpoint it may be that modulation of electrically coupled networks are key to controlling cell assemblies that contribute to information coding brain substrates.

  17. Transient spatiotemporal chaos in a diffusively and synaptically coupled Morris-Lecar neuronal network

    NASA Astrophysics Data System (ADS)

    Lafranceschina, Jacopo

    Transient spatiotemporal chaos was reported in models for chemical reactions and in experiments for turbulence in shear flow. This study shows that transient spatiotemporal chaos also exists in a diffusively coupled Morris-Lecar (ML) neuronal network, with a collapse to either a global rest state or to a state of pulse propagation. Adding synaptic coupling to this network reduces the average lifetime of spatiotemporal chaos for small to intermediate coupling strengths and almost all numbers of synapses. For large coupling strengths, 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 Lyapunov exponent and degree of phase coherence as the number of synaptic links increases. In contrast to the diffusive network, the pulse solution must not be asymptotic in the presence of synapses. The fact that chaos could be transient in higher dimensional systems, such as the one being explored in this study, point to its presence in every day life. Transient spatiotemporal chaos in a network of coupled neurons and the associated chaotic saddle provide a possibility for switching between metastable states observed in information processing and brain function. Such transient dynamics have been observed experimentally by Mazor, when stimulating projection neurons in the locust antennal lobe with different odors.

  18. Multistability and bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks with Delays.

    PubMed

    Ling, Guang; Guan, Zhi-Hong; Hu, Bin; Lai, Qiang; Wu, Yonghong

    2017-02-14

    Many biological systems have the conspicuous property to present more than one stable state and diverse rhythmic behaviors. A closed relationship between these complex dynamic behaviors and cyclic genetic structures has been witnessed by pioneering works. In this paper, a typical structure of inhibitory coupled cyclic genetic networks is introduced to further enlighten this mechanism of stability and biological rhythms of living cells. The coupled networks consist two identical cyclic genetic subnetworks, which inhibit each other directly. Each subnetwork can be regarded as a genetic unit at the cellular level. Multiple time delays, including both internal delays and coupling delays, are considered. The existence of positive equilibriums for this kind of coupled systems is proved, and the stability for each equilibrium are analyzed without or with delays. It is shown that the coupled networks with positive cyclic genetic units have a ability to show multistability, while the coupled networks with negative units may present a series of Hopf bifurcations with the variation of time delays. Several numerical simulations are made to prove our results.

  19. Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands

    PubMed Central

    Xie, Xiang-Qun; Wang, Lirong; Liu, Haibin; Ouyang, Qin; Fang, Cheng; Su, Weiwei

    2013-01-01

    Drug abuse (DA) and addiction is a complex illness, broadly viewed as a neurobiological impairment with genetic and environmental factors that influence its development and manifestation. Abused substances can disrupt the activity of neurons by interacting with many proteins, particularly G-protein coupled receptors (GPCRs). A few medicines that target the central nervous system (CNS) can also modulate DA related proteins, such as GPCRs, which can act in conjunction with the controlled psychoactive substance(s) and increase side effects. To fully explore the molecular interaction networks that underlie DA and to effectively modulate the GPCRs in these networks with small molecules for DA treatment, we built a drug-abuse domain specific chemogenomics knowledgebase (DA-KB) to centralize the reported chemogenomics research information related to DA and CNS disorders in an effort to benefit researchers across a broad range of disciplines. We then focus on the analysis of GPCRs as many of them are closely related with DA. Their distribution in human tissues was also analyzed for the study of side effects caused by abused drugs. We further implement our computational algorithms/tools to explore DA targets, DA mechanisms and pathways involved in polydrug addiction and to explore polypharmacological effects of the GPCR ligands. Finally, the polypharmacology effects of GPCRs-targeted medicines for DA treatment were investigated and such effects can be exploited for the development of drugs with polypharmacophore for DA intervention. The chemogenomics database and the analysis tools will help us better understand the mechanism of drugs abuse and facilitate to design new medications for system pharmacotherapy of DA. PMID:24567719

  20. Cascading failures of interdependent modular scale-free networks with different coupling preferences

    NASA Astrophysics Data System (ADS)

    Tian, Meng; Wang, Xianpei; Dong, Zhengcheng; Zhu, Guowei; Long, Jiachuang; Dai, Dangdang; Zhang, Qilin

    2015-07-01

    As one of the most important mesoscopic properties of networks, the community structure plays an important role in cascading failures on isolated networks. However, the study for understanding the influences of the community structure on the cascading failures on interdependent scale-free networks remains missing. In this paper, we investigate cascading failures on interdependent modular scale-free networks under inner attacks and hub attacks from the global and local perspective. We mainly analyse the inter-community connections and coupling preferences, i.e. random coupling in communities (RCIC), assortative coupling in communities (ACIC) and assortative coupling with communities (ACWC). We find that increasing inter-community connections can enhance the robustness of interdependent modular scale-free networks for both inner attacks and hub attacks. Furthermore, we also find that the ACIC is more beneficial to resisting cascading failures compared with RCIC or ACWC. For ACIC, the cascading failures propagate mainly in a local community where the initial failure occurs. It is meaningful to control the cascading failures on interdependent modular scale-free networks by constructing ACIC.

  1. Differences in protein-protein association networks for lung adenocarcinoma: A retrospective study

    PubMed Central

    Datta, Anisha; Sikdar, Sinjini; Gill, Ryan

    2014-01-01

    Various methods to determine the connectivity scores between groups of proteins associated with lung adenocarcinoma are examined. Proteins act together to perform a wide range of functions within biological processes. Hence, identification of key proteins and their interactions within protein networks can provide invaluable information on disease mechanisms. Differential network analysis provides a means of identifying differences in the interactions among proteins between two networks. We use connectivity scores based on the method of partial least squares to quantify the strength of the interactions between each pair of proteins. These scores are then used to perform permutation-based statistical tests. This examines if there are significant differences between the network connectivity scores for individual proteins or classes of proteins. The expression data from a study on lung adenocarcinoma is used in this study. Connectivity scores are computed for a group of 109 subjects who were in the complete remission and as well as for a group of 51 subjects whose cancer had progressed. The distributions of the connectivity scores are similar for the two networks yet subtle but statistically significant differences have been identified and their impact discussed. PMID:25489174

  2. Optimizing a global alignment of protein interaction networks

    PubMed Central

    Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie

    2013-01-01

    Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. Contact: bab@csail.mit.edu or csliao@ie.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24048352

  3. Efficient mapping of ligand migration channel networks in dynamic proteins.

    PubMed

    Lin, Tu-Liang; Song, Guang

    2011-08-01

    For many proteins such as myoglobin, the binding site lies in the interior, and there is no obvious route from the exterior to the binding site in the average structure. Although computer simulations for a limited number of proteins have found some transiently open channels, it is not clear if there exist more channels elsewhere or how the channels are regulated. A systematic approach that can map out the whole ligand migration channel network is lacking. Ligand migration in a dynamic protein resembles closely a well-studied problem in robotics, namely, the navigation of a mobile robot in a dynamic environment. In this work, we present a novel robotic motion planning inspired approach that can map the ligand migration channel network in a dynamic protein. The method combines an efficient spatial mapping of protein inner space with a temporal exploration of protein structural heterogeneity, which is represented by a structure ensemble. The spatial mapping of each conformation in the ensemble produces a partial map of protein inner cavities and their inter-connectivity. These maps are then merged to form a super map that contains all the channels that open dynamically. Results on the pathways in myoglobin for gaseous ligands demonstrate the efficiency of our approach in mapping the ligand migration channel networks. The results, obtained in a significantly less amount of time than trajectory-based approaches, are in agreement with previous simulation results. Additionally, the method clearly illustrates how and what conformational changes open or close a channel.

  4. Transactivation of Epidermal Growth Factor Receptor by G Protein-Coupled Receptors: Recent Progress, Challenges and Future Research

    PubMed Central

    Wang, Zhixiang

    2016-01-01

    Both G protein-coupled receptors (GPCRs) and receptor-tyrosine kinases (RTKs) regulate large signaling networks, control multiple cell functions and are implicated in many diseases including various cancers. Both of them are also the top therapeutic targets for disease treatment. The discovery of the cross-talk between GPCRs and RTKs connects these two vast signaling networks and complicates the already complicated signaling networks that regulate cell signaling and function. In this review, we focus on the transactivation of epidermal growth factor receptor (EGFR), a subfamily of RTKs, by GPCRs. Since the first report of EGFR transactivation by GPCR, significant progress has been made including the elucidation of the mechanisms underlying the transactivation. Here, we first provide a basic picture for GPCR, EGFR and EGFR transactivation by GPCR. We then discuss the progress made in the last five years and finally provided our view of the future challenge and future researches needed to overcome these challenges. PMID:26771606

  5. Transactivation of Epidermal Growth Factor Receptor by G Protein-Coupled Receptors: Recent Progress, Challenges and Future Research.

    PubMed

    Wang, Zhixiang

    2016-01-12

    Both G protein-coupled receptors (GPCRs) and receptor-tyrosine kinases (RTKs) regulate large signaling networks, control multiple cell functions and are implicated in many diseases including various cancers. Both of them are also the top therapeutic targets for disease treatment. The discovery of the cross-talk between GPCRs and RTKs connects these two vast signaling networks and complicates the already complicated signaling networks that regulate cell signaling and function. In this review, we focus on the transactivation of epidermal growth factor receptor (EGFR), a subfamily of RTKs, by GPCRs. Since the first report of EGFR transactivation by GPCR, significant progress has been made including the elucidation of the mechanisms underlying the transactivation. Here, we first provide a basic picture for GPCR, EGFR and EGFR transactivation by GPCR. We then discuss the progress made in the last five years and finally provided our view of the future challenge and future researches needed to overcome these challenges.

  6. Visualization of coupled protein folding and binding in bacteria and purification of the heterodimeric complex

    PubMed Central

    Wang, Haoyong; Chong, Shaorong

    2003-01-01

    During overexpression of recombinant proteins in Escherichia coli, misfolded proteins often aggregate and form inclusion bodies. If an aggregation-prone recombinant protein is fused upstream (as an N-terminal fusion) to GFP, aggregation of the recombinant protein domain also leads to misfolding of the downstream GFP domain, resulting in a decrease or loss of fluorescence. We investigated whether the GFP domain could fold correctly if aggregation of the upstream protein domain was prevented in vivo by a coupled protein folding and binding interaction. Such interaction has been previously shown to occur between the E. coli integration host factors α and β, and between the domains of the general transcriptional coactivator cAMP response element binding protein (CREB)-binding protein and the activator for thyroid hormone and retinoid receptors. In this study, fusion of integration host factor β or the CREB-binding protein domain upstream to GFP resulted in aggregation of the fusion protein. Coexpression of their respective partners, on the other hand, allowed soluble expression of the fusion protein and a dramatic increase in fluorescence. The study demonstrated that coupled protein folding and binding could be correlated to GFP fluorescence. A modified miniintein containing an affinity tag was inserted between the upstream protein domain and GFP to allow rapid purification and identification of the heterodimeric complex. The GFP coexpression fusion system may be used to identify novel protein–protein interactions that involve coupled folding and binding or protein partners that can solubilize aggregation-prone recombinant proteins. PMID:12515863

  7. Experimental evolution of protein–protein interaction networks

    PubMed Central

    Kaçar, Betül; Gaucher, Eric A.

    2013-01-01

    The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

  8. A coclustering approach for mining large protein-protein interaction networks.

    PubMed

    Pizzuti, Clara; Rombo, Simona E

    2012-01-01

    Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized networks are considered. We present a coclustering-based technique able to generate both overlapping and nonoverlapping clusters. The density of the clusters to search for can also be set by the user. We tested our method on the two networks of yeast and human, and compared it to other five well-known techniques on the same interaction data sets. The results showed that, for all the examples considered, our approach always reaches a good compromise between accuracy and network coverage. Furthermore, the behavior of our algorithm is not influenced by the structure of the input network, different from all the techniques considered in the comparison, which returned very good results on the yeast network, while on the human network their outcomes are rather poor.

  9. Protein function prediction using guilty by association from interaction networks.

    PubMed

    Piovesan, Damiano; Giollo, Manuel; Ferrari, Carlo; Tosatto, Silvio C E

    2015-12-01

    Protein function prediction from sequence using the Gene Ontology (GO) classification is useful in many biological problems. It has recently attracted increasing interest, thanks in part to the Critical Assessment of Function Annotation (CAFA) challenge. In this paper, we introduce Guilty by Association on STRING (GAS), a tool to predict protein function exploiting protein-protein interaction networks without sequence similarity. The assumption is that whenever a protein interacts with other proteins, it is part of the same biological process and located in the same cellular compartment. GAS retrieves interaction partners of a query protein from the STRING database and measures enrichment of the associated functional annotations to generate a sorted list of putative functions. A performance evaluation based on CAFA metrics and a fair comparison with optimized BLAST similarity searches is provided. The consensus of GAS and BLAST is shown to improve overall performance. The PPI approach is shown to outperform similarity searches for biological process and cellular compartment GO predictions. Moreover, an analysis of the best practices to exploit protein-protein interaction networks is also provided.

  10. Protein-RNA networks revealed through covalent RNA marks

    PubMed Central

    Lapointe, Christopher P.; Wilinski, Daniel; Saunders, Harriet A. J.; Wickens, Marvin

    2015-01-01

    Protein-RNA networks are ubiquitous and central in biological control. We present an approach, termed “RNA Tagging,” that identifies protein-RNA interactions in vivo by analyzing purified cellular RNA, without protein purification or crosslinking. An RNA-binding protein of interest is fused to an enzyme that adds uridines to the end of RNA. RNA targets bound by the chimeric protein in vivo are covalently marked with uridines and subsequently identified from extracted RNA using high-throughput sequencing. We used this approach to identify hundreds of RNAs bound by a Saccharomyces cerevisiae PUF protein, Puf3p. The method revealed that while RNA-binding proteins productively bind specific RNAs to control their function, they also “sample” RNAs without exerting a regulatory effect. We exploited the method to uncover hundreds of new and likely regulated targets for a protein without canonical RNA-binding domains, Bfr1p. The RNA Tagging approach is well-suited to detect and analyze protein-RNA networks in vivo. PMID:26524240

  11. Towards a matrix mechanics framework for dynamic protein network

    PubMed Central

    2010-01-01

    Protein–protein interaction networks are currently visualized by software generated interaction webs based upon static experimental data. Current state is limited to static, mostly non-compartmental network and non time resolved protein interactions. A satisfactory mathematical foundation for particle interactions within a viscous liquid state (situation within the cytoplasm) does not exist nor do current computer programs enable building dynamic interaction networks for time resolved interactions. Building mathematical foundation for intracellular protein interactions can be achieved in two increments (a) trigger and capture the dynamic molecular changes for a select subset of proteins using several model systems and high throughput time resolved proteomics and, (b) use this information to build the mathematical foundation and computational algorithm for a compartmentalized and dynamic protein interaction network. Such a foundation is expected to provide benefit in at least two spheres: (a) understanding physiology enabling explanation of phenomenon such as incomplete penetrance in genetic disorders and (b) enabling several fold increase in biopharmaceutical production using impure starting materials. PMID:20805933

  12. Synchronization of Coupled Reaction-Diffusion Neural Networks With Directed Topology via an Adaptive Approach.

    PubMed

    Zhang, Hao; Sheng, Yin; Zeng, Zhigang

    2017-03-15

    This paper investigates the synchronization issue of coupled reaction-diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results.

  13. Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks

    PubMed Central

    2011-01-01

    Background Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful. Results In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma genitalium. Conclusions To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network. PMID:21486441

  14. RAIN: RNA–protein Association and Interaction Networks

    PubMed Central

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

    2017-01-01

    Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA–RNA and ncRNA–protein interactions and its integration with the STRING database of protein–protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded. Database URL: http://rth.dk/resources/rain PMID:28077569

  15. Reconstruction and Application of Protein–Protein Interaction Network

    PubMed Central

    Hao, Tong; Peng, Wei; Wang, Qian; Wang, Bin; Sun, Jinsheng

    2016-01-01

    The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms. PMID:27338356

  16. Modeling of the water network at protein-RNA interfaces.

    PubMed

    Li, Yiyu; Sutch, Brian T; Bui, Huynh-Hoa; Gallaher, Timothy K; Haworth, Ian S

    2011-06-27

    Water plays an important role in the mediation of biomolecular interactions. Thus, accurate prediction and evaluation of water-mediated interactions is an important element in the computational design of interfaces involving proteins, RNA, and DNA. Here, we use an algorithm (WATGEN) to predict the locations of interfacial water molecules for a data set of 224 protein-RNA interfaces. The accuracy of the prediction is validated against water molecules present in the X-ray structures of 105 of these complexes. The complexity of the water networks is deconvoluted through definition of the characteristics of each water molecule based on its bridging properties between the protein and RNA and on its depth in the interface with respect to the bulk solvent. This approach has the potential for scoring the water network for incorporation into the computational design of protein-RNA complexes.

  17. A web-based protein interaction network visualizer

    PubMed Central

    2014-01-01

    Background Interaction between proteins is one of the most important mechanisms in the execution of cellular functions. The study of these interactions has provided insight into the functioning of an organism’s processes. As of October 2013, Homo sapiens had over 170000 Protein-Protein interactions (PPI) registered in the Interologous Interaction Database, which is only one of the many public resources where protein interactions can be accessed. These numbers exemplify the volume of data that research on the topic has generated. Visualization of large data sets is a well known strategy to make sense of information, and protein interaction data is no exception. There are several tools that allow the exploration of this data, providing different methods to visualize protein network interactions. However, there is still no native web tool that allows this data to be explored interactively online. Results Given the advances that web technologies have made recently it is time to bring these interactive views to the web to provide an easily accessible forum to visualize PPI. We have created a Web-based Protein Interaction Network Visualizer: PINV, an open source, native web application that facilitates the visualization of protein interactions (http://biosual.cbio.uct.ac.za/pinv.html). We developed PINV as a set of components that follow the protocol defined in BioJS and use the D3 library to create the graphic layouts. We demonstrate the use of PINV with multi-organism interaction networks for a predicted target from Mycobacterium tuberculosis, its interacting partners and its orthologs. Conclusions The resultant tool provides an attractive view of complex, fully interactive networks with components that allow the querying, filtering and manipulation of the visible subset. Moreover, as a web resource, PINV simplifies sharing and publishing, activities which are vital in today’s research collaborative environments. The source code is freely available for download at

  18. Spliced X-box Binding Protein 1 Couples the Unfolded Protein Response to Hexosamine Biosynthetic Pathway

    PubMed Central

    Wang, Zhao V.; Deng, Yingfeng; Gao, Ningguo; Pedrozo, Zully; Li, Dan L.; Morales, Cyndi R.; Criollo, Alfredo; Luo, Xiang; Tan, Wei; Jiang, Nan; Lehrman, Mark A.; Rothermel, Beverly A.; Lee, Ann-Hwee; Lavandero, Sergio; Mammen, Pradeep P.A.; Ferdous, Anwarul; Gillette, Thomas G.; Scherer, Philipp E.; Hill, Joseph A.

    2014-01-01

    SUMMARY The hexosamine biosynthetic pathway (HBP) generates UDP-GlcNAc (uridine diphosphate N-acetylglucosamine) for glycan synthesis and O-linked GlcNAc (O-GlcNAc) protein modifications. Despite the established role of the HBP in metabolism and multiple diseases, regulation of the HBP remains largely undefined. Here, we show that spliced X-box binding protein 1 (Xbp1s), the most conserved signal transducer of the unfolded protein response (UPR), is a direct transcriptional activator of the HBP. We demonstrate that the UPR triggers HBP activation via Xbp1s-dependent transcription of genes coding for key, rate-limiting enzymes. We further establish that this previously unrecognized UPR-HBP axis is triggered in a variety of stress conditions. Finally, we demonstrate a physiologic role for the UPR-HBP axis, by showing that acute stimulation of Xbp1s in heart by ischemia/reperfusion confers robust cardioprotection in part through induction of the HBP. Collectively, these studies reveal that Xbp1s couples the UPR to the HBP to protect cells under stress. PMID:24630721

  19. Spliced X-box binding protein 1 couples the unfolded protein response to hexosamine biosynthetic pathway.

    PubMed

    Wang, Zhao V; Deng, Yingfeng; Gao, Ningguo; Pedrozo, Zully; Li, Dan L; Morales, Cyndi R; Criollo, Alfredo; Luo, Xiang; Tan, Wei; Jiang, Nan; Lehrman, Mark A; Rothermel, Beverly A; Lee, Ann-Hwee; Lavandero, Sergio; Mammen, Pradeep P A; Ferdous, Anwarul; Gillette, Thomas G; Scherer, Philipp E; Hill, Joseph A

    2014-03-13

    The hexosamine biosynthetic pathway (HBP) generates uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) for glycan synthesis and O-linked GlcNAc (O-GlcNAc) protein modifications. Despite the established role of the HBP in metabolism and multiple diseases, regulation of the HBP remains largely undefined. Here, we show that spliced X-box binding protein 1 (Xbp1s), the most conserved signal transducer of the unfolded protein response (UPR), is a direct transcriptional activator of the HBP. We demonstrate that the UPR triggers HBP activation via Xbp1s-dependent transcription of genes coding for key, rate-limiting enzymes. We further establish that this previously unrecognized UPR-HBP axis is triggered in a variety of stress conditions. Finally, we demonstrate a physiologic role for the UPR-HBP axis by showing that acute stimulation of Xbp1s in heart by ischemia/reperfusion confers robust cardioprotection in part through induction of the HBP. Collectively, these studies reveal that Xbp1s couples the UPR to the HBP to protect cells under stress.

  20. Rab coupling protein (RCP), a novel Rab4 and Rab11 effector protein.

    PubMed

    Lindsay, Andrew J; Hendrick, Alan G; Cantalupo, Giuseppina; Senic-Matuglia, Francesca; Goud, Bruno; Bucci, Cecilia; McCaffrey, Mary W

    2002-04-05

    Rab4 and Rab11 are small GTPases belonging to the Ras superfamily. They both function as regulators along the receptor recycling pathway. We have identified a novel 80-kDa protein that interacts specifically with the GTP-bound conformation of Rab4, and subsequent work has shown that it also interacts strongly with Rab11. We name this protein Rab coupling protein (RCP). RCP is predominantly membrane-bound and is expressed in all cell lines and tissues tested. It colocalizes with early endosomal markers including Rab4 and Rab11 as well as with the transferrin receptor. Overexpression of the carboxyl-terminal region of RCP, which contains the Rab4- and Rab11-interacting domain, results in a dramatic tubulation of the transferrin compartment. Furthermore, expression of this mutant causes a significant reduction in endosomal recycling without affecting ligand uptake or degradation in quantitative assays. RCP is a homologue of Rip11 and therefore belongs to the recently described Rab11-FIP family.

  1. Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators.

    PubMed

    Nakano, H; Saito, T

    2004-09-01

    This paper studies a pulse-coupled network consisting of simple chaotic spiking oscillators (CSOs). If a unit oscillator and its neighbor(s) have (almost) the same parameter values, they exhibit in-phase synchronization of chaos. As the parameter values differ, they exhibit asynchronous phenomena. Based on such behavior, some synchronous groups appear partially in the network. Typical phenomena are verified in the laboratory via a simple test circuit. These phenomena can be evaluated numerically by using an effective mapping procedure. We then apply the proposed network to image segmentation. Using a lattice pulse-coupled network via grouping synchronous phenomena, the input image data can be segmented into some sub-regions.

  2. Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.

    PubMed

    Li, Ning; Cao, Jinde

    2016-03-01

    This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.

  3. Spike phase synchronization in delayed-coupled neural networks: Uniform vs. non-uniform transmission delay

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2013-03-01

    In this paper, we investigated phase synchronization in delayed dynamical networks. Non-identical spiking Hindmarsh-Rose neurons were considered as individual dynamical systems and coupled through a number of network structures such as scale-free, Erdős-Rényi, and modular. The individual neurons were coupled through excitatory chemical synapses with uniform or distributed time delays. The profile of spike phase synchrony was different when the delay was uniform across the edges as compared to the case when it was distributed, i.e., different delays for the edges. When an identical transmission delay was considered, a quasi-periodic pattern was observed in the spike phase synchrony. There were specific values of delay where the phase synchronization reached to its peaks. The behavior of the phase synchronization in the networks with non-uniform delays was different with the former case, where the phase synchrony decreased as distributed delays introduced to the networks.

  4. Experimental observation of chimera and cluster states in a minimal globally coupled network

    NASA Astrophysics Data System (ADS)

    Hart, Joseph D.; Bansal, Kanika; Murphy, Thomas E.; Roy, Rajarshi

    2016-09-01

    A "chimera state" is a dynamical pattern that occurs in a network of coupled identical oscillators when the symmetry of the oscillator population is broken into synchronous and asynchronous parts. We report the experimental observation of chimera and cluster states in a network of four globally coupled chaotic opto-electronic oscillators. This is the minimal network that can support chimera states, and our study provides new insight into the fundamental mechanisms underlying their formation. We use a unified approach to determine the stability of all the observed partially synchronous patterns, highlighting the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization. Our approach is general in terms of network size and connectivity. We also find that chimera states often appear in regions of multistability between global, cluster, and desynchronized states.

  5. Spin-glass phase in a neutral network with asymmetric couplings

    NASA Astrophysics Data System (ADS)

    Kree, R.; Widmaier, D.; Zippelius, A.

    1988-12-01

    The author studies the phase diagram of a neural network model which has learnt with the ADALINE algorithm, starting from tabula non rasa conditions. The resulting synaptic efficacies are not symmetric under an exchange of the pre- and post-synaptic neuron. In contrast to several other models which have been discussed in the literature, he finds a spin-glass phase in the asymmetrically coupled network. The main difference compared with the other models consists of long-ranged Gaussian correlations in the ensemble of couplings.

  6. Calculation of correlation function of a spatially coupled spiking neural network

    NASA Astrophysics Data System (ADS)

    Qiu, Siwei; Chow, Carson

    The dynamics of a large but finite number of coupled spiking neurons is not well understood. We analyze finite size effects in a network of synaptically coupled theta neurons. We show how the system can be characterized by a functional integral from which finite size effects are calculated perturbatively. We discuss the implications of this technique for bump attractors. Thanks to support of the Intramural Research Program of the NIH, NIDDK.

  7. Global exponential synchronization of multiple memristive neural networks with time delay via nonlinear coupling.

    PubMed

    Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

    2015-06-01

    This paper presents theoretical results on the global exponential synchronization of multiple memristive neural networks with time delays. A novel coupling scheme is introduced, in a general topological structure described by a directed or undirected graph, with a linear diffusive term and discontinuous sign term. Several criteria are derived based on the Lyapunov stability theory to ascertain the global exponential stability of synchronization manifold in the coupling scheme. Simulation results for several examples are given to substantiate the effectiveness of the theoretical results.

  8. Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks.

    PubMed

    Hwang, Seong Jae; Adluru, Nagesh; Collins, Maxwell D; Ravi, Sathya N; Bendlin, Barbara B; Johnson, Sterling C; Singh, Vikas

    2016-01-01

    There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points - quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer's disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant's brain connectivity into the future.

  9. Mining the Modular Structure of Protein Interaction Networks

    PubMed Central

    Furlong, Laura Inés; Chernomoretz, Ariel

    2015-01-01

    Background Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. Methodology We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera’s cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. Results As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge. PMID:25856434

  10. GPCR-ModSim: A comprehensive web based solution for modeling G-protein coupled receptors

    PubMed Central

    Esguerra, Mauricio; Siretskiy, Alexey; Bello, Xabier; Sallander, Jessica; Gutiérrez-de-Terán, Hugo

    2016-01-01

    GPCR-ModSim (http://open.gpcr-modsim.org) is a centralized and easy to use service dedicated to the structural modeling of G-protein Coupled Receptors (GPCRs). 3D molecular models can be generated from amino acid sequence by homology-modeling techniques, considering different receptor conformations. GPCR-ModSim includes a membrane insertion and molecular dynamics (MD) equilibration protocol, which can be used to refine the generated model or any GPCR structure uploaded to the server, including if desired non-protein elements such as orthosteric or allosteric ligands, structural waters or ions. We herein revise the main characteristics of GPCR-ModSim and present new functionalities. The templates used for homology modeling have been updated considering the latest structural data, with separate profile structural alignments built for inactive, partially-active and active groups of templates. We have also added the possibility to perform multiple-template homology modeling in a unique and flexible way. Finally, our new MD protocol considers a series of distance restraints derived from a recently identified conserved network of helical contacts, allowing for a smoother refinement of the generated models which is particularly advised when there is low homology to the available templates. GPCR- ModSim has been tested on the GPCR Dock 2013 competition with satisfactory results. PMID:27166369

  11. G-protein-coupled receptor signaling and neural tube closure defects.

    PubMed

    Shimada, Issei S; Mukhopadhyay, Saikat

    2017-01-30

    Disruption of the normal mechanisms that mediate neural tube closure can result in neural tube defects (NTDs) with devastating consequences in affected patients. With the advent of next-generation sequencing, we are increasingly detecting mutations in multiple genes in NTD cases. However, our ability to determine which of these genes contribute to the malformation is limited by our understanding of the pathways controlling neural tube closure. G-protein-coupled receptors (GPCRs) comprise the largest family of transmembrane receptors in humans and have been historically favored as drug targets. Recent studies implicate several GPCRs and downstream signaling pathways in neural tube development and closure. In this review, we will discuss our current understanding of GPCR signaling pathways in pathogenesis of NTDs. Notable examples include the orphan primary cilia-localized GPCR, Gpr161 that regulates the basal suppression machinery of sonic hedgehog pathway by means of activation of cAMP-protein kinase A signaling in the neural tube, and protease-activated receptors that are activated by a local network of membrane-tethered proteases during neural tube closure involving the surface ectoderm. Understanding the role of these GPCR-regulated pathways in neural tube development and closure is essential toward identification of underlying genetic causes to prevent NTDs. Birth Defects Research 109:129-139, 2017. © 2016 Wiley Periodicals, Inc.

  12. Inducing isolated-desynchronization states in complex network of coupled chaotic oscillators

    NASA Astrophysics Data System (ADS)

    Lin, Weijie; Li, Huiyan; Ying, Heping; Wang, Xingang

    2016-12-01

    In a recent study about chaos synchronization in complex networks [Nat. Commun. 5, 4079 (2014), 10.1038/ncomms5079], it is shown that a stable synchronous cluster may coexist with vast asynchronous nodes, resembling the phenomenon of a chimera state observed in a regular network of coupled periodic oscillators. Although of practical significance, this new type of state, namely, the isolated-desynchronization state, is hardly observed in practice due to its strict requirements on the network topology. Here, by the strategy of pinning coupling, we propose an effective method for inducing isolated-desynchronization states in symmetric networks of coupled chaotic oscillators. Theoretical analysis based on eigenvalue analysis shows that, by pinning a group of symmetric nodes in the network, there exists a critical pinning strength beyond which the group of pinned nodes can completely be synchronized while the unpinned nodes remain asynchronous. The feasibility and efficiency of the control method are verified by numerical simulations of both artificial and real-world complex networks with the numerical results in good agreement with the theoretical predictions.

  13. Rapid desynchronization of an electrically coupled interneuron network with sparse excitatory synaptic input.

    PubMed

    Vervaeke, Koen; Lorincz, Andrea; Gleeson, Padraig; Farinella, Matteo; Nusser, Zoltan; Silver, R Angus

    2010-08-12

    Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. To investigate this, we studied electrically coupled Golgi cells (GoC) in the cerebellar input layer. We show with immunohistochemistry, electron microscopy, and electrophysiology that Connexin-36 is necessary for functional gap junctions (GJs) between GoC dendrites. In the absence of coincident synaptic input, GoCs synchronize their firing. In contrast, sparse, coincident mossy fiber input triggered a mixture of excitation and inhibition of GoC firing and spike desynchronization. Inhibition is caused by propagation of the spike afterhyperpolarization through GJs. This triggers network desynchronization because heterogeneous coupling to surrounding cells causes spike-phase dispersion. Detailed network models predict that desynchronization is robust, local, and dependent on synaptic input properties. Our results show that GJ coupling can be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input.

  14. Independent Noise Can Synchronize Interacting Networks of Pulse-Coupled Oscillators

    NASA Astrophysics Data System (ADS)

    Riecke, Hermann; Meng, John

    Structured networks comprised of subnetwork modules are ubiquitous. Motivated by the observation of rhythms and their interaction in different brain areas, we study a network consisting of two subnetworks of pulse-coupled integrate-fire neurons. Through mutual inhibition the neurons in the individual subnetworks can become synchronized and each subnetwork can exhibit coherent oscillatory dynamics, e.g. an ING-rhythm. In the absence of coupling between the networks the rhythms will in general have different frequencies. We investigate the interaction between these different rhythms. Strikingly, we find that increasing the noise level in the input to the individual neurons can synchronize the rhythms of the two networks, even though the inputs to different neurons are uncorrelated, sharing no common component. A heuristic phase model for the coupled networks shows that this synchronization hinges on the fact that only a fraction of the neurons may spike in a given cycle. Thus, the synchronization of the network rhythms differs qualitatively from that of individual oscillators. Supported by NSF-CMMI 1435358.

  15. Exacerbated vulnerability of coupled socio-economic risk in complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Feng, Ling; Berman, Yonatan; Hu, Ning; Stanley, H. Eugene

    2016-10-01

    The study of risk contagion in economic networks has most often focused on the financial liquidities of institutions and assets. In practice the agents in a network affect each other through social contagion, i.e., through herd behavior and the tendency to follow leaders. We study the coupled risk between social and economic contagion and find it significantly more severe than when economic risk is considered alone. Using the empirical network from the China venture capital market we find that the system exhibits an extreme risk of abrupt phase transition and large-scale damage, which is in clear contrast to the smooth phase transition traditionally observed in economic contagion alone. We also find that network structure impacts market resilience and that the randomization of the social network of the market participants can reduce system fragility when there is herd behavior. Our work indicates that under coupled contagion mechanisms network resilience can exhibit a fundamentally different behavior, i.e., an abrupt transition. It also reveals the extreme risk when a system has coupled socio-economic risks, and this could be of interest to both policy makers and market practitioners.

  16. A neural network dynamics that resembles protein evolution

    NASA Astrophysics Data System (ADS)

    Ferrán, Edgardo A.; Ferrara, Pascual

    1992-06-01

    We use neutral networks to classify proteins according to their sequence similarities. A network composed by 7 × 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of cytochrome c proteins belonging to 76 different species. As a result of the training, the network self-organized the activation of its neurons into topologically ordered maps, wherein phylogenetically related sequences were positioned close to each other. The evolution of the topological map during learning, in a representative computational experiment, roughly resembles the way in which one species evolves into several others. For instance, sequences corresponding to vertebrates, initially grouped together into one neuron, were placed in a contiguous zone of the final neural map, with sequences of fishes, amphibia, reptiles, birds and mammals associated to different neurons. Some apparent wrong classifications are due to the fact that some proteins have a greater degree of sequence identity than the one expected by phylogenetics. In the final neural map, each synaptic vector may be considered as the pattern corresponding to the ancestor of all the proteins that are attached to that neuron. Although it may be also tempting to link real time with learning epochs and to use this relationship to calibrate the molecular evolutionary clock, this is not correct because the evolutionary time schedule obtained with the neural network depends highly on the discrete way in which the winner neighborhood is decreased during learning.

  17. Graphics processing unit-based alignment of protein interaction networks.

    PubMed

    Xie, Jiang; Zhou, Zhonghua; Ma, Jin; Xiang, Chaojuan; Nie, Qing; Zhang, Wu

    2015-08-01

    Network alignment is an important bridge to understanding human protein-protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large-scale networks via sequential computing. In this study, the typical Hungarian-Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2-nearest neighbours (HGA-2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA-2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA-2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large-scale networks are considered. By using HGA-2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods.

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

    NASA Astrophysics Data System (ADS)

    Poirot, Olivier; Timsit, Youri

    2016-05-01

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

  19. Optical protein modulation via quantum dot coupling and use of a hybrid sensor protein.

    PubMed

    Griep, Mark; Winder, Eric; Lueking, Donald; Friedrich, Craig; Mallick, Govind; Karna, Shashi

    2010-09-01

    Harnessing the energy transfer interactions between the optical protein bacteriorhodopsin (bR) and CdSe/ZnS quantum dots (QDs) could provide a novel bio-nano electronics substrate with a variety of applications. In the present study, a polydimethyldiallyammonium chloride based I-SAM technique has been utilized to produce bilayers, trilayers and multilayers of alternating monolayers of bR, PDAC and QD's on a conductive ITO substrate. The construction of multilayer systems was directly monitored by measuring the unique A570 nm absorbance of bR, as well as QD fluorescence emission. Both of these parameters displayed a linear relationship to the number of monolayers present on the ITO substrate. The photovoltaic response of bilayers of bR/PDAC was observed over a range of 3 to 12 bilayers and the ability to efficiently create an electrically active multilayered substrate composed of bR and QDs has been demonstrated for the first time. Evaluation of QD fluorescence emission in the multilayer system strongly suggests that FRET coupling is occurring and, since the I-SAM technique provide a means to control the bR/QD separation distance on the nanometer scale, this technique may prove highly valuable for optimizing the distance dependent energy transfer effects for maximum sensitivity to target molecule binding by a biosensor. Finally, preliminary studies on the production of a sensor protein/bR hybrid gene construct are presented. It is proposed that the energy associated with target molecule binding to a hybrid sensor protein would provide a means to directly modulate the electrical output from a sensor protein/bR biosensor platform.

  20. Coupling effect of nodes popularity and similarity on social network persistence

    PubMed Central

    Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

    2017-01-01

    Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes’ popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology. PMID:28220840

  1. Coupling effect of nodes popularity and similarity on social network persistence

    NASA Astrophysics Data System (ADS)

    Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

    2017-02-01

    Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes’ popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology.

  2. The role of field coupling in nano-scale cellular nonlinear networks.

    PubMed

    Porod, Wolfgang; Csaba, Gyorgy; Csurgay, Arpad

    2003-12-01

    We review some of our previous work on field-coupling in nano-scale cellular arrays. Electronic devices based on metallic and magnetic nanoscale dots and molecular structures have been suggested, however, no technologically viable architecture for nanoelectronic circuit integration has emerged so far. A natural architecture on the nanoscale appears to be near-neighbor cellular networking, and we explore promising alternative ways of integrating nanodevices by direct physical field coupling, i.e. either by Coulomb or by magnetic interactions. We review new architectures for such field-coupled nanocircuits.

  3. Modeling synchronization in networks of delay-coupled fiber ring lasers.

    PubMed

    Lindley, Brandon S; Schwartz, Ira B

    2011-11-21

    We study the onset of synchronization in a network of N delay-coupled stochastic fiber ring lasers with respect to various parameters when the coupling power is weak. In particular, for groups of three or more ring lasers mutually coupled to a central hub laser, we demonstrate a robust tendency toward out-of-phase (achronal) synchronization between the N-1 outer lasers and the single inner laser. In contrast to the achronal synchronization, we find the outer lasers synchronize with zero-lag (isochronal) with respect to each other, thus forming a set of N-1 coherent fiber lasers.

  4. Interdependent Networks: Reducing the Coupling Strength Leads to a Change from a First to Second Order Percolation Transition

    NASA Astrophysics Data System (ADS)

    Parshani, Roni; Buldyrev, Sergey V.; Havlin, Shlomo

    2010-07-01

    We study a system composed from two interdependent networks A and B, where a fraction of the nodes in network A depends on nodes of network B and a fraction of the nodes in network B depends on nodes of network A. Because of the coupling between the networks, when nodes in one network fail they cause dependent nodes in the other network to also fail. This invokes an iterative cascade of failures in both networks. When a critical fraction of nodes fail, the iterative process results in a percolation phase transition that completely fragments both networks. We show both analytically and numerically that reducing the coupling between the networks leads to a change from a first order percolation phase transition to a second order percolation transition at a critical point. The scaling of the percolation order parameter near the critical point is characterized by the critical exponent β=1.

  5. Circumventing the problems caused by protein diversity in microarrays: implications for protein interaction networks.

    PubMed

    Gordus, Andrew; MacBeath, Gavin

    2006-10-25

    Protein microarrays provide a well-controlled, high-throughput way to uncover protein-protein interactions. One problem with this and other standardized assays, however, is that proteins vary considerably with respect to their physical properties. If a simple threshold-based approach is used to define protein-protein interactions, the resulting binary networks can be strongly biased. Here, we investigate the extent to which even closely related protein interaction domains vary when printed as microarrays. We find that, when a collection of well behaved, monomeric Src homology 2 (SH2) domains are printed at the same concentration, they vary by up to 50-fold with respect to the resulting surface density of active protein. When a threshold-based binding assay is performed on these domains using fluorescently labeled phosphopeptides, a misleading picture of the underlying biophysical interactions emerges. This problem can be circumvented, however, by obtaining saturation binding curves for each protein-peptide interaction. Importantly, the equilibrium dissociation constants obtained from these curves are independent of the surface density of active protein. We submit that an increased emphasis should be placed on obtaining quantitative information from protein microarrays and that this should serve as a more general goal in all efforts to define large-scale protein interaction networks.

  6. Feedback control design for the complete synchronisation of two coupled Boolean networks

    NASA Astrophysics Data System (ADS)

    Li, Fangfei

    2016-09-01

    In the literatures, to design state feedback controllers to make the response Boolean network synchronise with the drive Boolean network is rarely considered. Motivated by this, feedback control design for the complete synchronisation of two coupled Boolean networks is investigated in this paper. A necessary condition for the existence of a state feedback controller achieving the complete synchronisation is established first. Then, based on the necessary condition, the feedback control law is proposed. Finally, an example is worked out to illustrate the proposed design procedure.

  7. Implementation of pulse-coupled neural networks in a CNAPS environment.

    PubMed

    Kinser, J M; Lindblad, T

    1999-01-01

    Pulse coupled neural networks (PCNN's) are biologically inspired algorithms very well suited for image/signal preprocessing. While several analog implementations are proposed we suggest a digital implementation in an existing environment, the connected network of adapted processors system (CNAPS). The reason for this is two fold. First, CNAPS is a commercially available chip which has been used for several neural-network implementations. Second, the PCNN is, in almost all applications, a very efficient component of a system requiring subsequent and additional processing. This may include gating, Fourier transforms, neural classifiers, data mining, etc, with or without feedback to the PCNN.

  8. Robust autoassociative memory with coupled networks of Kuramoto-type oscillators

    NASA Astrophysics Data System (ADS)

    Heger, Daniel; Krischer, Katharina

    2016-08-01

    Uncertain recognition success, unfavorable scaling of connection complexity, or dependence on complex external input impair the usefulness of current oscillatory neural networks for pattern recognition or restrict technical realizations to small networks. We propose a network architecture of coupled oscillators for pattern recognition which shows none of the mentioned flaws. Furthermore we illustrate the recognition process with simulation results and analyze the dynamics analytically: Possible output patterns are isolated attractors of the system. Additionally, simple criteria for recognition success are derived from a lower bound on the basins of attraction.

  9. Image identification system based on an optical broadcast neural network and a pulse coupled neural network preprocessor stage.

    PubMed

    Lamela, Horacio; Ruiz-Llata, Marta

    2008-04-01

    We describe the concept of a vision system based on an optoelectronic hardware neural processor. The proposed system is composed of a pulse coupled neural network (PCNN) preprocessor stage that converts an input image into a temporal pulsed pattern. These pulses are inputs to the optical broadcast neural network (OBNN) processor, which classifies the input pattern between a set of reference patterns based on a pattern matching strategy. The PCNN is to provide immunity to the scale, rotation, and translation of objects in the image. The OBNN provides high parallelism and a high speed hardware neural processor.

  10. Viral proteins that bridge unconnected proteins and components in the human PPI network.

    PubMed

    Rachita, H R; Nagarajaram, H A

    2014-07-29

    Viruses, despite having small genomes and few proteins, make an array of interactions with host proteins as they solely depend on host machinery for their replication and reproduction. Hence, analysis of the Human-Virus Protein-Protein Interaction Network (Hu-Vir PPI network) helps us to gain certain insights into the molecular mechanisms underlying the hijacking of host cell machinery by viruses for their perpetuation. Here we report an analysis of the Human-Virus Bridged PPI Networks that has led us to identify viral articulation points (VAPs) which connect unconnected components of the Human-PPI (Hu-PPI) network. VAPs cross-link peripheral nodes to the giant component of the Hu-PPI network. VAPs interact with a number of relatively lower topologically central human proteins and are conserved among related viruses. The linked nodes comprise of those that are mostly expressed during viral infection, as well as those that are found exclusively in some metabolic pathways, indicating that the novel viral mediation of certain human protein-protein interactions may form the basis for virus-specific tuning of the host machinery. The functional importance of VAPs and their interaction partners in virus replication make them potential drug targets against viral infection. Our investigations also led to the discovery of an example of a Human Endogenous Retrovirus (HERV) encoded protein, syncytin, as an Articulation Point (AP) in the Hu-PPI network, suggesting that VAPs may be retained in a genome if they result in any beneficial function in the host.

  11. Protein Network of the Pseudomonas aeruginosa Denitrification Apparatus

    PubMed Central

    Borrero-de Acuña, José Manuel; Rohde, Manfred; Wissing, Josef; Jänsch, Lothar; Schobert, Max; Molinari, Gabriella; Timmis, Kenneth N.

    2016-01-01

    ABSTRACT Oxidative phosphorylation using multiple-component, membrane-associated protein complexes is the most effective way for a cell to generate energy. Here, we systematically investigated the multiple protein-protein interactions of the denitrification apparatus of the pathogenic bacterium Pseudomonas aeruginosa. During denitrification, nitrate (Nar), nitrite (Nir), nitric oxide (Nor), and nitrous oxide (Nos) reductases catalyze the reaction cascade of NO3− → NO2− → NO → N2O → N2. Genetic experiments suggested that the nitric oxide reductase NorBC and the regulatory protein NosR are the nucleus of the denitrification protein network. We utilized membrane interactomics in combination with electron microscopy colocalization studies to elucidate the corresponding protein-protein interactions. The integral membrane proteins NorC, NorB, and NosR form the core assembly platform that binds the nitrate reductase NarGHI and the periplasmic nitrite reductase NirS via its maturation factor NirF. The periplasmic nitrous oxide reductase NosZ is linked via NosR. The nitrate transporter NarK2, the nitrate regulatory system NarXL, various nitrite reductase maturation proteins, NirEJMNQ, and the Nos assembly lipoproteins NosFL were also found to be attached. A number of proteins associated with energy generation, including electron-donating dehydrogenases, the complete ATP synthase, almost all enzymes of the tricarboxylic acid (TCA) cycle, and the Sec system of protein transport, among many other proteins, were found to interact with the denitrification proteins. This deduced nitrate respirasome is presumably only one part of an extensive cytoplasmic membrane-anchored protein network connecting cytoplasmic, inner membrane, and periplasmic proteins to mediate key activities occurring at the barrier/interface between the cytoplasm and the external environment. IMPORTANCE The processes of cellular energy generation are catalyzed by large multiprotein enzyme complexes

  12. Human enterovirus 71 protein interaction network prompts antiviral drug repositioning

    PubMed Central

    Han, Lu; Li, Kang; Jin, Chaozhi; Wang, Jian; Li, Qingjun; Zhang, Qiling; Cheng, Qiyue; Yang, Jing; Bo, Xiaochen; Wang, Shengqi

    2017-01-01

    As a predominant cause of human hand, foot, and mouth disease, enterovirus 71 (EV71) infection may lead to serious diseases and result in severe consequences that threaten public health and cause widespread panic. Although the systematic identification of physical interactions between viral proteins and host proteins provides initial information for the recognition of the cellular mechanism involved in viral infection and the development of new therapies, EV71-host protein interactions have not been explored. Here, we identified interactions between EV71 proteins and host cellular proteins and confirmed the functional relationships of EV71-interacting proteins (EIPs) with virus proliferation and infection by integrating a human protein interaction network and by functional annotation. We found that most EIPs had known interactions with other viruses. We also predicted ATP6V0C as a broad-spectrum essential host factor and validated its essentiality for EV71 infection in vitro. EIPs and their interacting proteins were more likely to be targets of anti-inflammatory and neurological drugs, indicating their potential to serve as host-oriented antiviral targets. Thus, we used a connectivity map to find drugs that inhibited EIP expression. We predicted tanespimycin as a candidate and demonstrated its antiviral efficiency in vitro. These findings provide the first systematic identification of EV71-host protein interactions, an analysis of EIP protein characteristics and a demonstration of their value in developing host-oriented antiviral therapies. PMID:28220872

  13. Human enterovirus 71 protein interaction network prompts antiviral drug repositioning.

    PubMed

    Han, Lu; Li, Kang; Jin, Chaozhi; Wang, Jian; Li, Qingjun; Zhang, Qiling; Cheng, Qiyue; Yang, Jing; Bo, Xiaochen; Wang, Shengqi

    2017-02-21

    As a predominant cause of human hand, foot, and mouth disease, enterovirus 71 (EV71) infection may lead to serious diseases and result in severe consequences that threaten public health and cause widespread panic. Although the systematic identification of physical interactions between viral proteins and host proteins provides initial information for the recognition of the cellular mechanism involved in viral infection and the development of new therapies, EV71-host protein interactions have not been explored. Here, we identified interactions between EV71 proteins and host cellular proteins and confirmed the functional relationships of EV71-interacting proteins (EIPs) with virus proliferation and infection by integrating a human protein interaction network and by functional annotation. We found that most EIPs had known interactions with other viruses. We also predicted ATP6V0C as a broad-spectrum essential host factor and validated its essentiality for EV71 infection in vitro. EIPs and their interacting proteins were more likely to be targets of anti-inflammatory and neurological drugs, indicating their potential to serve as host-oriented antiviral targets. Thus, we used a connectivity map to find drugs that inhibited EIP expression. We predicted tanespimycin as a candidate and demonstrated its antiviral efficiency in vitro. These findings provide the first systematic identification of EV71-host protein interactions, an analysis of EIP protein characteristics and a demonstration of their value in developing host-oriented antiviral therapies.

  14. High-throughput identification of protein localization dependency networks.

    PubMed

    Christen, Beat; Fero, Michael J; Hillson, Nathan J; Bowman, Grant; Hong, Sun-Hae; Shapiro, Lucy; McAdams, Harley H

    2010-03-09

    Bacterial cells are highly organized with many protein complexes and DNA loci dynamically positioned to distinct subcellular sites over the course of a cell cycle. Such dynamic protein localization is essential for polar organelle development, establishment of asymmetry, and chromosome replication during the Caulobacter crescentus cell cycle. We used a fluorescence microscopy screen optimized for high-throughput to find strains with anomalous temporal or spatial protein localization patterns in transposon-generated mutant libraries. Automated image acquisition and analysis allowed us to identify genes that affect the localization of two polar cell cycle histidine kinases, PleC and DivJ, and the pole-specific pili protein CpaE, each tagged with a different fluorescent marker in a single strain. Four metrics characterizing the observed localization patterns of each of the three labeled proteins were extracted for hundreds of cell images from each of 854 mapped mutant strains. Using cluster analysis of the resulting set of 12-element vectors for each of these strains, we identified 52 strains with mutations that affected the localization pattern of the three tagged proteins. This information, combined with quantitative localization data from epitasis experiments, also identified all previously known proteins affecting such localization. These studies provide insights into factors affecting the PleC/DivJ localization network and into regulatory links between the localization of the pili assembly protein CpaE and the kinase localization pathway. Our high-throughput screening methodology can be adapted readily to any sequenced bacterial species, opening the potential for databases of localization regulatory networks across species, and investigation of localization network phylogenies.

  15. Graph spectral analysis of protein interaction network evolution.

    PubMed

    Thorne, Thomas; Stumpf, Michael P H

    2012-10-07

    We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a bayesian approach and perform posterior density estimation using an approximate bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more naturally than previously used summary statistics such as the degree distribution. Furthermore, we include the effects of sampling into the analysis, to properly correct for the incompleteness of existing datasets, and have analysed the performance of our method under various degrees of sampling. We consider a number of models focusing not only on the biologically relevant class of duplication models, but also including models of scale-free network growth that have previously been claimed to describe such data. We find a preference for a duplication-divergence with linear preferential attachment model in the majority of the interaction datasets considered. We also illustrate how our method can be used to perform multi-model inference of network parameters to estimate properties of the full network from sampled data.

  16. Self-organized synchronous oscillations in a network of excitable cells coupled by gap junctions.

    PubMed

    Lewis, T J; Rinzel, J

    2000-11-01

    Recent evidence suggests that electrical coupling plays a role in generating oscillatory behaviour in networks of neurons; however, the underlying mechanisms have not been identified. Using a cellular automata model proposed by Traub et al (Traub R D, Schmitz D, Jefferys J G and Draguhn A 1999 High-frequency population oscillations are predicted to occur in hippocampal pyramidal neural networks interconnected by axo-axonal gap junctions Neuroscience 92 407-26), we describe a novel mechanism for self-organized oscillations in networks that have strong, sparse random electrical coupling via gap junctions. The network activity is generated by random spontaneous activity that is moulded into regular population oscillations by the propagation of activity through the network. We explain how this activity gives rise to particular dependences of mean oscillation frequency on network connectivity parameters and on the rate of spontaneous activity, and we derive analytical expressions to approximate the mean frequency and variance of the oscillations. In doing so, we provide insight into possible mechanisms for frequency control and modulation in networks of neurons.

  17. Coupled cluster algorithms for networks of shared memory parallel processors

    NASA Astrophysics Data System (ADS)

    Bentz, Jonathan L.; Olson, Ryan M.; Gordon, Mark S.; Schmidt, Michael W.; Kendall, Ricky A.

    2007-05-01

    As the popularity of using SMP systems as the building blocks for high performance supercomputers increases, so too increases the need for applications that can utilize the multiple levels of parallelism available in clusters of SMPs. This paper presents a dual-layer distributed algorithm, using both shared-memory and distributed-memory techniques to parallelize a very important algorithm (often called the "gold standard") used in computational chemistry, the single and double excitation coupled cluster method with perturbative triples, i.e. CCSD(T). The algorithm is presented within the framework of the GAMESS [M.W. Schmidt, K.K. Baldridge, J.A. Boatz, S.T. Elbert, M.S. Gordon, J.J. Jensen, S. Koseki, N. Matsunaga, K.A. Nguyen, S. Su, T.L. Windus, M. Dupuis, J.A. Montgomery, General atomic and molecular electronic structure system, J. Comput. Chem. 14 (1993) 1347-1363]. (General Atomic and Molecular Electronic Structure System) program suite and the Distributed Data Interface [M.W. Schmidt, G.D. Fletcher, B.M. Bode, M.S. Gordon, The distributed data interface in GAMESS, Comput. Phys. Comm. 128 (2000) 190]. (DDI), however, the essential features of the algorithm (data distribution, load-balancing and communication overhead) can be applied to more general computational problems. Timing and performance data for our dual-level algorithm is presented on several large-scale clusters of SMPs.

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

    PubMed

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

    2008-10-01

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

  19. Estimating interdependences in networks of weakly coupled deterministic systems

    NASA Astrophysics Data System (ADS)

    de Feo, Oscar; Carmeli, Cristian

    2008-02-01

    The extraction of information from measured data about the interactions taking place in a network of systems is a key topic in modern applied sciences. This topic has been traditionally addressed by considering bivariate time series, providing methods which are sometimes difficult to extend to multivariate data, the limiting factor being the computational complexity. Here, we present a computationally viable method based on black-box modeling which, while theoretically applicable only when a deterministic hypothesis about the processes behind the recordings is plausible, proves to work also when this assumption is severely affected. Conceptually, the method is very simple and is composed of three independent steps: in the first step a state-space reconstruction is performed separately on each measured signal; in the second step, a local model, i.e., a nonlinear dynamical system, is fitted separately on each (reconstructed) measured signal; afterward, a linear model of the dynamical interactions is obtained by cross-relating the (reconstructed) measured variables to the dynamics unexplained by the local models. The method is successfully validated on numerically generated data. An assessment of its sensitivity to data length and modeling and measurement noise intensity, and of its applicability to large-scale systems, is also provided.

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

    PubMed Central

    CREIGHTON, MATHEW J; RIOSMENA, FERNANDO

    2013-01-01

    Objectives 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. Methods 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. Results 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. Conclusion 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. PMID:23671352

  1. Modeling Synchronization in Networks of Delay-Coupled Fiber Ring Lasers

    DTIC Science & Technology

    2011-11-21

    Modeling synchronization in networks of delay-coupled fiber ring lasers Brandon S. Lindley and Ira B. Schwartz∗ U.S. Naval Research Laboratory...hub formation. Acknowledgments This work is supported by the Office of Naval Research. Brandon Lindley is currently an NRC Postdoctoral Fellow. The

  2. Expansion of Elderly Couples' IADL Caregiver Networks beyond the Marital Dyad

    ERIC Educational Resources Information Center

    Feld, Sheila; Dunkle, Ruth E.; Schroepfer, Tracy; Shen, Huei-Wern

    2006-01-01

    Factors influencing expansion of instrumental activities of daily living (IADL) caregiver networks beyond the spouse/partner were studied, using data from the Asset and Health Dynamics among the Oldest Old (AHEAD) nationally representative sample of American elders (ages 70 and older). Analyses were based on 427 Black and White couples in which…

  3. Chromatin topology is coupled to Polycomb group protein subnuclear organization

    PubMed Central

    Wani, Ajazul H.; Boettiger, Alistair N.; Schorderet, Patrick; Ergun, Ayla; Münger, Christine; Sadreyev, Ruslan I.; Zhuang, Xiaowei; Kingston, Robert E.; Francis, Nicole J.

    2016-01-01

    The genomes of metazoa are organized at multiple scales. Many proteins that regulate genome architecture, including Polycomb group (PcG) proteins, form subnuclear structures. Deciphering mechanistic links between protein organization and chromatin architecture requires precise description and mechanistic perturbations of both. Using super-resolution microscopy, here we show that PcG proteins are organized into hundreds of nanoscale protein clusters. We manipulated PcG clusters by disrupting the polymerization activity of the sterile alpha motif (SAM) of the PcG protein Polyhomeotic (Ph) or by increasing Ph levels. Ph with mutant SAM disrupts clustering of endogenous PcG complexes and chromatin interactions while elevating Ph level increases cluster number and chromatin interactions. These effects can be captured by molecular simulations based on a previously described chromatin polymer model. Both perturbations also alter gene expression. Organization of PcG proteins into small, abundant clusters on chromatin through Ph SAM polymerization activity may shape genome architecture through chromatin interactions. PMID:26759081

  4. On correlated reaction sets and coupled reaction sets in metabolic networks.

    PubMed

    Marashi, Sayed-Amir; Hosseini, Zhaleh

    2015-08-01

    Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.

  5. Clustered microRNAs' coordination in regulating protein-protein interaction network

    PubMed Central

    Yuan, Xiongying; Liu, Changning; Yang, Pengcheng; He, Shunmin; Liao, Qi; Kang, Shuli; Zhao, Yi

    2009-01-01

    Background MicroRNAs (miRNAs), a growing class of small RNAs with crucial regulatory roles at the post-transcriptional level, are usually found to be clustered on chromosomes. However, with the exception of a few individual cases, so far little is known about the functional consequence of this conserved clustering of miRNA loci. In animal genomes such clusters often contain non-homologous miRNA genes. One hypothesis to explain this heterogeneity suggests that clustered miRNAs are functionally related by virtue of co-targeting downstream pathways. Results Integrating of miRNA cluster information with protein protein interaction (PPI) network data, our research supports the hypothesis of the functional coordination of clustered miRNAs and links it to the topological features of miRNAs' targets in PPI network. Specifically, our results demonstrate that clustered miRNAs jointly regulate proteins in close proximity of the PPI network. The possibility that two proteins yield to this coordinated regulation is negatively correlated with their distance in PPI network. Guided by the knowledge of this preference, we found several network communities enriched with target genes of miRNA clusters. In addition, our results demonstrate that the variance of this propensity can also partly be explained by protein's connectivity and miRNA's conservation. Conclusion In summary, this work supports the hypothesis of intra-cluster coordination and investigates the extent of this coordination. PMID:19558649

  6. Spatiotemporal Dynamics of a Network of Coupled Time-Delay Digital Tanlock Loops

    NASA Astrophysics Data System (ADS)

    Paul, Bishwajit; Banerjee, Tanmoy; Sarkar, B. C.

    The time-delay digital tanlock loop (TDTLs) is an important class of phase-locked loop that is widely used in electronic communication systems. Although nonlinear dynamics of an isolated TDTL has been studied in the past but the collective behavior of TDTLs in a network is an important topic of research and deserves special attention as in practical communication systems separate entities are rarely isolated. In this paper, we carry out the detailed analysis and numerical simulations to explore the spatiotemporal dynamics of a network of a one-dimensional ring of coupled TDTLs with nearest neighbor coupling. The equation representing the network is derived and we carry out analytical calculations using the circulant matrix formalism to obtain the stability criteria. An extensive numerical simulation reveals that with the variation of gain parameter and coupling strength the network shows a variety of spatiotemporal dynamics such as frozen random pattern, pattern selection, spatiotemporal intermittency and fully developed spatiotemporal chaos. We map the distinct dynamical regions of the system in two-parameter space. Finally, we quantify the spatiotemporal dynamics by using quantitative measures like Lyapunov exponent and the average quadratic deviation of the full network.

  7. Cascading failures in coupled networks with both inner-dependency and inter-dependency links

    NASA Astrophysics Data System (ADS)

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-05-01

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.

  8. Cascading failures in coupled networks with both inner-dependency and inter-dependency links

    PubMed Central

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-01-01

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well. PMID:27142883

  9. Cascading failures in coupled networks with both inner-dependency and inter-dependency links.

    PubMed

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-05-04

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.

  10. Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity.

    PubMed

    Ollikainen, Noah; de Jong, René M; Kortemme, Tanja

    2015-01-01

    Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein-ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art "fixed backbone" design methods perform poorly on these tests, we develop a new "coupled moves" design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein-ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.

  11. Order statistics inference for describing topological coupling and mechanical symmetry breaking in multidomain proteins

    NASA Astrophysics Data System (ADS)

    Kononova, Olga; Jones, Lee; Barsegov, V.

    2013-09-01

    Cooperativity is a hallmark of proteins, many of which show a modular architecture comprising discrete structural domains. Detecting and describing dynamic couplings between structural regions is difficult in view of the many-body nature of protein-protein interactions. By utilizing the GPU-based computational acceleration, we carried out simulations of the protein forced unfolding for the dimer WW - WW of the all-β-sheet WW domains used as a model multidomain protein. We found that while the physically non-interacting identical protein domains (WW) show nearly symmetric mechanical properties at low tension, reflected, e.g., in the similarity of their distributions of unfolding times, these properties become distinctly different when tension is increased. Moreover, the uncorrelated unfolding transitions at a low pulling force become increasingly more correlated (dependent) at higher forces. Hence, the applied force not only breaks "the mechanical symmetry" but also couples the physically non-interacting protein domains forming a multi-domain protein. We call this effect "the topological coupling." We developed a new theory, inspired by order statistics, to characterize protein-protein interactions in multi-domain proteins. The method utilizes the squared-Gaussian model, but it can also be used in conjunction with other parametric models for the distribution of unfolding times. The formalism can be taken to the single-molecule experimental lab to probe mechanical cooperativity and domain communication in multi-domain proteins.

  12. Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism.

    PubMed

    Li, Lulu; Ho, Daniel W C; Cao, Jinde; Lu, Jianquan

    2016-04-01

    Cluster synchronization is a typical collective behavior in coupled dynamical systems, where the synchronization occurs within one group, while there is no synchronization among different groups. In this paper, under event-based mechanism, pinning cluster synchronization in an array of coupled neural networks is studied. A new event-triggered sampled-data transmission strategy, where only local and event-triggering states are utilized to update the broadcasting state of each agent, is proposed to realize cluster synchronization of the coupled neural networks. Furthermore, a self-triggered pinning cluster synchronization algorithm is proposed, and a set of iterative procedures is given to compute the event-triggered time instants. Hence, this will reduce the computational load significantly. Finally, an example is given to demonstrate the effectiveness of the theoretical results.

  13. Impact of pore size variability and network coupling on electrokinetic transport in porous media

    NASA Astrophysics Data System (ADS)

    Alizadeh, Shima; Bazant, Martin Z.; Mani, Ali

    2016-11-01

    We have developed and validated an efficient and robust computational model to study the coupled fluid and ion transport through electrokinetic porous media, which are exposed to external gradients of pressure, electric potential, and concentration. In our approach a porous media is modeled as a network of many pores through which the transport is described by the coupled Poisson-Nernst-Planck-Stokes equations. When the pore sizes are random, the interactions between various modes of transport may provoke complexities such as concentration polarization shocks and internal flow circulations. These phenomena impact mixing and transport in various systems including deionization and filtration systems, supercapacitors, and lab-on-a-chip devices. In this work, we present simulations of massive networks of pores and we demonstrate the impact of pore size variation, and pore-pore coupling on the overall electrokinetic transport in porous media.

  14. Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method

    NASA Astrophysics Data System (ADS)

    Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

    2014-08-01

    Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

  15. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

    PubMed

    Li, Hui; Liu, Chunmei

    2014-06-14

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

  16. Stability analysis and synchronization in discrete-time complex networks with delayed coupling.

    PubMed

    Cheng, Ranran; Peng, Mingshu; Yu, Weibin; Sun, Bo; Yu, Jinchen

    2013-12-01

    A new network of coupled maps is proposed in which the connections between units involve no delays but the intra-neural communication does, whereas in the work of Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], the focus is on information processing delayed by the inter-neural communication. We show that the synchronization of the network depends on not only the intrinsic dynamical features and inter-connection topology (characterized by the spectrum of the graph Laplacian) but also the delays and the coupling strength. There are two main findings: (i) the more neighbours, the easier to be synchronized; (ii) odd delays are easier to be synchronized than even ones. In addition, compared with those discussed by Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], our model has a better synchronizability for regular networks and small-world variants.

  17. Monolignol radical-radical coupling networks in western red cedar and Arabidopsis and their evolutionary implications

    NASA Technical Reports Server (NTRS)

    Kim, Myoung K.; Jeon, Jae-Heung; Davin, Laurence B.; Lewis, Norman G.

    2002-01-01

    The discovery of a nine-member multigene dirigent family involved in control of monolignol radical-radical coupling in the ancient gymnosperm, western red cedar, suggested that a complex multidimensional network had evolved to regulate such processes in vascular plants. Accordingly, in this study, the corresponding promoter regions for each dirigent multigene member were obtained by genome-walking, with Arabidopsis being subsequently transformed to express each promoter fused to the beta-glucuronidase (GUS) reporter gene. It was found that each component gene of the proposed network is apparently differentially expressed in individual tissues, organs and cells at all stages of plant growth and development. The data so obtained thus further support the hypothesis that a sophisticated monolignol radical-radical coupling network exists in plants which has been highly conserved throughout vascular plant evolution.

  18. Robust projective outer synchronization of coupled uncertain fractional-order complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Junwei; Zhang, Yun

    2013-06-01

    In this work, we propose a novel projective outer synchronization (POS) between unidirectionally coupled uncertain fractional-order complex networks through scalar transmitted signals. Based on the state observer theory, a control law is designed and some criteria are given in terms of linear matrix inequalities which guarantee global robust POS between such networks. Interestingly, in the POS regime, we show that different choices of scaling factor give rise to different outer synchrony, with various special cases including complete outer synchrony, anti-outer synchrony and even a state of amplitude death. Furthermore, it is demonstrated that although stability of POS is irrelevant to the inner-coupling strength, it will affect the convergence speed of POS. In particular, stronger inner synchronization can induce faster POS. The effectiveness of our method is revealed by numerical simulations on fractional-order complex networks with small-world communication topology.

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

    PubMed

    Li, Wan; Chen, Lina; He, Weiming; Li, Weiguo; Qu, Xiaoli; Liang, Binhua; Gao, Qianping; Feng, Chenchen; Jia, Xu; Lv, Yana; Zhang, Siya; Li, Xia

    2013-01-01

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

  20. Computational approaches for detecting protein complexes from protein interaction networks: a survey

    PubMed Central

    2010-01-01

    Background Most proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions. Results Given the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field. Conclusions Experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better

  1. Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings.

    PubMed

    Sauerwald, Natalie; Zhang, She; Kingsford, Carl; Bahar, Ivet

    2017-03-16

    Understanding the three-dimensional (3D) architecture of chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to study 3D genome organization, but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (>50 Mb) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to model chromatin dynamics using Hi-C data. We show that the GNM can identify spatial couplings at multiple scales: it can quantify the correlated fluctuations in the positions of gene loci, find large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with genome-wide experimental measurements. We use the GNM to identify novel cross-correlated distal domains (CCDDs) representing pairs of regions distinguished by their long-range dynamic coupling and show that CCDDs are associated with increased gene co-expression. Together, these results show that GNM provides a mathematically well-founded unified framework for modeling chromatin dynamics and assessing the structural basis of genome-wide observations.

  2. Unraveling protein interaction networks with near-optimal efficiency.

    PubMed

    Lappe, Michael; Holm, Liisa

    2004-01-01

    The functional characterization of genes and their gene products is the main challenge of the genomic era. Examining interaction information for every gene product is a direct way to assemble the jigsaw puzzle of proteins into a functional map. Here we demonstrate a method in which the information gained from pull-down experiments, in which single proteins act as baits to detect interactions with other proteins, is maximized by using a network-based strategy to select the baits. Because of the scale-free distribution of protein interaction networks, we were able to obtain fast coverage by focusing on highly connected nodes (hubs) first. Unfortunately, locating hubs requires prior global information about the network one is trying to unravel. Here, we present an optimized 'pay-as-you-go' strategy that identifies highly connected nodes using only local information that is collected as successive pull-down experiments are performed. Using this strategy, we estimate that 90% of the human interactome can be covered by 10,000 pull-down experiments, with 50% of the interactions confirmed by reciprocal pull-down experiments.

  3. “Fluctuograms” Reveal the Intermittent Intra-Protein Communication in Subtilisin Carlsberg and Correlate Mechanical Coupling with Co-Evolution

    PubMed Central

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

    2011-01-01

    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. PMID:21455286

  4. Compiled data set of exact NOE distance limits, residual dipolar couplings and scalar couplings for the protein GB3.

    PubMed

    Vögeli, Beat; Olsson, Simon; Riek, Roland; Güntert, Peter

    2015-12-01

    We compiled an NMR data set consisting of exact nuclear Overhauser enhancement (eNOE) distance limits, residual dipolar couplings (RDCs) and scalar (J) couplings for GB3, which forms one of the largest and most diverse data set for structural characterization of a protein to date. All data have small experimental errors, which are carefully estimated. We use the data in the research article Vogeli et al., 2015, Complementarity and congruence between exact NOEs and traditional NMR probes for spatial decoding of protein dynamics, J. Struct. Biol., 191, 3, 306-317, doi:10.1016/j.jsb.2015.07.008 [1] for cross-validation in multiple-state structural ensemble calculation. We advocate this set to be an ideal test case for molecular dynamics simulations and structure calculations.

  5. G Protein-Coupled Receptor Signaling in Stem Cells and Cancer

    PubMed Central

    Lynch, Jennifer R.; Wang, Jenny Yingzi

    2016-01-01

    G protein-coupled receptors (GPCRs) are a large superfamily of cell-surface signaling proteins that bind extracellular ligands and transduce signals into cells via heterotrimeric G proteins. GPCRs are highly tractable drug targets. Aberrant expression of GPCRs and G proteins has been observed in various cancers and their importance in cancer stem cells has begun to be appreciated. We have recently reported essential roles for G protein-coupled receptor 84 (GPR84) and G protein subunit Gαq in the maintenance of cancer stem cells in acute myeloid leukemia. This review will discuss how GPCRs and G proteins regulate stem cells with a focus on cancer stem cells, as well as their implications for the development of novel targeted cancer therapies. PMID:27187360

  6. G Protein-Coupled Receptor Signaling in Stem Cells and Cancer.

    PubMed

    Lynch, Jennifer R; Wang, Jenny Yingzi

    2016-05-11

    G protein-coupled receptors (GPCRs) are a large superfamily of cell-surface signaling proteins that bind extracellular ligands and transduce signals into cells via heterotrimeric G proteins. GPCRs are highly tractable drug targets. Aberrant expression of GPCRs and G proteins has been observed in various cancers and their importance in cancer stem cells has begun to be appreciated. We have recently reported essential roles for G protein-coupled receptor 84 (GPR84) and G protein subunit Gαq in the maintenance of cancer stem cells in acute myeloid leukemia. This review will discuss how GPCRs and G proteins regulate stem cells with a focus on cancer stem cells, as well as their implications for the development of novel targeted cancer therapies.

  7. Structural basis of GDP release and gating in G protein coupled Fe2+ transport

    PubMed Central

    Guilfoyle, Amy; Maher, Megan J; Rapp, Mikaela; Clarke, Ronald; Harrop, Stephen; Jormakka, Mika

    2009-01-01

    G proteins are key molecular switches in the regulation of membrane protein function and signal transduction. The prokaryotic membrane protein FeoB is involved in G protein coupled Fe2+ transport, and is unique in that the G protein is directly tethered to the membrane domain. Here, we report the structure of the soluble domain of FeoB, including the G protein domain, and its assembly into an unexpected trimer. Comparisons between nucleotide free and liganded structures reveal the closed and open state of a central cytoplasmic pore, respectively. In addition, these data provide the first observation of a conformational switch in the nucleotide-binding G5 motif, defining the structural basis for GDP release. From these results, structural parallels are drawn to eukaryotic G protein coupled membrane processes. PMID:19629046

  8. ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis.

    PubMed

    Veres, Daniel V; Gyurkó, Dávid M; Thaler, Benedek; Szalay, Kristóf Z; Fazekas, Dávid; Korcsmáros, Tamás; Csermely, Peter

    2015-01-01

    Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein-protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein-protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein-protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.

  9. Precision of pulse-coupled networks of integrate-and-fire neurons.

    PubMed

    Tiesinga, P H; Sejnowski, T J

    2001-05-01

    Some sensory tasks in the nervous system require highly precise spike trains to be generated in the presence of intrinsic neuronal noise. Collective enhancement of precision (CEP) can occur when spike trains of many neurons are pooled together into a more precise population discharge. We study CEP in a network of N model neurons connected by recurrent excitation. Each neuron is driven by a periodic inhibitory spike train with independent jitter in the spike arrival time. The network discharge is characterized by sigmaW, the dispersion in the spike times within one cycle, and sigmaB, the jitter in the network-averaged spike time between cycles. In an uncoupled network sigmaB approximately = 1/square root(N) and sigmaW is independent of N. In a strongly coupled network sigmaB approximately = 1/square root(log N) and sigmaW is close to zero. At intermediate coupling strengths, sigmaW is reduced, while sigmaB remains close to its uncoupled value. The population discharge then has optimal biophysical properties compared with the uncoupled network.

  10. Cellular pulse-coupled neural network with adaptive weights for image segmentation and its VLSI implementation

    NASA Astrophysics Data System (ADS)

    Schreiter, Juerg; Ramacher, Ulrich; Heittmann, Arne; Matolin, Daniel; Schuffny, Rene

    2004-05-01

    We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights. Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns. This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant differences in the synchronization properties between networks using the ideal adaptation rules and networks resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It comprises a 128 × 128 neuron array, analog image memory, and an address event representation pulse output.

  11. Robust synchronization of complex networks with uncertain couplings and incomplete information

    NASA Astrophysics Data System (ADS)

    Wang, Fan; Liang, Jinling; Wang, Zidong; Alsaadi, Fuad E.

    2016-07-01

    The mean square exponential (MSE) synchronization problem is investigated in this paper for complex networks with simultaneous presence of uncertain couplings and incomplete information, which comprise both the randomly occurring delay and the randomly occurring non-linearities. The network considered is uncertain with time-varying stochastic couplings. The randomly occurring delay and non-linearities are modelled by two Bernoulli-distributed white sequences with known probabilities to better describe realistic complex networks. By utilizing the coordinate transformation, the addressed complex network can be exponentially synchronized in the mean square if the MSE stability of a transformed subsystem can be assured. The stability problem is studied firstly for the transformed subsystem based on the Lyapunov functional method. Then, an easy-to-verify sufficient criterion is established by further decomposing the transformed system, which embodies the joint impacts of the single-node dynamics, the network topology and the statistical quantities of the uncertainties on the synchronization of the complex network. Numerical examples are exploited to illustrate the effectiveness of the proposed methods.

  12. Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses?

    NASA Astrophysics Data System (ADS)

    Rings, Thorsten; Lehnertz, Klaus

    2016-09-01

    We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain networks using intracranial electroencephalographic recordings from an epilepsy patient. For such networks, both direct and indirect directional couplings can be expected, given the brain's connection structure and effects that may arise from limitations inherent to the recording technique. Our findings indicate that particularly in larger networks (number of nodes ≫10 ), the partialized approach does not provide information about directional couplings extending the information gained with the evolution map approach.

  13. Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses?

    PubMed

    Rings, Thorsten; Lehnertz, Klaus

    2016-09-01

    We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain networks using intracranial electroencephalographic recordings from an epilepsy patient. For such networks, both direct and indirect directional couplings can be expected, given the brain's connection structure and effects that may arise from limitations inherent to the recording technique. Our findings indicate that particularly in larger networks (number of nodes ≫10), the partialized approach does not provide information about directional couplings extending the information gained with the evolution map approach.

  14. Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

    PubMed Central

    Ollikainen, Noah; de Jong, René M.; Kortemme, Tanja

    2015-01-01

    Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution. PMID:26397464

  15. Optimal weighting scheme and the role of coupling strength against load failures in degree-based weighted interdependent networks

    NASA Astrophysics Data System (ADS)

    Qiu, Yuzhuo

    2013-04-01

    The optimal weighting scheme and the role of coupling strength against load failures on symmetrically and asymmetrically coupled interdependent networks were investigated. The degree-based weighting scheme was extended to interdependent networks, with the flow dynamics dominated by global redistribution based on weighted betweenness centrality. Through contingency analysis of one-node removal, we demonstrated that there still exists an optimal weighting parameter on interdependent networks, but it might shift as compared to the case in isolated networks because of the break of symmetry. And it will be easier for the symmetrically and asymmetrically coupled interdependent networks to achieve robustness and better cost configuration against the one-node-removal-induced cascade of load failures when coupling strength was weaker. Our findings might have great generality for characterizing load-failure-induced cascading dynamics in real-world degree-based weighted interdependent networks.

  16. Robust Estimation for Neural Networks With Randomly Occurring Distributed Delays and Markovian Jump Coupling.

    PubMed

    Xu, Yong; Lu, Renquan; Shi, Peng; Tao, Jie; Xie, Shengli

    2017-01-24

    This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the l₂-l∞ performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.

  17. t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks.

    PubMed

    Zhu, Lin; You, Zhu-Hong; Huang, De-Shuang; Wang, Bing

    2013-01-01

    Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.

  18. Neural network definitions of highly predictable protein secondary structure classes

    SciTech Connect

    Lapedes, A. |; Steeg, E.; Farber, R.

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  19. Prediction and Annotation of Plant Protein Interaction Networks

    SciTech Connect

    McDermott, Jason E.; Wang, Jun; Yu, Jun; Wong, Gane Ka-Shu; Samudrala, Ram

    2009-02-01

    Large-scale experimental studies of interactions between components of biological systems have been performed for a variety of eukaryotic organisms. However, there is a dearth of such data for plants. Computational methods for prediction of relationships between proteins, primarily based on comparative genomics, provide a useful systems-level view of cellular functioning and can be used to extend information about other eukaryotes to plants. We have predicted networks for Arabidopsis thaliana, Oryza sativa indica and japonica and several plant pathogens using the Bioverse (http://bioverse.compbio.washington.edu) and show that they are similar to experimentally-derived interaction networks. Predicted interaction networks for plants can be used to provide novel functional annotations and predictions about plant phenotypes and aid in rational engineering of biosynthesis pathways.

  20. High content screening for G protein-coupled receptors using cell-based protein translocation assays.

    PubMed

    Grånäs, Charlotta; Lundholt, Betina Kerstin; Heydorn, Arne; Linde, Viggo; Pedersen, Hans-Christian; Krog-Jensen, Christian; Rosenkilde, Mette M; Pagliaro, Len

    2005-06-01

    G protein-coupled receptors (GPCRs) have been one of the most productive classes of drug targets for several decades, and new technologies for GPCR-based discovery promise to keep this field active for years to come. While molecular screens for GPCR receptor agonist- and antagonist-based drugs will continue to be valuable discovery tools, the most exciting developments in the field involve cell-based assays for GPCR function. Some cell-based discovery strategies, such as the use of beta-arrestin as a surrogate marker for GPCR function, have already been reduced to practice, and have been used as valuable discovery tools for several years. The application of high content cell-based screening to GPCR discovery has opened up additional possibilities, such as direct tracking of GPCRs, G proteins and other signaling pathway components using intracellular translocation assays. These assays provide the capability to probe GPCR function at the cellular level with better resolution than has previously been possible, and offer practical strategies for more definitive selectivity evaluation and counter-screening in the early stages of drug discovery. The potential of cell-based translocation assays for GPCR discovery is described, and proof-of-concept data from a pilot screen with a CXCR4 assay are presented. This chemokine receptor is a highly relevant drug target which plays an important role in the pathogenesis of inflammatory disease and also has been shown to be a co-receptor for entry of HIV into cells as well as to play a role in metastasis of certain cancer cells.

  1. Protein Unfolding Coupled to Ligand Binding: Differential Scanning Calorimetry Simulation Approach

    ERIC Educational Resources Information Center

    Celej, Maria Soledad; Fidelio, Gerardo Daniel; Dassie, Sergio Alberto

    2005-01-01

    A comprehensive theoretical description of thermal protein unfolding coupled to ligand binding is presented. The thermodynamic concepts are independent of the method used to monitor protein unfolding but a differential scanning calorimetry is being used as a tool for examining the unfolding process.

  2. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis.

    PubMed

    Saha, Sovan; Chatterjee, Piyali; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita

    2014-12-01

    Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at http://code.google.com/p/cmaterbioinfo/ .

  3. Synchronisation in networks of delay-coupled type-I excitable systems

    NASA Astrophysics Data System (ADS)

    Keane, A.; Dahms, T.; Lehnert, J.; Suryanarayana, S. A.; Hövel, P.; Schöll, E.

    2012-12-01

    We use a generic model for type-I excitability (known as the SNIPER or SNIC model) to describe the local dynamics of nodes within a network in the presence of non-zero coupling delays. Utilising the method of the Master Stability Function, we investigate the stability of the zero-lag synchronised dynamics of the network nodes and its dependence on the two coupling parameters, namely the coupling strength and delay time. Unlike in the FitzHugh-Nagumo model (a model for type-II excitability), there are parameter ranges where the stability of synchronisation depends on the coupling strength and delay time. One important implication of these results is that there exist complex networks for which the adding of inhibitory links in a small-world fashion may not only lead to a loss of stable synchronisation, but may also restabilise synchronisation or introduce multiple transitions between synchronisation and desynchronisation. To underline the scope of our results, we show using the Stuart-Landau model that such multiple transitions do not only occur in excitable systems, but also in oscillatory ones.

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

    NASA Astrophysics Data System (ADS)

    Alfinito, Eleonora; Pennetta, Cecilia; Reggiani, Lino

    2008-02-01

    Integrated nanodevices based on proteins or biomolecules are attracting increasing interest in today's research. In fact, it has been shown that proteins such as azurin and bacteriorhodopsin manifest some electrical properties that are promising for the development of active components of molecular electronic devices. Here we focus on two relevant kinds of protein: bovine rhodopsin, prototype of G-protein-coupled-receptor (GPCR) proteins, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer's disease. Both these proteins exert their function starting with a conformational change of their native structure. Our guess is that such a change should be accompanied with a detectable variation of their electrical properties. To investigate this conjecture, we present an impedance network model of <