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Sample records for signaling protein networking

  1. Protein and signaling networks in vertebrate photoreceptor cells

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    Karl-Wilhelm eKoch

    2015-11-01

    Full Text Available Vertebrate photoreceptor cells are exquisite light detectors operating under very dim and bright illumination. The photoexcitation and adaptation machinery in photoreceptor cells consists of protein complexes that can form highly ordered supramolecular structures and control the homeostasis and mutual dependence of the secondary messengers cGMP and Ca2+. The visual pigment in rod photoreceptors, the G protein-coupled receptor rhodopsin is organized in tracks of dimers thereby providing a signaling platform for the dynamic scaffolding of the G protein transducin. Illuminated rhodopsin is turned off by phosphorylation catalyzed by rhodopsin kinase GRK1 under control of Ca2+-recoverin. The GRK1 protein complex partly assembles in lipid raft structures, where shutting off rhodopsin seems to be more effective. Re-synthesis of cGMP is another crucial step in the recovery of the photoresponse after illumination. It is catalyzed by membrane bound sensory guanylate cyclases and is regulated by specific neuronal Ca2+-sensor proteins called GCAPs. At least one guanylate cyclase (ROS-GC1 was shown to be part of a multiprotein complex having strong interactions with the cytoskeleton and being controlled in a multimodal Ca2+-dependent fashion. The final target of the cGMP signaling cascade is a cyclic nucleotide-gated channel that is a hetero-oligomeric protein located in the plasma membrane and interacting with accessory proteins in highly organized microdomains. We summarize results and interpretations of findings related to the inhomogeneous organization of signaling units in photoreceptor outer segments.

  2. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

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    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Dynamic protein interaction networks and new structural paradigms in signaling

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    Csizmok, Veronika; Follis, Ariele Viacava; Kriwacki, Richard W.; Forman-Kay, Julie D.

    2017-01-01

    Understanding signaling and other complex biological processes requires elucidating the critical roles of intrinsically disordered proteins and regions (IDPs/IDRs), which represent ~30% of the proteome and enable unique regulatory mechanisms. In this review we describe the structural heterogeneity of disordered proteins that underpins these mechanisms and the latest progress in obtaining structural descriptions of ensembles of disordered proteins that are needed for linking structure and dynamics to function. We describe the diverse interactions of IDPs that can have unusual characteristics such as “ultrasensitivity” and “regulated folding and unfolding”. We also summarize the mounting data showing that large-scale assembly and protein phase separation occurs within a variety of signaling complexes and cellular structures. In addition, we discuss efforts to therapeutically target disordered proteins with small molecules. Overall, we interpret the remodeling of disordered state ensembles due to binding and post-translational modifications within an expanded framework for allostery that provides significant insights into how disordered proteins transmit biological information. PMID:26922996

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

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    Sylvie Lalonde

    2010-09-01

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

  5. Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

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    Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

    2015-01-01

    As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

  6. Discovery of intramolecular signal transduction network based on a new protein dynamics model of energy dissipation.

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    Cheng-Wei Ma

    Full Text Available A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.

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

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

    2015-01-01

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

  8. Signal-transduction networks and the regulation of muscle protein degradation.

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    Szewczyk, Nathaniel J; Jacobson, Lewis A

    2005-10-01

    Protein degradation in muscle functions in maintaining normal physiological homeostasis and adapting to new homeostatic states, and is required for muscle wasting or atrophy in various pathological states. The interplay between protein synthesis and degradation to maintain homeostasis is complex and responds to a variety of autocrine and intercellular signals from neuronal inputs, hormones, cytokines, growth factors and other regulatory molecules. The intracellular events that connect extracellular signals to the molecular control of protein degradation are incompletely understood, but likely involve interacting signal-transduction networks rather than isolated pathways. We review some examples of signal-transduction systems that regulate protein degradation, including effectors of proteolysis inducing factor (PIF), insulin and insulin-like growth factor (IGF) and their receptors, and fibroblast growth factor (FGF) and its receptors.

  9. Perturbation waves in proteins and protein networks: Applications of percolation and game theories in signaling and drug design

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    Antal, Miklos A; Csermely, Peter

    2008-01-01

    The network paradigm is increasingly used to describe the dynamics of complex systems. Here we review the current results and propose future development areas in the assessment of perturbation waves, i.e. propagating structural changes in amino acid networks building individual protein molecules and in protein-protein interaction networks (interactomes). We assess the possibilities and critically review the initial attempts for the application of game theory to the often rather complicated process, when two protein molecules approach each other, mutually adjust their conformations via multiple communication steps and finally, bind to each other. We also summarize available data on the application of percolation theory for the prediction of amino acid network- and interactome-dynamics. Furthermore, we give an overview of the dissection of signals and noise in the cellular context of various perturbations. Finally, we propose possible applications of the reviewed methodologies in drug design.

  10. Mammalian Hippo signalling: a kinase network regulated by protein-protein interactions

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    Hergovich, Alexander

    2012-01-01

    The Hippo signal transduction cascade controls cell growth, proliferation, and death, all of which are frequently deregulated in tumour cells. Since initial studies in Drosophila melanogaster were instrumental in defining Hippo signalling, the machinery was named after the central Ste20-like kinase Hippo. Moreover, given that loss of Hippo signalling components Hippo, Warts, and Mats resulted in uncontrolled tissue overgrowth, Hippo signalling was defined as a tumour suppressor cascade. Significantly, all core factors of Hippo signalling have mammalian orthologues that functionally compensate for loss of their counterparts in flies. Furthermore, studies in flies and mammalian cell systems showed that Hippo signalling represents a kinase cascade that is tightly regulated by protein-protein interactions (PPIs). Several Hippo signalling molecules contain SARAH domains that mediate specific PPIs, thereby influencing the activities of MST1/2 kinases, the human Hippo orthologues. Moreover, WW domains are present in several Hippo factors, and these domains also serve as interaction surfaces for regulatory PPIs in Hippo signalling. Finally, the kinase activities of LATS1/2, the human counterparts of Warts, are controlled by binding to hMOB1, the human Mats. Therefore, Hippo signalling is regulated by PPIs on several levels. Here we review our current understanding of how these regulatory PPIs are regulated and contribute to the functionality of Hippo signalling. PMID:22260677

  11. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks

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    Kirouac Daniel C

    2012-05-01

    Full Text Available Abstract Background Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID, PANTHER, Reactome, I2D, and STRING. We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data. Results We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless, we find a multiplicity of network topologies in which receptors couple to downstream

  12. Signaling by Small GTPases at Cell-Cell junctions: Protein Interactions Building Control and Networks.

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    Braga, Vania

    2017-09-11

    A number of interesting reports highlight the intricate network of signaling proteins that coordinate formation and maintenance of cell-cell contacts. We have much yet to learn about how the in vitro binding data is translated into protein association inside the cells and whether such interaction modulates the signaling properties of the protein. What emerges from recent studies is the importance to carefully consider small GTPase activation in the context of where its activation occurs, which upstream regulators are involved in the activation/inactivation cycle and the GTPase interacting partners that determine the intracellular niche and extent of signaling. Data discussed here unravel unparalleled cooperation and coordination of functions among GTPases and their regulators in supporting strong adhesion between cells. Copyright © 2017 Cold Spring Harbor Laboratory Press; all rights reserved.

  13. Landscape mapping of functional proteins in insulin signal transduction and insulin resistance: a network-based protein-protein interaction analysis.

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    Chiranjib Chakraborty

    Full Text Available The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins.

  14. From pathways to networks: connecting dots by establishing protein-protein interaction networks in signaling pathways using affinity purification and mass spectrometry.

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    Li, Xu; Wang, Wenqi; Chen, Junjie

    2015-01-01

    Signal transductions are the basis of biological activities in all living organisms. Studying the signaling pathways, especially under physiological conditions, has become one of the most important facets of modern biological research. During the last decade, MS has been used extensively in biological research and is proven to be effective in addressing important biological questions. Here, we review the current progress in the understanding of signaling networks using MS approaches. We will focus on studies of protein-protein interactions that use affinity purification followed by MS approach. We discuss obstacles to affinity purification, data processing, functional validation, and identification of transient interactions and provide potential solutions for pathway-specific proteomics analysis, which we hope one day will lead to a comprehensive understanding of signaling networks in humans. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Exploring G protein-coupled receptor signaling networks using SILAC-based phosphoproteomics.

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    Williams, Grace R; Bethard, Jennifer R; Berkaw, Mary N; Nagel, Alexis K; Luttrell, Louis M; Ball, Lauren E

    2016-01-01

    The type 1 parathyroid hormone receptor (PTH1R) is a key regulator of calcium homeostasis and bone turnover. Here, we employed SILAC-based quantitative mass spectrometry and bioinformatic pathways analysis to examine global changes in protein phosphorylation following short-term stimulation of endogenously expressed PTH1R in osteoblastic cells in vitro. Following 5min exposure to the conventional agonist, PTH(1-34), we detected significant changes in the phosphorylation of 224 distinct proteins. Kinase substrate motif enrichment demonstrated that consensus motifs for PKA and CAMK2 were the most heavily upregulated within the phosphoproteome, while consensus motifs for mitogen-activated protein kinases were strongly downregulated. Signaling pathways analysis identified ERK1/2 and AKT as important nodal kinases in the downstream network and revealed strong regulation of small GTPases involved in cytoskeletal rearrangement, cell motility, and focal adhesion complex signaling. Our data illustrate the utility of quantitative mass spectrometry in measuring dynamic changes in protein phosphorylation following GPCR activation. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Mechanosensitive molecular networks involved in transducing resistance exercise-signals into muscle protein accretion

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    Emil Rindom

    2016-11-01

    Full Text Available Loss of skeletal muscle myofibrillar protein with disease and/or inactivity can severely deteriorate muscle strength and function. Strategies to counteract wasting of muscle myofibrillar protein are therefore desirable and invite for considerations on the potential superiority of specific modes of resistance exercise and/or the adequacy of low load resistance exercise regimens as well as underlying mechanisms. In this regard, delineation of the potentially mechanosensitive molecular mechanisms underlying muscle protein synthesis (MPS, may contribute to understanding on how differentiated resistance exercise can transduce a mechanical signal into stimulation of muscle accretion. Recent findings suggest specific upstream exercise-induced mechano-sensitive myocellular signaling pathways to converge on mammalian target of rapamycin complex 1 (mTORC1, to influence MPS. This may e.g. implicate mechanical activation of signaling through a diacylglycerol kinase (DGKζ-phosphatidic acid (PA axis or implicate integrin deformation to signal through a Focal adhesion kinase (FAK-Tuberous Sclerosis Complex 2TSC2-Ras homolog enriched in brain (Rheb axis. Moreover, since initiation of translation is reliant on mRNA, it is also relevant to consider potentially mechanosensitive signaling pathways involved in muscle myofibrillar gene transcription and whether some of these pathways converge with those affecting mTORC1 activation for MPS. In this regard, recent findings suggest how mechanical stress may implicate integrin deformation and/or actin dynamics to signal through a Ras homolog gene family member A protein (RhoA-striated muscle activator of Rho signaling (STARS axis or how it may implicate deformation of Notch to affect Bone Morphogenetic Protein (BMP signaling through a small mother of decapentaplegic (Smad axis.

  17. Mechanosensitive Molecular Networks Involved in Transducing Resistance Exercise-Signals into Muscle Protein Accretion.

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    Rindom, Emil; Vissing, Kristian

    2016-01-01

    Loss of skeletal muscle myofibrillar protein with disease and/or inactivity can severely deteriorate muscle strength and function. Strategies to counteract wasting of muscle myofibrillar protein are therefore desirable and invite for considerations on the potential superiority of specific modes of resistance exercise and/or the adequacy of low load resistance exercise regimens as well as underlying mechanisms. In this regard, delineation of the potentially mechanosensitive molecular mechanisms underlying muscle protein synthesis (MPS), may contribute to an understanding on how differentiated resistance exercise can transduce a mechanical signal into stimulation of muscle accretion. Recent findings suggest specific upstream exercise-induced mechano-sensitive myocellular signaling pathways to converge on mammalian target of rapamycin complex 1 (mTORC1), to influence MPS. This may e.g. implicate mechanical activation of signaling through a diacylglycerol kinase (DGKζ)-phosphatidic acid (PA) axis or implicate integrin deformation to signal through a Focal adhesion kinase (FAK)-Tuberous Sclerosis Complex 2 (TSC2)-Ras homolog enriched in brain (Rheb) axis. Moreover, since initiation of translation is reliant on mRNA, it is also relevant to consider potentially mechanosensitive signaling pathways involved in muscle myofibrillar gene transcription and whether some of these pathways converge with those affecting mTORC1 activation for MPS. In this regard, recent findings suggest how mechanical stress may implicate integrin deformation and/or actin dynamics to signal through a Ras homolog gene family member A protein (RhoA)-striated muscle activator of Rho signaling (STARS) axis or implicate deformation of Notch to affect Bone Morphogenetic Protein (BMP) signaling through a small mother of decapentaplegic (Smad) axis.

  18. Interactome of signaling networks in wheat: the protein-protein interaction between TaRAR1 and TaSGT1.

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    Tai, Yin-Shan

    2008-09-01

    RAR1 and SGT1 are required for development and disease resistance in plants. In many cases, RAR1 and SGT1 regulate the resistance (R)-gene-mediated defense signaling pathways. Lr21 is the first identified NBS-LRR-type R protein in wheat and is required for resistance to the leaf rust pathogen. The Lr21-mediated signaling pathways require the wheat homologs of RAR1, SGT1, and HSP90. However, the molecular mechanisms of the Lr21-mediated signaling networks remain unknown. Here I present the DNA and protein sequences of TaRAR1 and TaSGT1, and demonstrate for the first time a direct protein-protein interaction between them.

  19. Regulation and function of ribosomal protein S6 kinase (S6K) within mTOR signalling networks.

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    Magnuson, Brian; Ekim, Bilgen; Fingar, Diane C

    2012-01-01

    The ribosomal protein S6K (S6 kinase) represents an extensively studied effector of the TORC1 [TOR (target of rapamycin) complex 1], which possesses important yet incompletely defined roles in cellular and organismal physiology. TORC1 functions as an environmental sensor by integrating signals derived from diverse environmental cues to promote anabolic and inhibit catabolic cellular functions. mTORC1 (mammalian TORC1) phosphorylates and activates S6K1 and S6K2, whose first identified substrate was rpS6 (ribosomal protein S6), a component of the 40S ribosome. Studies over the past decade have uncovered a number of additional S6K1 substrates, revealing multiple levels at which the mTORC1-S6K1 axis regulates cell physiology. The results thus far indicate that the mTORC1-S6K1 axis controls fundamental cellular processes, including transcription, translation, protein and lipid synthesis, cell growth/size and cell metabolism. In the present review we summarize the regulation of S6Ks, their cellular substrates and functions, and their integration within rapidly expanding mTOR (mammalian TOR) signalling networks. Although our understanding of the role of mTORC1-S6K1 signalling in physiology remains in its infancy, evidence indicates that this signalling axis controls, at least in part, glucose homoeostasis, insulin sensitivity, adipocyte metabolism, body mass and energy balance, tissue and organ size, learning, memory and aging. As dysregulation of this signalling axis contributes to diverse disease states, improved understanding of S6K regulation and function within mTOR signalling networks may enable the development of novel therapeutics.

  20. Critical analysis of protein signaling networks involved in the regulation of plant secondary metabolism: focus on anthocyanins.

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    Bulgakov, Victor P; Avramenko, Tatiana V; Tsitsiashvili, Gurami Sh

    2017-09-01

    Anthocyanin biosynthesis in Arabidopsis is a convenient and relatively simple model for investigating the basic principles of secondary metabolism regulation. In recent years, many publications have described links between anthocyanin biosynthesis and general defense reactions in plants as well as photomorphogenesis and hormonal signaling. These relationships are complex, and they cannot be understood intuitively. Upon observing the lacuna in the Arabidopsis interactome (an interaction map of the factors involved in the regulation of Arabidopsis secondary metabolism is not available), we attempted to connect various cellular processes that affect anthocyanin biosynthesis. In this review, we revealed the main signaling protein modules that regulate anthocyanin biosynthesis. To our knowledge, this is the first reconstruction of a network of proteins involved in plant secondary metabolism.

  1. Simulated evolution of signal transduction networks.

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    Mohammad Mobashir

    Full Text Available Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually transduce the signal to the nucleus. The main objective of our work is to develop a theoretical approach which will help to better understand the behavior of signal transduction networks due to changes in kinetic parameters and network topology. By using an evolutionary algorithm, we designed a mathematical model which performs basic signaling tasks similar to the signaling process of living cells. We use a simple dynamical model of signaling networks of interacting proteins and their complexes. We study the evolution of signaling networks described by mass-action kinetics. The fitness of the networks is determined by the number of signals detected out of a series of signals with varying strength. The mutations include changes in the reaction rate and network topology. We found that stronger interactions and addition of new nodes lead to improved evolved responses. The strength of the signal does not play any role in determining the response type. This model will help to understand the dynamic behavior of the proteins involved in signaling pathways. It will also help to understand the robustness of the kinetics of the output response upon changes in the rate of reactions and the topology of the network.

  2. A protein–protein interaction network linking the energy-sensor kinase SnRK1 to multiple signaling pathways in Arabidopsis thaliana

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    Madlen Nietzsche

    2016-04-01

    Full Text Available In plants, the sucrose non-fermenting (SNF1-related protein kinase 1 (SnRK1 represents a central integrator of low energy signaling and acclimation towards many environmental stress responses. Although SnRK1 acts as a convergent point for many different environmental and metabolic signals to control growth and development, it is currently unknown how these many different signals could be translated into a cell-type or stimulus specific response since many components of SnRK1-regulated signaling pathways remain unidentified. Recently, we have demonstrated that proteins containing a domain of unknown function (DUF 581 interact with the catalytic α subunits of SnRK1 (AKIN10/11 from Arabidopsis thaliana and could potentially act as mediators conferring tissue- and stimulus-type specific differences in SnRK1 regulation. To further extend the SnRK1 signaling network in plants, we systematically screened for novel DUF581 interaction partners using the yeast two-hybrid system. A deep and exhaustive screening identified 17 interacting partners for 10 of the DUF581 proteins tested. Many of these novel interaction partners are implicated in cellular processes previously associated with SnRK1 signaling. Furthermore, we mined publicly available interaction data to identify additional DUF581 interacting proteins. A protein–protein interaction network resulting from our studies suggests connections between SnRK1 signaling and other central signaling pathways involved in growth regulation and environmental responses. These include TOR and MAP-kinase signaling as well as hormonal pathways. The resulting protein–protein interaction network promises to be effective in generating hypotheses to study the precise mechanisms SnRK1 signaling on a functional level.

  3. Proteomic analysis of proteins surrounding occludin and claudin-4 reveals their proximity to signaling and trafficking networks.

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    Karin Fredriksson

    Full Text Available Tight junctions are complex membrane structures that regulate paracellular movement of material across epithelia and play a role in cell polarity, signaling and cytoskeletal organization. In order to expand knowledge of the tight junction proteome, we used biotin ligase (BioID fused to occludin and claudin-4 to biotinylate their proximal proteins in cultured MDCK II epithelial cells. We then purified the biotinylated proteins on streptavidin resin and identified them by mass spectrometry. Proteins were ranked by relative abundance of recovery by mass spectrometry, placed in functional categories, and compared not only among the N- and C- termini of occludin and the N-terminus of claudin-4, but also with our published inventory of proteins proximal to the adherens junction protein E-cadherin and the tight junction protein ZO-1. When proteomic results were analyzed, the relative distribution among functional categories was similar between occludin and claudin-4 proximal proteins. Apart from already known tight junction- proteins, occludin and claudin-4 proximal proteins were enriched in signaling and trafficking proteins, especially endocytic trafficking proteins. However there were significant differences in the specific proteins comprising the functional categories near each of the tagging proteins, revealing spatial compartmentalization within the junction complex. Taken together, these results expand the inventory of known and unknown proteins at the tight junction to inform future studies of the organization and physiology of this complex structure.

  4. Aroclor 1254, a developmental neurotoxicant, alters energy metabolism- and intracellular signaling-associated protein networks in rat cerebellum and hippocampus

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    Kodavanti, Prasada Rao S., E-mail: kodavanti.prasada@epa.gov [Neurotoxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Osorio, Cristina [Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States); Royland, Joyce E.; Ramabhadran, Ram [Genetic and Cellular Toxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Alzate, Oscar [Department of Cellular and Developmental Biology, University of North Carolina at Chapel Hill, North Carolina (United States); Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States)

    2011-11-15

    The vast literature on the mode of action of polychlorinated biphenyls (PCBs) indicates that PCBs are a unique model for understanding the mechanisms of toxicity of environmental mixtures of persistent chemicals. PCBs have been shown to adversely affect psychomotor function and learning and memory in humans. Although the molecular mechanisms for PCB effects are unclear, several studies indicate that the disruption of Ca{sup 2+}-mediated signal transduction plays significant roles in PCB-induced developmental neurotoxicity. Culminating events in signal transduction pathways include the regulation of gene and protein expression, which affects the growth and function of the nervous system. Our previous studies showed changes in gene expression related to signal transduction and neuronal growth. In this study, protein expression following developmental exposure to PCB is examined. Pregnant rats (Long Evans) were dosed with 0.0 or 6.0 mg/kg/day of Aroclor-1254 from gestation day 6 through postnatal day (PND) 21, and the cerebellum and hippocampus from PND14 animals were analyzed to determine Aroclor 1254-induced differential protein expression. Two proteins were found to be differentially expressed in the cerebellum following PCB exposure while 18 proteins were differentially expressed in the hippocampus. These proteins are related to energy metabolism in mitochondria (ATP synthase, sub unit {beta} (ATP5B), creatine kinase, and malate dehydrogenase), calcium signaling (voltage-dependent anion-selective channel protein 1 (VDAC1) and ryanodine receptor type II (RyR2)), and growth of the nervous system (dihydropyrimidinase-related protein 4 (DPYSL4), valosin-containing protein (VCP)). Results suggest that Aroclor 1254-like persistent chemicals may alter energy metabolism and intracellular signaling, which might result in developmental neurotoxicity. -- Highlights: Black-Right-Pointing-Pointer We performed brain proteomic analysis of rats exposed to the neurotoxicant

  5. Multi-tasking role of the mechanosensing protein Ankrd2 in the signaling network of striated muscle.

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    Anna Belgrano

    Full Text Available Ankrd2 (also known as Arpp together with Ankrd1/CARP and DARP are members of the MARP mechanosensing proteins that form a complex with titin (N2A/calpain 3 protease/myopalladin. In muscle, Ankrd2 is located in the I-band of the sarcomere and moves to the nucleus of adjacent myofibers on muscle injury. In myoblasts it is predominantly in the nucleus and on differentiation shifts from the nucleus to the cytoplasm. In agreement with its role as a sensor it interacts both with sarcomeric proteins and transcription factors.Expression profiling of endogenous Ankrd2 silenced in human myotubes was undertaken to elucidate its role as an intermediary in cell signaling pathways. Silencing Ankrd2 expression altered the expression of genes involved in both intercellular communication (cytokine-cytokine receptor interaction, endocytosis, focal adhesion, tight junction, gap junction and regulation of the actin cytoskeleton and intracellular communication (calcium, insulin, MAPK, p53, TGF-β and Wnt signaling. The significance of Ankrd2 in cell signaling was strengthened by the fact that we were able to show for the first time that Nkx2.5 and p53 are upstream effectors of the Ankrd2 gene and that Ankrd1/CARP, another MARP member, can modulate the transcriptional ability of MyoD on the Ankrd2 promoter. Another novel finding was the interaction between Ankrd2 and proteins with PDZ and SH3 domains, further supporting its role in signaling. It is noteworthy that we demonstrated that transcription factors PAX6, LHX2, NFIL3 and MECP2, were able to bind both the Ankrd2 protein and its promoter indicating the presence of a regulatory feedback loop mechanism.In conclusion we demonstrate that Ankrd2 is a potent regulator in muscle cells affecting a multitude of pathways and processes.

  6. Defining a modular signalling network from the fly interactome.

    Science.gov (United States)

    Baudot, Anaïs; Angelelli, Jean-Baptiste; Guénoche, Alain; Jacq, Bernard; Brun, Christine

    2008-05-19

    Signalling pathways relay information by transmitting signals from cell surface receptors to intracellular effectors that eventually activate the transcription of target genes. Since signalling pathways involve several types of molecular interactions including protein-protein interactions, we postulated that investigating their organization in the context of the global protein-protein interaction network could provide a new integrated view of signalling mechanisms. Using a graph-theory based method to analyse the fly protein-protein interaction network, we found that each signalling pathway is organized in two to three different signalling modules. These modules contain canonical proteins of the signalling pathways, known regulators as well as other proteins thereby predicted to participate to the signalling mechanisms. Connections between the signalling modules are prominent as compared to the other network's modules and interactions within and between signalling modules are among the more central routes of the interaction network. Altogether, these modules form an interactome sub-network devoted to signalling with particular topological properties: modularity, density and centrality. This finding reflects the integration of the signalling system into cell functioning and its important role connecting and coordinating different biological processes at the level of the interactome.

  7. A network of PUF proteins and Ras signaling promote mRNA repression and oogenesis in C. elegans

    Science.gov (United States)

    Hubstenberger, Arnaud; Cameron, Cristiana; Shtofman, Rebecca; Gutman, Shiri; Evans, Thomas C.

    2012-01-01

    Cell differentiation requires integration of gene expression controls with dynamic changes in cell morphology, function, and control. Post-transcriptional mRNA regulation and signaling systems are important to this process but their mechanisms and connections are unclear. During C. elegans oogenesis, we find that two groups of PUF RNA binding proteins (RNABPs), PUF-3/11 and PUF-5/6/7, control different specific aspects of oocyte formation. PUF-3/11 limits oocyte growth, while PUF-5/6/7 promotes oocyte organization and formation. These two PUF groups repress mRNA translation through overlapping but distinct sets of 3’ untranslated regions (3’UTRs). Several PUF-dependent mRNAs encode other mRNA regulators suggesting both PUF groups control developmental patterning of mRNA regulation circuits. Furthermore, we find that the Ras-MapKinase/ERK pathway functions with PUF-5/6/7 to repress specific mRNAs and control oocyte organization and growth. These results suggest that diversification of PUF proteins and their integration with Ras-MAPK signaling modulates oocyte differentiation. Together with other studies, these findings suggest positive and negative interactions between the Ras-MAPK system and PUF RNA-binding proteins likely occur at multiple levels. Changes in these interactions over time can influence spatiotemporal patterning of tissue development. PMID:22542599

  8. Soft Cysteine Signaling Network: The Functional Significance of Cysteine in Protein Function and the Soft Acids/Bases Thiol Chemistry That Facilitates Cysteine Modification.

    Science.gov (United States)

    Wible, Ryan S; Sutter, Thomas R

    2017-03-20

    The unique biophysical and electronic properties of cysteine make this molecule one of the most biologically critical amino acids in the proteome. The defining sulfur atom in cysteine is much larger than the oxygen and nitrogen atoms more commonly found in the other amino acids. As a result of its size, the valence electrons of sulfur are highly polarizable. Unique protein microenvironments favor the polarization of sulfur, thus increasing the overt reactivity of cysteine. Here, we provide a brief overview of the endogenous generation of reactive oxygen and electrophilic species and specific examples of enzymes and transcription factors in which the oxidation or covalent modification of cysteine in those proteins modulates their function. The perspective concludes with a discussion of cysteine chemistry and biophysics, the hard and soft acids and bases model, and the proposal of the Soft Cysteine Signaling Network: a hypothesis proposing the existence of a complex signaling network governed by layered chemical reactivity and cross-talk in which the chemical modification of reactive cysteine in biological networks triggers the reorganization of intracellular biochemistry to mitigate spikes in endogenous or exogenous oxidative or electrophilic stress.

  9. Stochastic delay accelerates signaling in gene networks.

    Science.gov (United States)

    Josić, Krešimir; López, José Manuel; Ott, William; Shiau, LieJune; Bennett, Matthew R

    2011-11-01

    The creation of protein from DNA is a dynamic process consisting of numerous reactions, such as transcription, translation and protein folding. Each of these reactions is further comprised of hundreds or thousands of sub-steps that must be completed before a protein is fully mature. Consequently, the time it takes to create a single protein depends on the number of steps in the reaction chain and the nature of each step. One way to account for these reactions in models of gene regulatory networks is to incorporate dynamical delay. However, the stochastic nature of the reactions necessary to produce protein leads to a waiting time that is randomly distributed. Here, we use queueing theory to examine the effects of such distributed delay on the propagation of information through transcriptionally regulated genetic networks. In an analytically tractable model we find that increasing the randomness in protein production delay can increase signaling speed in transcriptional networks. The effect is confirmed in stochastic simulations, and we demonstrate its impact in several common transcriptional motifs. In particular, we show that in feedforward loops signaling time and magnitude are significantly affected by distributed delay. In addition, delay has previously been shown to cause stable oscillations in circuits with negative feedback. We show that the period and the amplitude of the oscillations monotonically decrease as the variability of the delay time increases.

  10. Auxiliary and autonomous proteoglycan signaling networks.

    Science.gov (United States)

    Elfenbein, Arye; Simons, Michael

    2010-01-01

    Proteoglycans represent a structurally heterogeneous family of proteins that typically undergo extensive posttranslational modification with sulfated sugar chains. Although historically believed to affect signaling pathways exclusively as growth factor coreceptors, proteoglycans are now understood to initiate and modulate signal transduction cascades independently of other receptors. From within the extracellular matrix, proteoglycans are able to shield protein growth factors from circulating proteases and establish gradients that guide cell migration. Extracellular proteoglycans are also critical in the maintenance of growth factor stores and are thus instrumental in modulating paracrine signaling. At the cell membrane, proteoglycans stabilize ligand-receptor interactions, creating potentiated ternary signaling complexes that regulate cell proliferation, endocytosis, migration, growth factor sensitivity, and matrix adhesion. In some cases, proteoglycans are able to independently activate various signaling cascades, attenuate the signaling of growth factors, or orchestrate multimeric intracellular signaling complexes. Signaling between cells is also modulated by proteoglycan activity at the cell membrane, as exemplified by the proteoglycan requirement for effective synaptogenesis between neurons. Finally, proteoglycans are able to regulate signaling from intracellular compartments, particularly in the context of storage granule formation and maintenance. These proteoglycans are also major determinants of exocytic vesicle fate and other vesicular trafficking pathways. In contrast to the mechanisms underlying classical ligand-receptor signaling, proteoglycan signaling is frequently characterized by ligand promiscuity and low-affinity binding; likewise, these events commonly do not exhibit the same degree of reliance on intermolecular structure or charge configurations as other ligand-receptor interactions. Such unique features often defy conventional mechanisms of

  11. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.

    2013-01-01

    During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

  12. Quantitative phosphoproteomics to characterize signaling networks

    DEFF Research Database (Denmark)

    Rigbolt, Kristoffer T G; Blagoev, Blagoy

    2012-01-01

    Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform for analy......Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform...... for analyzing protein phosphorylation at a system-wide scale and has become the intuitive strategy for comprehensive characterization of signaling networks. Contemporary phosphoproteomics use highly optimized procedures for sample preparation, mass spectrometry and data analysis algorithms to identify...... and quantify thousands of phosphorylations, thus providing extensive overviews of the cellular signaling networks. As a result of these developments quantitative phosphoproteomics have been applied to study processes as diverse as immunology, stem cell biology and DNA damage. Here we review the developments...

  13. Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

    Directory of Open Access Journals (Sweden)

    Masanao Sato

    Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

  14. Prediction of N-terminal protein sorting signals

    DEFF Research Database (Denmark)

    Claros, Manuel G.; Brunak, Søren; von Heijne, Gunnar

    1997-01-01

    Recently, neural networks have been applied to a widening range of problems in molecular biology. An area particularly suited to neural-network methods is the identification of protein sorting signals and the prediction of their cleavage sites, as these functional units are encoded by local, linear...

  15. Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling*

    Science.gov (United States)

    Ryall, Karen A.; Holland, David O.; Delaney, Kyle A.; Kraeutler, Matthew J.; Parker, Audrey J.; Saucerman, Jeffrey J.

    2012-01-01

    Cardiac hypertrophy is managed by a dense web of signaling pathways with many pathways influencing myocyte growth. A quantitative understanding of the contributions of individual pathways and their interactions is needed to better understand hypertrophy signaling and to develop more effective therapies for heart failure. We developed a computational model of the cardiac myocyte hypertrophy signaling network to determine how the components and network topology lead to differential regulation of transcription factors, gene expression, and myocyte size. Our computational model of the hypertrophy signaling network contains 106 species and 193 reactions, integrating 14 established pathways regulating cardiac myocyte growth. 109 of 114 model predictions were validated using published experimental data testing the effects of receptor activation on transcription factors and myocyte phenotypic outputs. Network motif analysis revealed an enrichment of bifan and biparallel cross-talk motifs. Sensitivity analysis was used to inform clustering of the network into modules and to identify species with the greatest effects on cell growth. Many species influenced hypertrophy, but only a few nodes had large positive or negative influences. Ras, a network hub, had the greatest effect on cell area and influenced more species than any other protein in the network. We validated this model prediction in cultured cardiac myocytes. With this integrative computational model, we identified the most influential species in the cardiac hypertrophy signaling network and demonstrate how different levels of network organization affect myocyte size, transcription factors, and gene expression. PMID:23091058

  16. Spectral affinity in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-11-01

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

  17. Spectral affinity in protein networks.

    Science.gov (United States)

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

    2009-11-29

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

  18. Signal propagation in cortical networks: a digital signal processing approach.

    Science.gov (United States)

    Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano

    2009-01-01

    This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

  19. Rewiring cell signalling through chimaeric regulatory protein engineering.

    Science.gov (United States)

    Wang, Baojun; Barahona, Mauricio; Buck, Martin; Schumacher, Jörg

    2013-10-01

    Bacterial cells continuously sense and respond to their environment using their inherent signalling and gene regulatory networks. Cells are equipped with parallel signalling pathways, which can specifically cope with individual input signals, while interconnectivities between pathways lead to an enhanced complexity of regulatory responses that enable sophisticated adaptation. In principle, any cell signalling pathway may be rewired to respond to non-cognate signals by exchanging and recombining their underlying cognate signalling components. In the present article, we review the engineering strategies and use of chimaeric regulatory proteins in cell signalling pathways, especially the TCS (two-component signalling) system in bacteria, to achieve novel customized signalling or regulatory functions. We envisage that engineered chimaeric regulatory proteins can play an important role to aid both forward and reverse engineering of biological systems for many desired applications.

  20. Community detection by signaling on complex networks

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  1. Community detection by signaling on complex networks.

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; Di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  2. Reconstruction of periodic signals using neural networks

    Directory of Open Access Journals (Sweden)

    José Danilo Rairán Antolines

    2014-01-01

    Full Text Available In this paper, we reconstruct a periodic signal by using two neural networks. The first network is trained to approximate the period of a signal, and the second network estimates the corresponding coefficients of the signal's Fourier expansion. The reconstruction strategy consists in minimizing the mean-square error via backpro-pagation algorithms over a single neuron with a sine transfer function. Additionally, this paper presents mathematical proof about the quality of the approximation as well as a first modification of the algorithm, which requires less data to reach the same estimation; thus making the algorithm suitable for real-time implementations.

  3. Signaling in large-scale neural networks

    DEFF Research Database (Denmark)

    Berg, Rune W; Hounsgaard, Jørn

    2009-01-01

    We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this m......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

  4. Signal processing devices and networks

    Science.gov (United States)

    Graveline, S. W.

    1985-02-01

    According to an axiom employed with respect to electronic warfare (EW) behavior, system effectiveness increases directly with the amount of information recovered from an intercepted signal. The evolution in EW signal processing capability has proceeded accordingly. After an initiation of EW systems as broadband receivers, the most significant advance was related to the development of digital instantaneous frequency measurement (DIFM) devices. The use of such devices provides significant improvements regarding signal identification and RF measurement to within a few MHz. An even more accurate processing device, the digital RF memory (DRFM), allows frequency characterization to within a few Hz. This invention was made in response to the need to process coherent pulse signals. Attention is given to the generic EW system, the modern EW system, and the generic receiver function for a modern EW system showing typical output signals.

  5. Predicting Secretory Proteins with SignalP

    DEFF Research Database (Denmark)

    Nielsen, Henrik

    2017-01-01

    SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history...

  6. Reaction network analysis in biochemical signaling pathways

    OpenAIRE

    Martinez-Forero, I. (Iván); Pelaez, A. (Antonio); Villoslada, P. (Pablo)

    2010-01-01

    The aim of this thesis is to improve the understanding of signaling pathways through a theoretical study of chemical reaction networks. The equilibirum solution to the equations derived from chemical networks will be analytically resolved using tools from algebraic geometry. The chapters are organized as follows: 1. An introduction to chemical dynamics in biological systems with a special emphasis on steady state analysis 2. Complete description of the chemical reaction network theor...

  7. The nuclear retention signal of HPV16 L2 protein is essential for incoming viral genome to transverse the trans-Golgi network

    Energy Technology Data Exchange (ETDEWEB)

    DiGiuseppe, Stephen; Bienkowska-Haba, Malgorzata; Hilbig, Lydia; Sapp, Martin, E-mail: msapp1@lsuhsc.edu

    2014-06-15

    The Human papillomavirus (HPV) capsid is composed of the major and minor capsid proteins, L1 and L2, respectively. Infectious entry requires a complex series of conformational changes in both proteins that lead to uptake and allow uncoating to occur. During entry, the capsid is disassembled and host cyclophilins dissociate L1 protein from the L2/DNA complex. Herein, we describe a mutant HPV16 L2 protein (HPV16 L2-R302/5A) that traffics pseudogenome to the trans-Golgi network (TGN) but fails to egress. Our data provide further evidence that HPV16 traffics through the TGN and demonstrates that L2 is essential for TGN egress. Furthermore, we show that cyclophilin activity is required for the L2/DNA complex to be transported to the TGN which is accompanied by a reduced L1 protein levels. - Highlights: • mNLS mutant HPV16 L2 protein traffics pseudogenome to the TGN but fails to egress. • Cyclophilin activity is required for trafficking of the L2/DNA complex to the TGN. • Majority of L1 protein is shed from the L2/DNA complex prior to reaching the TGN.

  8. Cancer signaling networks and their implications for personalized medicine

    DEFF Research Database (Denmark)

    Creixell, Pau

    as well as cancer resistance to treatment, represent not only a huge challenge, but also one with potentially extreme benefit for our understanding of the disease and for patients. This thesis summarizes my efforts during the last years in contributing positively to overcome this challenge. This thesis...... is divided into six parts. Starting with a brief introduction to the history and some basic concepts of cancer, signaling networks and human protein kinases (part I), we quickly move on to describing existing methods to analyze cancer signaling networks, including methods proposed by us, as well as three...

  9. Biased Gs Versus Gq Proteins and β-Arrestin Signaling in the NK1 Receptor Determined by Interactions in the Water Hydrogen Bond Network

    DEFF Research Database (Denmark)

    Valentin-Hansen, Louise; Frimurer, Thomas M; Mokrosinski, Jacek

    2015-01-01

    of the highly conserved AspII:10 (2.50). Here, we find that this GluII:10 occupies the space of a putative allosteric modulating Na(+) ion and makes direct inter-helical interactions in particular with SerIII:15 (3.39) and AsnVII:16 (7.49) of the NPXXY motif. Mutational changes in the interface between GluII:10......X-ray structures, molecular dynamics simulations, and mutational analysis have previously indicated that an extended water hydrogen bond network between trans-membranes I-III, VI, and VII constitutes an allosteric interface essential for stabilizing different active and inactive helical....... It is concluded that the interface between position II:10 (2.50), III:15 (3.39), and VII:16 (7.49) in the center of the water hydrogen bond network constitutes a focal point for fine-tuning seven trans-membrane receptor conformations activating different signal transduction pathways....

  10. A conserved mammalian protein interaction network.

    Directory of Open Access Journals (Sweden)

    Åsa Pérez-Bercoff

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

  11. A protein interaction network associated with asthma.

    Science.gov (United States)

    Hwang, Sohyun; Son, Seung-Woo; Kim, Sang Cheol; Kim, Young Joo; Jeong, Hawoong; Lee, Doheon

    2008-06-21

    Identifying candidate genes related to complex diseases or traits and mapping their relationships require a system-level analysis at a cellular scale. The objective of the present study is to systematically analyze the complex effects of interrelated genes and provide a framework for revealing their relationships in association with a specific disease (asthma in this case). We observed that protein-protein interaction (PPI) networks associated with asthma have a power-law connectivity distribution as many other biological networks have. The hub nodes and skeleton substructure of the result network are consistent with the prior knowledge about asthma pathways, and also suggest unknown candidate target genes associated with asthma, including GNB2L1, BRCA1, CBL, and VAV1. In particular, GNB2L1 appears to play a very important role in the asthma network through frequent interactions with key proteins in cellular signaling. This network-based approach represents an alternative method for analyzing the complex effects of candidate genes associated with complex diseases and suggesting a list of gene drug targets. The full list of genes and the analysis details are available in the following online supplementary materials: http://biosoft.kaist.ac.kr:8080/resources/asthma_ppi.

  12. Signaling pathway networks mined from human pituitary adenoma proteomics data

    Directory of Open Access Journals (Sweden)

    Zhan Xianquan

    2010-04-01

    Full Text Available Abstract Background We obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins, comparative proteomic data (56 differentially expressed proteins, and nitroproteomic data (17 nitroproteins. There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system. Methods The Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses. Results For the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a

  13. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

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

  14. SiGNet: A signaling network data simulator to enable signaling network inference.

    Directory of Open Access Journals (Sweden)

    Elizabeth A Coker

    Full Text Available Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks: a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.

  15. Primary Cilia, Signaling Networks and Cell Migration

    DEFF Research Database (Denmark)

    Veland, Iben Rønn

    on formation of the primary cilium and CDE at the pocket region. The ciliary protein Inversin functions as a molecular switch between canonical and non-canonical Wnt signaling. In paper II, we show that Inversin and the primary cilium control Wnt signaling and are required for polarization and cell migration....... A number of central Wnt components localize to the fibroblast primary cilium, including the Wnt5a-receptor, Fzd3, and Dvl proteins. Inversin-deficient MEFs have an elevated expression of canonical Wnt-associated genes and proteins, in addition to dysregulation of components in non-canonical Wnt signaling......, which leads to uncontrolled cell movements. Together, the results obtained from my PhD studies reflect the high level of complexity within signaling systems regulated by the primary cilium that control cellular processes during embryonic development and in tissue homeostasis. As such, this dissertation...

  16. Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

    Science.gov (United States)

    Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

    2015-01-01

    Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

  17. MAPK cascade signalling networks in plant defence.

    Science.gov (United States)

    Pitzschke, Andrea; Schikora, Adam; Hirt, Heribert

    2009-08-01

    The sensing of stress signals and their transduction into appropriate responses is crucial for the adaptation and survival of plants. Kinase cascades of the mitogen-activated protein kinase (MAPK) class play a remarkably important role in plant signalling of a variety of abiotic and biotic stresses. MAPK cascade-mediated signalling is an essential step in the establishment of resistance to pathogens. Here, we describe the most recent insights into MAPK-mediated pathogen defence response regulation with a particular focus on the cascades involving MPK3, MPK4 and MPK6. We also discuss the strategies developed by plant pathogens to circumvent, inactivate or even 'hijack' MAPK-mediated defence responses.

  18. Signal peptides and protein localization prediction

    DEFF Research Database (Denmark)

    Nielsen, Henrik

    2005-01-01

    In 1999, the Nobel prize in Physiology or Medicine was awarded to Gunther Blobel “for the discovery that proteins have intrinsic signals that govern their transport and localization in the cell”. Since the subcellular localization of a protein is an important clue to its function, the characteriz......In 1999, the Nobel prize in Physiology or Medicine was awarded to Gunther Blobel “for the discovery that proteins have intrinsic signals that govern their transport and localization in the cell”. Since the subcellular localization of a protein is an important clue to its function......, the characterization and prediction of these intrinsic signals – the “zip codes” of proteins – has become a major task in bioinformatics. Here, I will review the most important methods for the prediction of subcellular localization, also known as protein sorting. Owing to the limited space, this review is far from...... complete; especially, applications that are not publicly available on-line are ignored. Generally, there are two approaches to protein localization prediction: signal detection, that is, prediction of the sorting signals themselves, and prediction based on global properties (amino acid composition and...

  19. A novel gene’s role in an ancient mechanism: secreted Frizzled-related protein 1 is a critical component in the anterior–posterior Wnt signaling network that governs the establishment of the anterior neuroectoderm in sea urchin embryos

    Directory of Open Access Journals (Sweden)

    Anita Khadka

    2018-01-01

    Full Text Available Abstract The anterior neuroectoderm (ANE in many deuterostome embryos (echinoderms, hemichordates, urochordates, cephalochordates, and vertebrates is progressively restricted along the anterior–posterior axis to a domain around the anterior pole. In the sea urchin embryo, three integrated Wnt signaling branches (Wnt/β-catenin, Wnt/JNK, and Wnt/PKC govern this progressive restriction process, which begins around the 32- to 60-cell stage and terminates by the early gastrula stage. We previously have established that several secreted Wnt modulators of the Dickkopf and secreted Frizzled-related protein families (Dkk1, Dkk3, and sFRP-1/5 are expressed within the ANE and play important roles in modulating the Wnt signaling network during this process. In this study, we use morpholino and dominant-negative interference approaches to characterize the function of a novel Frizzled-related protein, secreted Frizzled-related protein 1 (sFRP-1, during ANE restriction. sFRP-1 appears to be related to a secreted Wnt modulator, sFRP3/4, that is essential to block Wnt signaling and establish the ANE in vertebrates. Here, we show that the sea urchin sFRP3/4 orthologue is not expressed during ANE restriction in the sea urchin embryo. Instead, our results indicate that ubiquitously expressed maternal sFRP-1 and Fzl1/2/7 signaling act together as early as the 32- to 60-cell stage to antagonize the ANE restriction mechanism mediated by Wnt/β-catenin and Wnt/JNK signaling. Then, starting from the blastula stage, Fzl5/8 signaling activates zygotic sFRP-1 within the ANE territory, where it works with the secreted Wnt antagonist Dkk1 (also activated by Fzl5/8 signaling to antagonize Wnt1/Wnt8–Fzl5/8–JNK signaling in a negative feedback mechanism that defines the outer ANE territory boundary. Together, these data indicate that maternal and zygotic sFRP-1 protects the ANE territory by antagonizing the Wnt1/Wnt8–Fzl5/8–JNK signaling pathway throughout ANE

  20. A novel gene's role in an ancient mechanism: secreted Frizzled-related protein 1 is a critical component in the anterior-posterior Wnt signaling network that governs the establishment of the anterior neuroectoderm in sea urchin embryos.

    Science.gov (United States)

    Khadka, Anita; Martínez-Bartolomé, Marina; Burr, Stephanie D; Range, Ryan C

    2018-01-01

    The anterior neuroectoderm (ANE) in many deuterostome embryos (echinoderms, hemichordates, urochordates, cephalochordates, and vertebrates) is progressively restricted along the anterior-posterior axis to a domain around the anterior pole. In the sea urchin embryo, three integrated Wnt signaling branches (Wnt/β-catenin, Wnt/JNK, and Wnt/PKC) govern this progressive restriction process, which begins around the 32- to 60-cell stage and terminates by the early gastrula stage. We previously have established that several secreted Wnt modulators of the Dickkopf and secreted Frizzled-related protein families (Dkk1, Dkk3, and sFRP-1/5) are expressed within the ANE and play important roles in modulating the Wnt signaling network during this process. In this study, we use morpholino and dominant-negative interference approaches to characterize the function of a novel Frizzled-related protein, secreted Frizzled-related protein 1 (sFRP-1), during ANE restriction. sFRP-1 appears to be related to a secreted Wnt modulator, sFRP3/4, that is essential to block Wnt signaling and establish the ANE in vertebrates. Here, we show that the sea urchin sFRP3/4 orthologue is not expressed during ANE restriction in the sea urchin embryo. Instead, our results indicate that ubiquitously expressed maternal sFRP-1 and Fzl1/2/7 signaling act together as early as the 32- to 60-cell stage to antagonize the ANE restriction mechanism mediated by Wnt/β-catenin and Wnt/JNK signaling. Then, starting from the blastula stage, Fzl5/8 signaling activates zygotic sFRP-1 within the ANE territory, where it works with the secreted Wnt antagonist Dkk1 (also activated by Fzl5/8 signaling) to antagonize Wnt1/Wnt8-Fzl5/8-JNK signaling in a negative feedback mechanism that defines the outer ANE territory boundary. Together, these data indicate that maternal and zygotic sFRP-1 protects the ANE territory by antagonizing the Wnt1/Wnt8-Fzl5/8-JNK signaling pathway throughout ANE restriction, providing precise

  1. The statistical mechanics of complex signaling networks: nerve growth factor signaling

    Science.gov (United States)

    Brown, K. S.; Hill, C. C.; Calero, G. A.; Myers, C. R.; Lee, K. H.; Sethna, J. P.; Cerione, R. A.

    2004-10-01

    The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'

  2. CBL-CIPK network for calcium signaling in higher plants

    Science.gov (United States)

    Luan, Sheng

    Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.

  3. Orm Proteins Integrate Multiple Signals to Maintain Sphingolipid Homeostasis*

    Science.gov (United States)

    Gururaj, Charulatha; Federman, Ross; Chang, Amy

    2013-01-01

    Sphingolipids are structural components of membranes, and sphingolipid metabolites serve as signaling molecules. The first and rate-limiting step in sphingolipid synthesis is catalyzed by serine palmitoyltransferase (SPT). The recently discovered SPT-associated proteins, Orm1 and Orm2, are critical regulators of sphingolipids. Orm protein phosphorylation mediating feedback regulation of SPT activity occurs in response to multiple sphingolipid intermediates, including long chain base and complex sphingolipids. Both branches of the TOR signaling network, TORC1 and TORC2, participate in regulating sphingolipid synthesis via Orm phosphorylation in response to sphingolipid intermediates as well as nutritional conditions. Moreover, sphingolipid synthesis is regulated in response to endoplasmic reticulum (ER) stress by activation of a calcium- and calcineurin-dependent pathway via transcriptional induction of ORM2. Conversely, the calcium- and calcineurin-dependent pathway signals ER stress response upon lipid dysregulation in the absence of the Orm proteins to restore ER homeostasis. PMID:23737533

  4. Orm proteins integrate multiple signals to maintain sphingolipid homeostasis.

    Science.gov (United States)

    Gururaj, Charulatha; Federman, Ross S; Federman, Ross; Chang, Amy

    2013-07-12

    Sphingolipids are structural components of membranes, and sphingolipid metabolites serve as signaling molecules. The first and rate-limiting step in sphingolipid synthesis is catalyzed by serine palmitoyltransferase (SPT). The recently discovered SPT-associated proteins, Orm1 and Orm2, are critical regulators of sphingolipids. Orm protein phosphorylation mediating feedback regulation of SPT activity occurs in response to multiple sphingolipid intermediates, including long chain base and complex sphingolipids. Both branches of the TOR signaling network, TORC1 and TORC2, participate in regulating sphingolipid synthesis via Orm phosphorylation in response to sphingolipid intermediates as well as nutritional conditions. Moreover, sphingolipid synthesis is regulated in response to endoplasmic reticulum (ER) stress by activation of a calcium- and calcineurin-dependent pathway via transcriptional induction of ORM2. Conversely, the calcium- and calcineurin-dependent pathway signals ER stress response upon lipid dysregulation in the absence of the Orm proteins to restore ER homeostasis.

  5. Identifying Driver Nodes in the Human Signaling Network Using Structural Controllability Analysis.

    Science.gov (United States)

    Liu, Xueming; Pan, Linqiang

    2015-01-01

    Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.

  6. Stochastic effects as a force to increase the complexity of signaling networks

    KAUST Repository

    Kuwahara, Hiroyuki

    2013-07-29

    Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism. However, the conditions under which molecular noise influences cellular information processing remain unclear. Here, we explore a large number of simple biological models of varying network sizes to understand the architectural conditions under which the interactions of signaling proteins can exhibit specific stochastic effects - called deviant effects - in which the average behavior of a biological system is substantially altered in the presence of molecular noise. We find that a small fraction of these networks does exhibit deviant effects and shares a common architectural feature whereas most of the networks show only insignificant levels of deviations. Interestingly, addition of seemingly unimportant interactions into protein networks gives rise to deviant effects.

  7. Modeling Signaling Networks to Advance New Cancer Therapies.

    Science.gov (United States)

    Saez-Rodriguez, Julio; MacNamara, Aidan; Cook, Simon

    2015-01-01

    Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

  8. Unraveling protein networks with power graph analysis.

    Science.gov (United States)

    Royer, Loïc; Reimann, Matthias; Andreopoulos, Bill; Schroeder, Michael

    2008-07-11

    Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks.

  9. Acoustic signal propagation characterization of conduit networks

    Science.gov (United States)

    Khan, Muhammad Safeer

    Analysis of acoustic signal propagation in conduit networks has been an important area of research in acoustics. One major aspect of analyzing conduit networks as acoustic channels is that a propagating signal suffers frequency dependent attenuation due to thermo-viscous boundary layer effects and the presence of impedance mismatches such as side branches. The signal attenuation due to side branches is strongly influenced by their numbers and dimensions such as diameter and length. Newly developed applications for condition based monitoring of underground conduit networks involve measurement of acoustic signal attenuation through tests in the field. In many cases the exact installation layout of the field measurement location may not be accessible or actual installation may differ from the documented layout. The lack of exact knowledge of numbers and lengths of side branches, therefore, introduces uncertainty in the measurements of attenuation and contributes to the random variable error between measured results and those predicted from theoretical models. There are other random processes in and around conduit networks in the field that also affect the propagation of an acoustic signal. These random processes include but are not limited to the presence of strong temperature and humidity gradients within the conduits, blockages of variable sizes and types, effects of aging such as cracks, bends, sags and holes, ambient noise variations and presence of variable layer of water. It is reasonable to consider that the random processes contributing to the error in the measured attenuation are independent and arbitrarily distributed. The error, contributed by a large number of independent sources of arbitrary probability distributions, is best described by an approximately normal probability distribution in accordance with the central limit theorem. Using an analytical approach to model the attenuating effect of each of the random variable sources can be very complex and

  10. Qualitative networks: a symbolic approach to analyze biological signaling networks

    Directory of Open Access Journals (Sweden)

    Henzinger Thomas A

    2007-01-01

    Full Text Available Abstract Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.

  11. The glucose signaling network in yeast

    Science.gov (United States)

    Kim, Jeong-Ho; Roy, Adhiraj; Jouandot, David; Cho, Kyu Hong

    2013-01-01

    Background Most cells possess a sophisticated mechanism for sensing glucose and responsing to it appropriately. Glucose sensing and signaling in the budding yeast Saccharomyces cerevisiae represents an important paradigm for understanding how extracellular signals lead to changes in the gene expression program in eukaryotes. Scope of review This review focuses on the yeast glucose sensing and signaling pathways that operate in a highly regulated and cooperative manner to bring about glucose-induction of HXT gene expression. Major conclusions The yeast cells possess a family of glucose transporters (HXTs), with different kinetic properties. They employ three major glucose signaling pathways— Rgt2/Snf3, AMPK, and cAMP-PKA—to express only those transporters best suited for the amounts of glucose available. We discuss the current understanding of how these pathways are integrated into a regulatory network to ensure efficient uptake and utilization of glucose. General significance Elucidating the role of multiple glucose signals and pathways involved in glucose uptake and metabolism in yeast may reveal the molecular basis of glucose homeostasis in humans, especially under pathological conditions, such as hyperglycemia in diabetics and the elevated rate of glycolysis observed in many solid tumors. PMID:23911748

  12. Geometric evolutionary dynamics of protein interaction networks.

    Science.gov (United States)

    Przulj, Natasa; Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne

    2010-01-01

    Understanding the evolution and structure of protein-protein interaction (PPI) networks is a central problem of systems biology. Since most processes in the cell are carried out by groups of proteins acting together, a theoretical model of how PPI networks develop based on duplications and mutations is an essential ingredient for understanding the complex wiring of the cell. Many different network models have been proposed, from those that follow power-law degree distributions and those that model complementarity of protein binding domains, to those that have geometric properties. Here, we introduce a new model for PPI network (and thus gene) evolution that produces well-fitting network models for currently available PPI networks. The model integrates geometric network properties with evolutionary dynamics of PPI network evolution.

  13. Emerging connections in the ethylene signaling network.

    Science.gov (United States)

    Yoo, Sang-Dong; Cho, Younghee; Sheen, Jen

    2009-05-01

    The gaseous plant hormone ethylene acts as a pivotal mediator to respond to and coordinate internal and external cues in modulating plant growth dynamics and developmental programs. Genetic analysis of Arabidopsis thaliana has been used to identify key components and to build a linear ethylene-signaling pathway from the receptors through to the nuclear transcription factors. Studies applying integrative approaches have revealed new regulators, molecular connections and mechanisms in ethylene signaling and unexpected links to other plant hormones. Here, we review and discuss recent discoveries about the functional mode of ethylene receptor complexes, dual mitogen-activated protein kinase cascade signaling, stability control of the master nuclear transcription activator ETHYLENE INSENSITIVE 3 (EIN3), and the contextual relationships between ethylene and other plant hormones, such as auxin and gibberellins, in organ-specific growth regulation.

  14. Temporal protein expression pattern in intracellular signalling ...

    Indian Academy of Sciences (India)

    To study the propagation of such defects with time and their effect on the intracellular protein expression patterns, a comprehensive and largest pathway map of T-cell activation network is reconstructed manually. The entire pathway reactions are then translated using logical equations and simulated using the published ...

  15. Modeling signaling networks with different formalisms: a preview.

    Science.gov (United States)

    MacNamara, Aidan; Henriques, David; Saez-Rodriguez, Julio

    2013-01-01

    In the last 30 years, many of the mechanisms behind signal transduction, the process by which the cell takes extracellular signals as an input and converts them to a specific cellular phenotype, have been experimentally determined. With these discoveries, however, has come the realization that the architecture of signal transduction, the signaling network, is incredibly complex. Although the main pathways between receptor and output are well-known, there is a complex net of regulatory features that include crosstalk between different pathways, spatial and temporal effects, and positive and negative feedbacks. Hence, modeling approaches have been used to try and unravel some of these complexities. We use the mitogen-activated protein kinase cascade to illustrate chemical kinetic and logic approaches to modeling signaling networks. By using a common well-known model, we illustrate here the assumptions and level of detail behind each modeling approach, which serves as an introduction to the more detailed discussions of each in the accompanying chapters in this book.

  16. Integrating multiple networks for protein function prediction.

    Science.gov (United States)

    Yu, Guoxian; Zhu, Hailong; Domeniconi, Carlotta; Guo, Maozu

    2015-01-01

    High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction. We address this issue by modeling the optimization of the composite network and the prediction problems within a unified objective function. In particular, we use a kernel target alignment technique and the loss function of a network based classifier to jointly adjust the weights assigned to the individual networks. We show that the proposed method, called MNet, can achieve a performance that is superior (with respect to different evaluation criteria) to related techniques using the multiple networks of four example species (yeast, human, mouse, and fly) annotated with thousands (or hundreds) of GO terms. MNet can effectively integrate multiple networks for protein function prediction and is robust to the input parameters. Supplementary data is available at https://sites.google.com/site/guoxian85/home/mnet. The Matlab code of MNet is available upon request.

  17. Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

    Directory of Open Access Journals (Sweden)

    Menizibeya O. Welcome

    2015-01-01

    Full Text Available Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning.

  18. Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

    Science.gov (United States)

    Welcome, Menizibeya O.; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

    2015-01-01

    Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose) regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning. PMID:25653876

  19. Ubiquitin in signaling and protein quality control

    DEFF Research Database (Denmark)

    Al-Saoudi, Sofie Vincents

    Protein ubiquitylation is an important post-translational modification that holds a variety of cellular functions. This Ph.D. thesis is comprised of two studies, of which one focused on ubiquitylation related to inflammatory signaling, and the other on the role of the ubiquitin-proteasome system ...

  20. Signal regulatory proteins in the immune system

    NARCIS (Netherlands)

    van Beek, Ellen M.; Cochrane, Fiona; Barclay, A. Neil; van den Berg, Timo K.

    2005-01-01

    Signal regulatory proteins (SIRPs) constitute a family of transmembrane glycoproteins with extracellular Ig-like domains. Several SIRP family members have thus far been identified on myeloid and other cells in man, mouse, rat, and cattle. In the present study, we provide a description of the SIRP

  1. Barcoding of GPCR trafficking and signaling through the various trafficking roadmaps by compartmentalized signaling networks.

    Science.gov (United States)

    Bahouth, Suleiman W; Nooh, Mohammed M

    2017-08-01

    Proper signaling by G protein coupled receptors (GPCR) is dependent on the specific repertoire of transducing, enzymatic and regulatory kinases and phosphatases that shape its signaling output. Activation and signaling of the GPCR through its cognate G protein is impacted by G protein-coupled receptor kinase (GRK)-imprinted "barcodes" that recruit β-arrestins to regulate subsequent desensitization, biased signaling and endocytosis of the GPCR. The outcome of agonist-internalized GPCR in endosomes is also regulated by sequence motifs or "barcodes" within the GPCR that mediate its recycling to the plasma membrane or retention and eventual degradation as well as its subsequent signaling in endosomes. Given the vast number of diverse sequences in GPCR, several trafficking mechanisms for endosomal GPCR have been described. The majority of recycling GPCR, are sorted out of endosomes in a "sequence-dependent pathway" anchored around a type-1 PDZ-binding module found in their C-tails. For a subset of these GPCR, a second "barcode" imprinted onto specific GPCR serine/threonine residues by compartmentalized kinase networks was required for their efficient recycling through the "sequence-dependent pathway". Mutating the serine/threonine residues involved, produced dramatic effects on GPCR trafficking, indicating that they played a major role in setting the trafficking itinerary of these GPCR. While endosomal SNX27, retromer/WASH complexes and actin were required for efficient sorting and budding of all these GPCR, additional proteins were required for GPCR sorting via the second "barcode". Here we will review recent developments in GPCR trafficking in general and the human β 1 -adrenergic receptor in particular across the various trafficking roadmaps. In addition, we will discuss the role of GPCR trafficking in regulating endosomal GPCR signaling, which promote biochemical and physiological effects that are distinct from those generated by the GPCR signal transduction

  2. MSAT signalling and network management architectures

    Science.gov (United States)

    Garland, Peter; Keelty, J. Malcolm

    1989-01-01

    Spar Aerospace has been active in the design and definition of Mobile Satellite Systems since the mid 1970's. In work sponsored by the Canadian Department of Communications, various payload configurations have evolved. In addressing the payload configuration, the requirements of the mobile user, the service provider and the satellite operator have always been the most important consideration. The current Spar 11 beam satellite design is reviewed, and its capabilities to provide flexibility and potential for network growth within the WARC87 allocations are explored. To enable the full capabilities of the payload to be realized, a large amount of ground based Switching and Network Management infrastructure will be required, when space segment becomes available. Early indications were that a single custom designed Demand Assignment Multiple Access (DAMA) switch should be implemented to provide efficient use of the space segment. As MSAT has evolved into a multiple service concept, supporting many service providers, this architecture should be reviewed. Some possible signalling and Network Management solutions are explored.

  3. Discovering functional interaction patterns in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2008-06-01

    Full Text Available Abstract Background In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks. Results In this article, we map known functional annotations of proteins onto a PPI network in order to identify frequently occurring interaction patterns in the functional space. We propose a new frequent pattern identification technique, PPISpan, adapted specifically for PPI networks from a well-known frequent subgraph identification method, gSpan. Existing module discovery techniques either look for specific clique-like highly interacting protein clusters or linear paths of interaction. However, our goal is different; instead of single clusters or pathways, we look for recurring functional interaction patterns in arbitrary topologies. We have applied PPISpan on PPI networks of Saccharomyces cerevisiae and identified a number of frequently occurring functional interaction patterns. Conclusion With the help of PPISpan, recurring functional interaction patterns in an organism's PPI network can be identified. Such an analysis offers a new perspective on the modular organization of PPI networks. The complete list of identified functional interaction patterns is available at http://bioserver.ceng.metu.edu.tr/PPISpan/.

  4. A modular analysis of the auxin signalling network.

    Directory of Open Access Journals (Sweden)

    Etienne Farcot

    Full Text Available Auxin is essential for plant development from embryogenesis onwards. Auxin acts in large part through regulation of transcription. The proteins acting in the signalling pathway regulating transcription downstream of auxin have been identified as well as the interactions between these proteins, thus identifying the topology of this network implicating 54 Auxin Response Factor (ARF and Aux/IAA (IAA transcriptional regulators. Here, we study the auxin signalling pathway by means of mathematical modeling at the single cell level. We proceed analytically, by considering the role played by five functional modules into which the auxin pathway can be decomposed: the sequestration of ARF by IAA, the transcriptional repression by IAA, the dimer formation amongst ARFs and IAAs, the feedback loop on IAA and the auxin induced degradation of IAA proteins. Focusing on these modules allows assessing their function within the dynamics of auxin signalling. One key outcome of this analysis is that there are both specific and overlapping functions between all the major modules of the signaling pathway. This suggests a combinatorial function of the modules in optimizing the speed and amplitude of auxin-induced transcription. Our work allows identifying potential functions for homo- and hetero-dimerization of transcriptional regulators, with ARF:IAA, IAA:IAA and ARF:ARF dimerization respectively controlling the amplitude, speed and sensitivity of the response and a synergistic effect of the interaction of IAA with transcriptional repressors on these characteristics of the signaling pathway. Finally, we also suggest experiments which might allow disentangling the structure of the auxin signaling pathway and analysing further its function in plants.

  5. A modular analysis of the auxin signalling network.

    Science.gov (United States)

    Farcot, Etienne; Lavedrine, Cyril; Vernoux, Teva

    2015-01-01

    Auxin is essential for plant development from embryogenesis onwards. Auxin acts in large part through regulation of transcription. The proteins acting in the signalling pathway regulating transcription downstream of auxin have been identified as well as the interactions between these proteins, thus identifying the topology of this network implicating 54 Auxin Response Factor (ARF) and Aux/IAA (IAA) transcriptional regulators. Here, we study the auxin signalling pathway by means of mathematical modeling at the single cell level. We proceed analytically, by considering the role played by five functional modules into which the auxin pathway can be decomposed: the sequestration of ARF by IAA, the transcriptional repression by IAA, the dimer formation amongst ARFs and IAAs, the feedback loop on IAA and the auxin induced degradation of IAA proteins. Focusing on these modules allows assessing their function within the dynamics of auxin signalling. One key outcome of this analysis is that there are both specific and overlapping functions between all the major modules of the signaling pathway. This suggests a combinatorial function of the modules in optimizing the speed and amplitude of auxin-induced transcription. Our work allows identifying potential functions for homo- and hetero-dimerization of transcriptional regulators, with ARF:IAA, IAA:IAA and ARF:ARF dimerization respectively controlling the amplitude, speed and sensitivity of the response and a synergistic effect of the interaction of IAA with transcriptional repressors on these characteristics of the signaling pathway. Finally, we also suggest experiments which might allow disentangling the structure of the auxin signaling pathway and analysing further its function in plants.

  6. Protein Networks in Alzheimer’s Disease

    DEFF Research Database (Denmark)

    Carlsen, Eva Maria Meier; Rasmussen, Rune

    2017-01-01

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

  7. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Science.gov (United States)

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  8. Identification and quantitation of signal molecule-dependent protein phosphorylation

    KAUST Repository

    Groen, Arnoud J.

    2013-09-03

    Phosphoproteomics is a fast-growing field that aims at characterizing phosphorylated proteins in a cell or a tissue at a given time. Phosphorylation of proteins is an important regulatory mechanism in many cellular processes. Gel-free phosphoproteome technique involving enrichment of phosphopeptide coupled with mass spectrometry has proven to be invaluable to detect and characterize phosphorylated proteins. In this chapter, a gel-free quantitative approach involving 15N metabolic labelling in combination with phosphopeptide enrichment by titanium dioxide (TiO2) and their identification by MS is described. This workflow can be used to gain insights into the role of signalling molecules such as cyclic nucleotides on regulatory networks through the identification and quantification of responsive phospho(proteins). © Springer Science+Business Media New York 2013.

  9. Protein-intrinsic and signaling network-based sources of resistance to EGFR- and ErbB family-targeted therapies in head and neck cancer

    OpenAIRE

    Mehra, Ranee; Serebriiskii, Ilya G.; Dunbrack, Roland L.; Robinson, Matthew K.; Burtness, Barbara; Golemis, Erica A.

    2011-01-01

    Agents targeting EGFR and related ErbB family proteins are valuable therapies for the treatment of many cancers. For some tumor types, including squamous cell carcinomas of the head and neck (SCCHN), antibodies targeting EGFR were the first protein-directed agents to show clinical benefit, and remain a standard component of clinical strategies for management of the disease. Nevertheless, many patients display either intrinsic or acquired resistance to these drugs; hence, major research goals ...

  10. Palmitoylated transmembrane adaptor proteins in leukocyte signaling.

    Science.gov (United States)

    Stepanek, Ondrej; Draber, Peter; Horejsi, Vaclav

    2014-05-01

    Transmembrane adaptor proteins (TRAPs) are structurally related proteins that have no enzymatic function, but enable inducible recruitment of effector molecules to the plasma membrane, usually in a phosphorylation dependent manner. Numerous surface receptors employ TRAPs for either propagation or negative regulation of the signal transduction. Several TRAPs (LAT, NTAL, PAG, LIME, PRR7, SCIMP, LST1/A, and putatively GAPT) are known to be palmitoylated that could facilitate their localization in lipid rafts or tetraspanin enriched microdomains. This review summarizes expression patterns, binding partners, signaling pathways, and biological functions of particular palmitoylated TRAPs with an emphasis on the three most recently discovered members, PRR7, SCIMP, and LST1/A. Moreover, we discuss in silico methodology used for discovery of new family members, nature of their binding partners, and microdomain localization. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Magnetoencephalography from signals to dynamic cortical networks

    CERN Document Server

    Aine, Cheryl

    2014-01-01

    "Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...

  12. Concentration dependent model of protein-protein interaction networks

    CERN Document Server

    Zhang, Jingshan

    2007-01-01

    The scale free structure p(k)~k^{-gamma} of protein-protein interaction networks can be produced by a static physical model. We find the earlier study of deterministic threshold models with exponential fitness distributions can be generalized to explain the apparent scale free degree distribution of the physical model, and this explanation provides a generic mechanism of "scale free" networks. We predict the dependence of gamma on experimental protein concentrations. The clustering coefficient distribution of the model is also studied.

  13. Global Optimization for Transport Network Expansion and Signal Setting

    Directory of Open Access Journals (Sweden)

    Haoxiang Liu

    2015-01-01

    Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.

  14. Optimized null model for protein structure networks.

    Science.gov (United States)

    Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa

    2009-06-26

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

  15. Optimized null model for protein structure networks.

    Directory of Open Access Journals (Sweden)

    Tijana Milenković

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

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

    Science.gov (United States)

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

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

  17. Signal propagation in proteins and relation to equilibrium fluctuations.

    Directory of Open Access Journals (Sweden)

    Chakra Chennubhotla

    2007-09-01

    Full Text Available Elastic network (EN models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biological function. We posit that equilibrium motions also determine communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the number of steps it takes on an average to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topological basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have physical origins directly relevant to the equilibrium fluctuations of residues predicted by EN models.

  18. From networks of protein interactions to networks of functional dependencies

    Directory of Open Access Journals (Sweden)

    Luciani Davide

    2012-05-01

    Full Text Available Abstract Background As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation. However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. Results Reasoning that topological features (e.g., clusters of highly inter-connected proteins might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations, based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud or biological processes (e.g., cell budding of the model organism S. cerevisiae. Conclusions The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms.

  19. Evaluation of global differential gene and protein expression in primary Pterygium: S100A8 and S100A9 as possible drivers of a signaling network.

    Science.gov (United States)

    Hou, Aihua; Lan, Wanwen; Law, Kai Pong; Khoo, Ser Chin Jasmine; Tin, Min Qi; Lim, Yoon Pin; Tong, Louis

    2014-01-01

    Pterygium is a wing shaped fibrovascular growth on the ocular surface, characterized by fibrosis, angiogenesis, extracellular matrix remodeling, and inflammatory infiltrates. Epidemiologic studies have linked pterygium formation to various chronic inflammatory conditions, such as ultraviolet radiation, sawdust exposure, and dry eye disease. The purpose of this study is to identify proteins that are differentially expressed in primary pterygium by using a combination of gene microarray and proteomic platforms. Paired pterygium and uninvolved conjunctiva tissues of four patients were evaluated for differences in global gene transcript levels using a genechip microarray. Proteins extracted from another four pairs of tissues were quantified by iTRAQ approach. Western blot and immunofluorescent staining on additional patients were used to validate dysregulated protein expression obtained from microarray and proteomics data. In addition, primary conjunctival fibroblasts were treated with recombinant S100A8, S100A9 or both. Transcript level changes of a panel of potential target genes were evaluated by real time-PCR. The following were up-regulated at both protein and transcript levels S100 A8 and A9, aldehyde dehydrogenase 3 family, member1 (ALDH3A1) and vimentin (VIM). Conversely, serpin peptidase inhibitor clade A member 1 (SERPINA1) and transferrin (TF) were down-regulated. Upon adding S100A8, S100A9 or both, the inflammatory chemokine CXCL1, matrix proteins vimentin, biglycan, and gelsolin, as well as annexin-A2, thymosin-β4, chymase (CMA1), member of Ras oncogene family RAB10 and SERPINA1 were found to be up-regulated. We identified 3 up-regulated and 2 down-regulated proteins by using a stringent approach comparing microarray and proteomic data. On stimulating cells with S100A8/9, a repertoire of key genes found to be up-regulated in pterygium tissue, were induced in these cells. S100A8/9 may be an upstream trigger for inflammation and other disease pathways in

  20. Evaluation of global differential gene and protein expression in primary Pterygium: S100A8 and S100A9 as possible drivers of a signaling network.

    Directory of Open Access Journals (Sweden)

    Aihua Hou

    Full Text Available PURPOSE: Pterygium is a wing shaped fibrovascular growth on the ocular surface, characterized by fibrosis, angiogenesis, extracellular matrix remodeling, and inflammatory infiltrates. Epidemiologic studies have linked pterygium formation to various chronic inflammatory conditions, such as ultraviolet radiation, sawdust exposure, and dry eye disease. The purpose of this study is to identify proteins that are differentially expressed in primary pterygium by using a combination of gene microarray and proteomic platforms. METHODS: Paired pterygium and uninvolved conjunctiva tissues of four patients were evaluated for differences in global gene transcript levels using a genechip microarray. Proteins extracted from another four pairs of tissues were quantified by iTRAQ approach. Western blot and immunofluorescent staining on additional patients were used to validate dysregulated protein expression obtained from microarray and proteomics data. In addition, primary conjunctival fibroblasts were treated with recombinant S100A8, S100A9 or both. Transcript level changes of a panel of potential target genes were evaluated by real time-PCR. RESULTS: The following were up-regulated at both protein and transcript levels S100 A8 and A9, aldehyde dehydrogenase 3 family, member1 (ALDH3A1 and vimentin (VIM. Conversely, serpin peptidase inhibitor clade A member 1 (SERPINA1 and transferrin (TF were down-regulated. Upon adding S100A8, S100A9 or both, the inflammatory chemokine CXCL1, matrix proteins vimentin, biglycan, and gelsolin, as well as annexin-A2, thymosin-β4, chymase (CMA1, member of Ras oncogene family RAB10 and SERPINA1 were found to be up-regulated. CONCLUSIONS: We identified 3 up-regulated and 2 down-regulated proteins by using a stringent approach comparing microarray and proteomic data. On stimulating cells with S100A8/9, a repertoire of key genes found to be up-regulated in pterygium tissue, were induced in these cells. S100A8/9 may be an upstream

  1. Mining protein networks for synthetic genetic interactions

    Directory of Open Access Journals (Sweden)

    Zhao Shan

    2008-10-01

    Full Text Available Abstract Background The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified. Results We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. Conclusion Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein

  2. MANET: tracing evolution of protein architecture in metabolic networks

    Directory of Open Access Journals (Sweden)

    Caetano-Anollés Gustavo

    2006-07-01

    Full Text Available Abstract Background Cellular metabolism can be characterized by networks of enzymatic reactions and transport processes capable of supporting cellular life. Our aim is to find evolutionary patterns and processes embedded in the architecture and function of modern metabolism, using information derived from structural genomics. Description The Molecular Ancestry Network (MANET project traces evolution of protein architecture in biomolecular networks. We describe metabolic MANET, a database that links information in the Structural Classification of Proteins (SCOP, the Kyoto Encyclopedia of Genes and Genomes (KEGG, and phylogenetic reconstructions depicting the evolution of protein fold architecture. Metabolic MANET literally 'paints' the ancestries of enzymes derived from rooted phylogenomic trees directly onto over one hundred metabolic subnetworks, enabling the study of evolutionary patterns at global and local levels. An initial analysis of painted subnetworks reveals widespread enzymatic recruitment and an early origin of amino acid metabolism. Conclusion MANET maps evolutionary relationships directly and globally onto biological networks, and can generate and test hypotheses related to evolution of metabolism. We anticipate its use in the study of other networks, such as signaling and other protein-protein interaction networks.

  3. RMOD: a tool for regulatory motif detection in signaling network.

    Directory of Open Access Journals (Sweden)

    Jinki Kim

    Full Text Available Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

  4. RMOD: a tool for regulatory motif detection in signaling network.

    Science.gov (United States)

    Kim, Jinki; Yi, Gwan-Su

    2013-01-01

    Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

  5. Mapping of protein-protein interaction network of Alexander disease.

    Science.gov (United States)

    Saxena, A K; Saxena, V L; Dixit, S

    2016-05-30

    Alexander disease (ALXD) is slowly progressive neurodegenerative disorder which affects white matter of the central nervous system. The main cause of disorder is mutation in GFAP gene and mutation in some other genes were also reported. This study was aimed at getting a better insight into ALXD pathogenesis and identifying the important functional and highly interconnected nodes in human protein interaction network, identifying the important sub-networks in the system could be helpful in understanding the underlying molecular mechanism. The topological analysis of human protein interaction network strategy to identify highly interconnected sub-network modules from which six proteins are found i.e. GFAP, PLEC, CRYAB, NDUFV1, CASP3 and MAPK14 plays important role in disease. Further, the enrichment analysis of interaction network identifies crucial pathways in which most of the diseased proteins overlaps. Through system biology approach, the undirected human protein interaction network of ALXD is buildup with the help of Cytoscape tool and its various plugins helps to investigate network further. The systematic approach suggests the finding of previously known proteins, GFAP, PLEC, CRYAB, NDUFV1, CASP3 and MAPK14 can be used as a drug targets and potential treatment discovered also enrichment analysis will provide guidance for the future study on Alexander disease.

  6. Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

    Science.gov (United States)

    Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

    2017-03-01

    Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological

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

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

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

  8. Signaling networks: information flow, computation, and decision making.

    Science.gov (United States)

    Azeloglu, Evren U; Iyengar, Ravi

    2015-04-01

    Signaling pathways come together to form networks that connect receptors to many different cellular machines. Such networks not only receive and transmit signals but also process information. The complexity of these networks requires the use of computational models to understand how information is processed and how input-output relationships are determined. Two major computational approaches used to study signaling networks are graph theory and dynamical modeling. Both approaches are useful; network analysis (application of graph theory) helps us understand how the signaling network is organized and what its information-processing capabilities are, whereas dynamical modeling helps us determine how the system changes in time and space upon receiving stimuli. Computational models have helped us identify a number of emergent properties that signaling networks possess. Such properties include ultrasensitivity, bistability, robustness, and noise-filtering capabilities. These properties endow cell-signaling networks with the ability to ignore small or transient signals and/or amplify signals to drive cellular machines that spawn numerous physiological functions associated with different cell states. Copyright © 2015 Cold Spring Harbor Laboratory Press; all rights reserved.

  9. Regulation of G protein-coupled receptor signalling: focus on the cardiovascular system and regulator of G protein signalling proteins.

    Science.gov (United States)

    Hendriks-Balk, Mariëlle C; Peters, Stephan L M; Michel, Martin C; Alewijnse, Astrid E

    2008-05-13

    G protein-coupled receptors (GPCRs) are involved in many biological processes. Therefore, GPCR function is tightly controlled both at receptor level and at the level of signalling components. Well-known mechanisms by which GPCR function can be regulated comprise desensitization/resensitization processes and GPCR up- and downregulation. GPCR function can also be regulated by several proteins that directly interact with the receptor and thereby modulate receptor activity. An additional mechanism by which receptor signalling is regulated involves an emerging class of proteins, the so-called regulators of G protein signalling (RGS). In this review we will describe some of these control mechanisms in more detail with some specific examples in the cardiovascular system. In addition, we will provide an overview on RGS proteins and the involvement of RGS proteins in cardiovascular function.

  10. Anti-apoptotic ARC protein confers chemoresistance by controlling leukemia-microenvironment interactions through a NFκB/IL1β signaling network

    KAUST Repository

    Carter, Bing Z.

    2016-04-11

    To better understand how the apoptosis repressor with caspase recruitment domain (ARC) protein confers drug resistance in acute myeloid leukemia (AML), we investigated the role of ARC in regulating leukemia-mesenchymal stromal cell (MSC) interactions. In addition to the previously reported effect on AML apoptosis, we have demonstrated that ARC enhances migration and adhesion of leukemia cells to MSCs both in vitro and in a novel human extramedullary bone/bone marrow mouse model. Mechanistic studies revealed that ARC induces IL1β expression in AML cells and increases CCL2, CCL4, and CXCL12 expression in MSCs, both through ARC-mediated activation of NFκB. Expression of these chemokines in MSCs increased by AML cells in an ARC/IL1β-dependent manner; likewise, IL1β expression was elevated when leukemia cells were co-cultured with MSCs. Further, cells from AML patients expressed the receptors for and migrated toward CCL2, CCL4, and CXCL12. Inhibition of IL1β suppressed AML cell migration and sensitized the cells co-cultured with MSCs to chemotherapy. Our results suggest the existence of a complex ARC-regulated circuit that maintains intimate connection of AML with the tumor microenvironment through NFκB/IL1β-regulated chemokine receptor/ligand axes and reciprocal crosstalk resulting in cytoprotection. The data implicate ARC as a promising drug target to potentially sensitize AML cells to chemotherapy.

  11. Using Artificial Neural Networks for ECG Signals Denoising

    Directory of Open Access Journals (Sweden)

    Zoltán Germán-Salló

    2010-12-01

    Full Text Available The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1th sample from n previous samples To train and adjust the network weights, the backpropagation (BP algorithm was used. In this paper, prediction of ECG signals (as time series using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.

  12. Modeling evolution of crosstalk in noisy signal transduction networks

    Science.gov (United States)

    Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

    2018-02-01

    Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.

  13. Dopamine D1 signaling organizes network dynamics underlying working memory.

    Science.gov (United States)

    Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

    2016-06-01

    Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.

  14. Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

    Directory of Open Access Journals (Sweden)

    Ryan Suderman

    Full Text Available Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively

  15. Computational study of noise in a large signal transduction network

    Directory of Open Access Journals (Sweden)

    Ruohonen Keijo

    2011-06-01

    Full Text Available Abstract Background Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Results We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. Conclusions We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.

  16. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

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

  17. Information flow in a network of dispersed signalers-receivers

    Science.gov (United States)

    Halupka, Konrad

    2017-11-01

    I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.

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

    CERN Document Server

    Hu, Fei

    2012-01-01

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

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Oleksii Kuchaiev

    2009-08-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  2. Multisite phosphorylation networks as signal processors for Cdk1.

    Science.gov (United States)

    Kõivomägi, Mardo; Ord, Mihkel; Iofik, Anna; Valk, Ervin; Venta, Rainis; Faustova, Ilona; Kivi, Rait; Balog, Eva Rose M; Rubin, Seth M; Loog, Mart

    2013-12-01

    The order and timing of cell-cycle events is controlled by changing substrate specificity and different activity thresholds of cyclin-dependent kinases (CDKs). However, it is not understood how a single protein kinase can trigger hundreds of switches in a sufficiently time-resolved fashion. We show that cyclin-Cdk1-Cks1-dependent phosphorylation of multisite targets in Saccharomyces cerevisiae is controlled by key substrate parameters including distances between phosphorylation sites, distribution of serines and threonines as phosphoacceptors and positioning of cyclin-docking motifs. The component mediating the key interactions in this process is Cks1, the phosphoadaptor subunit of the cyclin-Cdk1-Cks1 complex. We propose that variation of these parameters within networks of phosphorylation sites in different targets provides a wide range of possibilities for differential amplification of Cdk1 signals, thus providing a mechanism to generate a wide range of thresholds in the cell cycle.

  3. Protein phosphorylation in bcterial signaling and regulation

    KAUST Repository

    Mijakovic, Ivan

    2016-01-26

    In 2003, it was demonstrated for the first time that bacteria possess protein-tyrosine kinases (BY-kinases), capable of phosphorylating other cellular proteins and regulating their activity. It soon became apparent that these kinases phosphorylate a number of protein substrates, involved in different cellular processes. More recently, we found out that BY-kinases can be activated by several distinct protein interactants, and are capable of engaging in cross-phosphorylation with other kinases. Evolutionary studies based on genome comparison indicate that BY-kinases exist only in bacteria. They are non-essential (present in about 40% bacterial genomes), and their knockouts lead to pleiotropic phenotypes, since they phosphorylate many substrates. Surprisingly, BY-kinase genes accumulate mutations at an increased rate (non-synonymous substitution rate significantly higher than other bacterial genes). One direct consequence of this phenomenon is no detectable co-evolution between kinases and their substrates. Their promiscuity towards substrates thus seems to be “hard-wired”, but why would bacteria maintain such promiscuous regulatory devices? One explanation is the maintenance of BY-kinases as rapidly evolving regulators, which can readily adopt new substrates when environmental changes impose selective pressure for quick evolution of new regulatory modules. Their role is clearly not to act as master regulators, dedicated to triggering a single response, but they might rather be employed to contribute to fine-tuning and improving robustness of various cellular responses. This unique feature makes BY-kinases a potentially useful tool in synthetic biology. While other bacterial kinases are very specific and their signaling pathways insulated, BY-kinase can relatively easily be engineered to adopt new substrates and control new biosynthetic processes. Since they are absent in humans, and regulate some key functions in pathogenic bacteria, they are also very promising

  4. Network compression as a quality measure for protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Loic Royer

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

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  7. Modularity detection in protein-protein interaction networks.

    Science.gov (United States)

    Narayanan, Tejaswini; Gersten, Merril; Subramaniam, Shankar; Grama, Ananth

    2011-12-29

    Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, C.Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the application of those measures to

  8. Patterns of human gene expression variance show strong associations with signaling network hierarchy.

    Science.gov (United States)

    Komurov, Kakajan; Ram, Prahlad T

    2010-11-12

    Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV) of genes and their relationship to cellular functions and physiological responses is poorly understood. To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes. Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.

  9. Patterns of human gene expression variance show strong associations with signaling network hierarchy

    Directory of Open Access Journals (Sweden)

    Ram Prahlad T

    2010-11-01

    Full Text Available Abstract Background Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV of genes and their relationship to cellular functions and physiological responses is poorly understood. Results To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes. Conclusion Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.

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

    DEFF Research Database (Denmark)

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

    2010-01-01

    BACKGROUND: Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different......-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs...

  11. Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks.

    Directory of Open Access Journals (Sweden)

    Song Feng

    2016-05-01

    Full Text Available Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications.

  12. Protein interaction networks as metric spaces: a novel perspective on distribution of hubs.

    Science.gov (United States)

    Fadhal, Emad; Gamieldien, Junaid; Mwambene, Eric C

    2014-01-18

    In the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located. By analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance. We conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping' functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence

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

    Science.gov (United States)

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

    2017-05-01

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

  14. G protein signaling in plants: minus times minus equals plus.

    Science.gov (United States)

    Stateczny, Dave; Oppenheimer, Jara; Bommert, Peter

    2016-12-01

    Heterotrimeric G proteins are key regulators in the transduction of extracellular signals both in animals and plants. In plants, heterotrimeric G protein signaling plays essential roles in development and in response to biotic and abiotic stress. However, over the last decade it has become clear that plants have unique mechanisms of G protein signaling. Although plants share most of the core components of heterotrimeric G proteins, some of them exhibit unusual properties compared to their animal counterparts. In addition, plants do not share functional GPCRs. Therefore the well-established paradigm of the animal G protein signaling cycle is not applicable in plants. In this review, we summarize recent insights into these unique mechanisms of G protein signaling in plants with special focus on the evident potential of G protein signaling as a target to modify developmental and physiological parameters important for yield increase. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Jafari, Mohieddin; Mirzaie, Mehdi; Sadeghi, Mehdi

    2015-10-05

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

  16. Integrative signaling networks of membrane guanylate cyclases: Biochemistry and physiology

    Directory of Open Access Journals (Sweden)

    Rameshwar K Sharma

    2016-09-01

    Full Text Available This monograph presents a historical perspective of cornerstone developments on the biochemistry and physiology of mammalian membrane guanylate cyclases (MGCs, highlighting contributions made by the authors and their collaborators. Upon resolution of early, contentious studies, cyclic GMP emerged, alongside cyclic AMP, as an important intracellular second messenger for hormonal signaling. However, the two signaling pathways differ in significant ways. In the cyclic AMP pathway, hormone binding to a G protein coupled receptor leads to stimulation or inhibition of an adenylate cyclase, whereas the cyclic GMP pathway dispenses with intermediaries; hormone binds to an MGC to affect its activity. Although the cyclic GMP pathway is direct, it is by no means simple. The modular design of the molecule incorporates regulation by ATP binding and phosphorylation. MGCs can form complexes with Ca2+-sensing subunits that either increase or decrease cyclic GMP synthesis, depending on subunit identity. In some systems, co-expression of two Ca2+ sensors, GCAP1 and S100B with ROS-GC1 confers bimodal signaling marked by increases in cyclic GMP synthesis when intracellular Ca2+ concentration rises or falls. Some MGCs monitor or are modulated by carbon dioxide via its conversion to bicarbonate. One MGC even functions as a thermosensor as well as a chemosensor; activity reaches a maximum with a mild drop in temperature. The complexity afforded by these multiple limbs of operation enables MGC networks to perform transductions traditionally reserved for G protein coupled receptors and TRP (Transient Receptor Potential channels and to serve a diverse array of functions, including control over cardiac vasculature, smooth muscle relaxation, blood pressure regulation, cellular growth, sensory transductions, neural plasticity and memory.

  17. Data management of protein interaction networks

    CERN Document Server

    Cannataro, Mario

    2012-01-01

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

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

    Science.gov (United States)

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

    2015-01-16

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

  19. Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

    Directory of Open Access Journals (Sweden)

    Korf Ulrike

    2011-07-01

    Full Text Available Abstract Background Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. Results We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. Conclusions The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.

  20. A survey of conformational and energetic changes in G protein signaling

    Directory of Open Access Journals (Sweden)

    Alyssa D. Lokits

    2015-11-01

    Full Text Available Cell signaling is a fundamental process for all living organisms. G protein-coupled receptors (GPCRs are a large and diverse group of transmembrane receptors which convert extracellular signals into intracellular responses primarily via coupling to heterotrimeric G proteins. In order to integrate the range of very diverse extracellular signals into a message the cell can recognize and respond to, conformational changes occur that rewire the interactions between the receptor and heterotrimer in a specific and coordinated manner. By interrogating the energetics of these interactions within the individual proteins and across protein-protein interfaces, a communication network between amino acids involved in conformational changes for signaling, is created. To construct this mapping of pairwise interactions in silico, we analyzed the Rhodopsin GPCR coupled to a Gαi1β1γ1 heterotrimer. The structure of this G protein complex was modeled in the receptor-bound and unbound heterotrimeric states as well as the activated, monomeric Gα(GTP state. From these tertiary structural models, we computed the average pairwise residue-residue interactions and interface energies across ten models of each state using the ROSETTA modeling software suite. Here we disseminate a comprehensive survey of all critical interactions and create intra-protein network communication maps. These networks represent nodes of interaction necessary for G protein activation.

  1. Optimizing Signal Behavior of Femtocells for Improved Network

    Directory of Open Access Journals (Sweden)

    Meera Joseph

    2016-10-01

    Full Text Available The high demand for network coverage in an indoor setting brought about the acceptance of femtocell technology as a solution using the backhaul connectivity in the existing network. The quality of signal, voice calling, Internet, security and data are improved through the use femtocell at the indoor environment. Here the service provider attempts to reduce their operation cost by presenting self-organizing mechanisms for optimization of the network. The remarkable part is that, femtocells improves coverage, enhances the data rate at the indoor environment. Therefore, the challenges of the femtocell also known as interference deteriorates the capacity and quality performance of the whole cellular network. In this paper we simulate the bit error rate against signal behaviour at the indoor environment and we also simulate the transmitting power over signal for both macrocells and femtocells. We focus on the transmitting power that might cause interference within the cellular network.

  2. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    A report is presented on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal...... understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given......, and an explanation facility designed to help neural signal understanding is described. The results are compared to those obtained with a knowledge-based signal interpretation system using the same instrument and data...

  3. Experimental evolution of protein-protein interaction networks.

    Science.gov (United States)

    Kaçar, Betül; Gaucher, Eric A

    2013-08-01

    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.

  4. Finding local communities in protein networks.

    Science.gov (United States)

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

    2009-09-18

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

  5. Finding local communities in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-09-01

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

  6. Hybrid digital signal processing and neural networks applications in PWRs

    Energy Technology Data Exchange (ETDEWEB)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-12-31

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications.

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

    Science.gov (United States)

    Poirot, Olivier; Timsit, Youri

    2016-05-01

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

  8. Primary Cilia, Signaling Networks and Cell Migration

    DEFF Research Database (Denmark)

    Veland, Iben Rønn

    composition of receptors and signal components in the cilium to regulate cellular processes such as transcriptional control or cytoskeletal reorganization. This dissertation focuses on selected signaling systems regulated by the primary cilium, including the PDGFRα, TGFβ and Wnt pathways, and how......, which leads to uncontrolled cell movements. Together, the results obtained from my PhD studies reflect the high level of complexity within signaling systems regulated by the primary cilium that control cellular processes during embryonic development and in tissue homeostasis. As such, this dissertation......-dependent regulation of signal transduction. Upon ligand-binding and activation in the cilium, TGFβ receptors accumulate and are internalized at the ciliary base together with Smad2/3 transcription factors that are phosphorylated here and translocated to the nucleus for target gene expression. These processes depend...

  9. Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

    Science.gov (United States)

    2009-03-01

    Jordan JD, Landau EM, Iyengar R (2000) Signaling networks: the origins of cellular multitasking. Cell 103: 193–200. 53. Eungdamrong NJ, Iyengar R...I, Shamir R (2009) Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 25: 1158–1164

  10. Protein conservation and variation suggest mechanisms of cell type-specific modulation of signaling pathways.

    Directory of Open Access Journals (Sweden)

    Martin H Schaefer

    2014-06-01

    Full Text Available Many proteins and signaling pathways are present in most cell types and tissues and yet perform specialized functions. To elucidate mechanisms by which these ubiquitous pathways are modulated, we overlaid information about cross-cell line protein abundance and variability, and evolutionary conservation onto functional pathway components and topological layers in the pathway hierarchy. We found that the input (receptors and the output (transcription factors layers evolve more rapidly than proteins in the intermediary transmission layer. In contrast, protein expression variability decreases from the input to the output layer. We observed that the differences in protein variability between the input and transmission layer can be attributed to both the network position and the tendency of variable proteins to physically interact with constitutively expressed proteins. Differences in protein expression variability and conservation are also accompanied by the tendency of conserved and constitutively expressed proteins to acquire somatic mutations, while germline mutations tend to occur in cell type-specific proteins. Thus, conserved core proteins in the transmission layer could perform a fundamental role in most cell types and are therefore less tolerant to germline mutations. In summary, we propose that the core signal transmission machinery is largely modulated by a variable input layer through physical protein interactions. We hypothesize that the bow-tie organization of cellular signaling on the level of protein abundance variability contributes to the specificity of the signal response in different cell types.

  11. Decoding signalling networks by mass spectrometry-based proteomics

    DEFF Research Database (Denmark)

    Choudhary, Chuna Ram; Mann, Matthias

    2010-01-01

    Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system...

  12. Social multimedia signals a signal processing approach to social network phenomena

    CERN Document Server

    Roy, Suman Deb

    2014-01-01

    This book provides a comprehensive coverage of the state-of-the-art in understanding media popularity and trends in online social networks through social multimedia signals. With insights from the study of popularity and sharing patterns of online media, trend spread in social media, social network analysis for multimedia and visualizing diffusion of media in online social networks. In particular, the book will address the following important issues: Understanding social network phenomena from a signal processing point of view; The existence and popularity of multimedia as shared and social me

  13. Quantitative Imaging of Lymphocyte Membrane Protein Reorganization and Signaling

    OpenAIRE

    Kasson, Peter M.; Huppa, Johannes B.; Krogsgaard, Michelle; Davis, Mark M.; Brunger, Axel T.

    2005-01-01

    Changes in membrane protein localization are critical to establishing cell polarity and regulating cell signaling. Fluorescence microscopy of labeled proteins allows visualization of these changes, but quantitative analysis is needed to study this aspect of cell signaling in full mechanistic detail. We have developed a novel approach for quantitative assessment of membrane protein redistribution based on four-dimensional video microscopy of fluorescently labeled proteins. Our analytic system ...

  14. Signal-transducing proteins for nanoelectronics.

    Science.gov (United States)

    Pichierri, Fabio

    2006-12-01

    This aim of this article is to provide novel paradigms for 21st century nanoelectronics by taking inspiration from the biology of signal transduction events where Nature has solved many complex problems, particularly those concerned with signal integration and amplification.

  15. Protein phosphatase 2A: the Trojan Horse of cellular signaling.

    Science.gov (United States)

    Sontag, E

    2001-01-01

    Dynamic phosphorylation and dephosphorylation of proteins are fundamental mechanisms utilized by cells to transduce signals. Whereas transduction by protein kinases has been a major focus of studies in the last decade, protein phosphatase 2A (PP2A) enzymes emerge in this millenium as the most fashionable players in cellular signaling. Viral proteins target specific PP2A enzymes in order to deregulate chosen cellular pathways in the host and promote viral progeny. The observation that a variety of viruses utilize PP2A to alienate cellular behavior emphasizes the fundamental importance of PP2A in signal transduction. This review will primarily focus on discussing the uniqueness of PP2A regulation and uncovering the critical role played by protein-protein interactions in the modulation of PP2A signaling. Moreover, the place of PP2A in signaling pathways and its functional significance for human diseases will be discussed.

  16. Inferring protein function by domain context similarities in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Sun Zhirong

    2009-12-01

    Full Text Available Abstract Background Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions. Results The domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves. Conclusion This paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods.

  17. The relationship between modularity and robustness in signalling networks.

    Science.gov (United States)

    Tran, Tien-Dzung; Kwon, Yung-Keun

    2013-11-06

    Many biological networks tend to have a high modularity structural property and the dynamic characteristic of high robustness against perturbations. However, the relationship between modularity and robustness is not well understood. To investigate this relationship, we examined real signalling networks and conducted simulations using a random Boolean network model. As a result, we first observed that the network robustness is negatively correlated with the network modularity. In particular, this negative correlation becomes more apparent as the network density becomes sparser. Even more interesting is that, the negative relationship between the network robustness and the network modularity occurs mainly because nodes in the same module with the perturbed node tend to be more sensitive to the perturbation than those in other modules. This result implies that dynamically similar nodes tend to be located in the same module of a network. To support this, we show that a pair of genes associated with the same disease or a pair of functionally similar genes is likely to belong to the same module in a human signalling network.

  18. Signaling network dynamics investigated by quantitative phosphoproteomics

    NARCIS (Netherlands)

    Giansanti, Piero

    2014-01-01

    This thesis describes the application of proteomics technologies to get insight into several aspects of phosphorylation signaling dynamics. The core tool in all performed experiments is mass spectrometry (MS)-based phosphoproteomics. In Chapter 1, a general introduction is given into proteomics and

  19. Towards blueprints for network architecture, biophysical dynamics and signal transduction.

    Science.gov (United States)

    Coombes, Stephen; Doiron, Brent; Josić, Kresimir; Shea-Brown, Eric

    2006-12-15

    We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.

  20. Techniques for labeling of optical signals in bust switched networks

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Koonen, A. M. J.; Zhang, Jianfeng

    2003-01-01

    We present a review of significant issues related to labeled optical burst switched (LOBS) networks and technologies enabling future optical internet networks. Labeled optical burst switching provides a quick and efficient forwarding mechanism of IP packets/bursts over wavelength division...... multiplexed (WDM) networks due to its single forwarding algorithm, thus yielding low latency, and it enables scaling to terabit rates. Moreover, LOBS is compatible with the general multiprotocol label switching (GMPLS) framework for a unified control plane. We present a review on techniques for labeling...... of optical signals for LOBS networks, including experimental results, we discuss as well issues for further research....

  1. Experimental and computational tools for analysis of signaling networks in primary cells

    DEFF Research Database (Denmark)

    Schoof, Erwin M; Linding, Rune

    2014-01-01

    , or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein-protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge......; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network...

  2. Plant gravitropic signal transduction: A network analysis leads to gene discovery

    Science.gov (United States)

    Wyatt, Sarah

    Gravity plays a fundamental role in plant growth and development. Although a significant body of research has helped define the events of gravity perception, the role of the plant growth regulator auxin, and the mechanisms resulting in the gravity response, the events of signal transduction, those that link the biophysical action of perception to a biochemical signal that results in auxin redistribution, those that regulate the gravitropic effects on plant growth, remain, for the most part, a “black box.” Using a cold affect, dubbed the gravity persistent signal (GPS) response, we developed a mutant screen to specifically identify components of the signal transduction pathway. Cloning of the GPS genes have identified new proteins involved in gravitropic signaling. We have further exploited the GPS response using a multi-faceted approach including gene expression microarrays, proteomics analysis, and bioinformatics analysis and continued mutant analysis to identified additional genes, physiological and biochemical processes. Gene expression data provided the foundation of a regulatory network for gravitropic signaling. Based on these gene expression data and related data sets/information from the literature/repositories, we constructed a gravitropic signaling network for Arabidopsis inflorescence stems. To generate the network, both a dynamic Bayesian network approach and a time-lagged correlation coefficient approach were used. The dynamic Bayesian network added existing information of protein-protein interaction while the time-lagged correlation coefficient allowed incorporation of temporal regulation and thus could incorporate the time-course metric from the data set. Thus the methods complemented each other and provided us with a more comprehensive evaluation of connections. Each method generated a list of possible interactions associated with a statistical significance value. The two networks were then overlaid to generate a more rigorous, intersected

  3. SOCS proteins in regulation of receptor tyrosine kinase signaling

    DEFF Research Database (Denmark)

    Kazi, Julhash U.; Kabir, Nuzhat N.; Flores Morales, Amilcar

    2014-01-01

    proteins, SOCS1-7, and cytokine-inducible SH2-containing protein (CIS). A key feature of this family of proteins is the presence of an SH2 domain and a SOCS box. Recent studies suggest that SOCS proteins also play a role in RTK signaling. Activation of RTK results in transcriptional activation of SOCS......-encoding genes. These proteins associate with RTKs through their SH2 domains and subsequently recruit the E3 ubiquitin machinery through the SOCS box, and thereby limit receptor stability by inducing ubiquitination. In a similar fashion, SOCS proteins negatively regulate mitogenic signaling by RTKs. It is also...

  4. Systematic discovery of new recognition peptides mediating protein interaction networks.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP. Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues, and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a KD of 22 microM and a VxxxRxYS motif that binds Translin with a KD of 43 microM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.

  5. Inferring cell-scale signalling networks via compressive sensing.

    Directory of Open Access Journals (Sweden)

    Lei Nie

    Full Text Available Signalling network inference is a central problem in system biology. Previous studies investigate this problem by independently inferring local signalling networks and then linking them together via crosstalk. Since a cellular signalling system is in fact indivisible, this reductionistic approach may have an impact on the accuracy of the inference results. Preferably, a cell-scale signalling network should be inferred as a whole. However, the holistic approach suffers from three practical issues: scalability, measurement and overfitting. Here we make this approach feasible based on two key observations: 1 variations of concentrations are sparse due to separations of timescales; 2 several species can be measured together using cross-reactivity. We propose a method, CCELL, for cell-scale signalling network inference from time series generated by immunoprecipitation using Bayesian compressive sensing. A set of benchmark networks with varying numbers of time-variant species is used to demonstrate the effectiveness of our method. Instead of exhaustively measuring all individual species, high accuracy is achieved from relatively few measurements.

  6. Emerging connections in the ethylene signaling network

    OpenAIRE

    Yoo, Sang-Dong; Cho, Younghee; Sheen, Jen

    2009-01-01

    The gaseous plant hormone ethylene acts as a pivotal mediator to respond to and coordinate internal and external cues in modulating plant growth dynamics and developmental programs. Genetic analysis of Arabidopsis thaliana has been used to identify key components and to build a linear ethylene-signaling pathway from the receptors through to the nuclear transcription factors. Studies applying integrative approaches have revealed new regulators, molecular connections and mechanisms in ethylene ...

  7. Regulator of G-protein signaling - 5 (RGS5 is a novel repressor of hedgehog signaling.

    Directory of Open Access Journals (Sweden)

    William M Mahoney

    Full Text Available Hedgehog (Hh signaling plays fundamental roles in morphogenesis, tissue repair, and human disease. Initiation of Hh signaling is controlled by the interaction of two multipass membrane proteins, patched (Ptc and smoothened (Smo. Recent studies identify Smo as a G-protein coupled receptor (GPCR-like protein that signals through large G-protein complexes which contain the Gαi subunit. We hypothesize Regulator of G-Protein Signaling (RGS proteins, and specifically RGS5, are endogenous repressors of Hh signaling via their ability to act as GTPase activating proteins (GAPs for GTP-bound Gαi, downstream of Smo. In support of this hypothesis, we demonstrate that RGS5 over-expression inhibits sonic hedgehog (Shh-mediated signaling and osteogenesis in C3H10T1/2 cells. Conversely, signaling is potentiated by siRNA-mediated knock-down of RGS5 expression, but not RGS4 expression. Furthermore, using immuohistochemical analysis and co-immunoprecipitation (Co-IP, we demonstrate that RGS5 is present with Smo in primary cilia. This organelle is required for canonical Hh signaling in mammalian cells, and RGS5 is found in a physical complex with Smo in these cells. We therefore conclude that RGS5 is an endogenous regulator of Hh-mediated signaling and that RGS proteins are potential targets for novel therapeutics in Hh-mediated diseases.

  8. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

    Science.gov (United States)

    Samarasinghe, S; Ling, H

    In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced

  9. Learning Signaling Network Structures with Sparsely Distributed Data

    OpenAIRE

    Sachs, Karen; Itani, Solomon; Carlisle, Jennifer; Nolan, Garry P.; Pe'er, Dana; Lauffenburger, Douglas A.

    2009-01-01

    Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, f...

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

    Science.gov (United States)

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

    2015-05-15

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

  11. Experimental evolution of protein?protein interaction networks

    OpenAIRE

    Ka?ar, Bet?l; 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 molec...

  12. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

    Directory of Open Access Journals (Sweden)

    Ananthi Jebaseeli Samuelraj

    2015-01-01

    Full Text Available Proxy Mobile IPV6 (PMIPV6 is a network based mobility management protocol which supports node’s mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node’s mobility should be modified to support group nodes’ mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point.

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

    Directory of Open Access Journals (Sweden)

    Guang Hu

    2017-01-01

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

  14. 1st International Conference on Signal, Networks, Computing, and Systems

    CERN Document Server

    Mohapatra, Durga; Nagar, Atulya; Sahoo, Manmath

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the first International Conference on Signal, Networks, Computing, and Systems (ICSNCS 2016) held at Jawaharlal Nehru University, New Delhi, India during February 25–27, 2016. The book is organized in to two volumes and primarily focuses on theory and applications in the broad areas of communication technology, computer science and information security. The book aims to bring together the latest scientific research works of academic scientists, professors, research scholars and students in the areas of signal, networks, computing and systems detailing the practical challenges encountered and the solutions adopted.

  15. Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

    Science.gov (United States)

    Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

    2017-01-01

    Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

  16. Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

    Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

  17. The role of ras proteins in insulin signal transduction

    NARCIS (Netherlands)

    Maassen, J.A.; Burgering, B.M.T.; Medema, R.H.; Osterop, A.P.R.M.; Zon, G.C.M. van der; Möller, W.; Bos, J.L.

    1992-01-01

    Ras-proteins are guanine nucleotide binding proteins, which, in the GTP bound state emit a strong mitogenic signal. In the GDP bound state, the protein appears inactive. We have found that stimulation by insulin of cells expressing elevated levels of insulin receptors results in a rapid conversion

  18. Glucose responsive hydrogel networks based on protein recognition.

    Science.gov (United States)

    Ehrick, Jason D; Luckett, Matthew R; Khatwani, Santoshkumar; Wei, Yinan; Deo, Sapna K; Bachas, Leonidas G; Daunert, Sylvia

    2009-09-09

    Stimuli-responsive materials capable of manifesting physical changes in response to environmental signals are valuable tools for use in a variety of biomedical applications. Herein we describe one such smart glucose-responsive hydrogel material prepared by immobilizing the glucose/galactose binding protein within an acrylamide hydrogel network. This hydrogel demonstrates a quantitative "accordion"-like dynamic response in the presence of glucose. We further show the feasibility of employing this responsive smart material as a gating agent for controlled drug delivery, thus, demonstrating that these hydrogels may eventually lead to the development of implantable drug delivery systems for diabetes management applications.

  19. Use of artificial neural networks in biosensor signal classification

    Directory of Open Access Journals (Sweden)

    Vlastimil Dohnal

    2008-01-01

    Full Text Available Biosensors are analytical devices that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytical signal and that utilizes a biochemical mechanism for the chemical recognition. The complexity of biosensor construction and generation of measured signal requires the development of new method for signal eva­luation and its possible defects recognition. A new method based on artificial neural networks (ANN was developed for recognition of characteristic behavior of signals joined with malfunction of sensor. New algorithm uses unsupervised Kohonen self-organizing neural networks. The work with ANN has two phases – adaptation and prediction. During the adaptation step the classification model is build. Measured data form groups after projection into two-dimensional space based on theirs similarity. After identification of these groups and establishing the connection with signal disorders ANN can be used for evaluation of newly measured signals. This algorithm was successfully applied for 540 signal classification obtained from immobilized acetylcholinesterase biosensor measurement of organophosphate and carbamate pesticides in vegetables, fruits, spices, potatoes and soil samples. From six different signal defects were successfully classified four – low response after substrate addition, equilibration at high values, slow equilibration after substrate addition respectively low sensitivity on syntostigmine.

  20. Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

    Directory of Open Access Journals (Sweden)

    Andre Terzic

    2009-04-01

    Full Text Available Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7 are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network.

  1. Mapping the follicle-stimulating hormone-induced signalling networks

    Directory of Open Access Journals (Sweden)

    Pauline eGloaguen

    2011-10-01

    Full Text Available Follicle-stimulating hormone (FSH is a central regulator of male and female reproductive function. Over the last decade, there has been a growing perception of the complexity associated with FSH-induced cellular signalling. It is now clear that the canonical Gs/cAMP/PKA pathway is not the sole mechanism that must be considered in FSH biological actions. In parallel, consistent with the emerging concept of biased agonism, several examples of ligand-mediated selective signalling pathway activation by gonadotropin receptors have been reported. In this context, it is important to gain an integrative view of the signalling pathways induced by FSH and how they interconnect to form a network. In this review, we propose a first attempt at building topological maps of various pathways known to be involved in the FSH-induced signalling network. We discuss the multiple facets of FSH-induced signalling and how they converge to the hormone integrated biological response. Despite of their incompleteness, these maps of the FSH-induced signalling network represent a first step towards gaining a system-level comprehension of this hormone’s actions, which may ultimately facilitate the discovery of novel regulatory processes and therapeutic strategies for infertilities and non-steroidal contraception.

  2. The fidelity of dynamic signaling by noisy biomolecular networks.

    Directory of Open Access Journals (Sweden)

    Clive G Bowsher

    Full Text Available Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.

  3. Temporal protein expression pattern in intracellular signalling ...

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... 1. Introduction. Exhibition of diverse patterns in the biological world has been ... molecular biology, genomics and proteomics experiments have come up with ..... proteins at 0, 2, 4 and 6 h, (B) temporal protein expression pattern observed in synchronous update up to 21 time points (0 to 10 h), (C) temporal ...

  4. Tolerance to drought and salt stress in plants: Unraveling the signaling networks

    Directory of Open Access Journals (Sweden)

    Dortje eGolldack

    2014-04-01

    Full Text Available Tolerance of plants to abiotic stressors such as drought and salinity is triggered by complex multicomponent signaling pathways to restore cellular homeostasis and promote survival. Major plant transcription factor families such as bZIP, NAC, AP2/ERF and MYB orchestrate regulatory networks underlying abiotic stress tolerance. Sucrose nonfermenting 1-related protein kinase 2 (SnRK2 and MAPK pathways contribute to initiation of stress adaptive downstream responses and promote plant growth and development. As a convergent point of multiple abiotic cues, cellular effects of environmental stresses are not only imbalances of ionic and osmotic homeostasis but also impaired photosynthesis, cellular energy depletion, and redox imbalances. Recent evidence of regulatory systems that link sensing and signaling of environmental conditions and the intracellular redox status have shed light on interfaces of stress and energy signaling. ROS (reactive oxygen species cause severe cellular damage by peroxidation and de-esterification of membrane lipids, however, current models also define a pivotal signaling function of ROS in triggering tolerance against stress. Recent research advances suggest and support a regulatory role of ROS in the cross talks of stress triggered hormonal signaling such as the abscisic acid (ABA pathway and endogenously induced redox and metabolite signals. Here, we discuss and review the versatile molecular convergence in the abiotic stress responsive signaling networks in the context of ROS and lipid derived signals and the specific role of stomatal signaling.

  5. Brain expressed and X-linked (Bex proteins are intrinsically disordered proteins (IDPs and form new signaling hubs.

    Directory of Open Access Journals (Sweden)

    Eva M Fernandez

    Full Text Available Intrinsically disordered proteins (IDPs are abundant in complex organisms. Due to their promiscuous nature and their ability to adopt several conformations IDPs constitute important points of network regulation. The family of Brain Expressed and X-linked (Bex proteins consists of 5 members in humans (Bex1-5. Recent reports have implicated Bex proteins in transcriptional regulation and signaling pathways involved in neurodegeneration, cancer, cell cycle and tumor growth. However, structural and biophysical data for this protein family is almost non-existent. We used bioinformatics analyses to show that Bex proteins contain long regions of intrinsic disorder which are conserved across all members. Moreover, we confirmed the intrinsic disorder by circular dichroism spectroscopy of Bex1 after expression and purification in E. coli. These observations strongly suggest that Bex proteins constitute a new group of IDPs. Based on these findings, together with the demonstrated promiscuity of Bex proteins and their involvement in different signaling pathways, we propose that Bex family members play important roles in the formation of protein network hubs.

  6. Emergent decision-making in biological signal transduction networks

    Science.gov (United States)

    Helikar, Tomáš; Konvalina, John; Heidel, Jack; Rogers, Jim A.

    2008-01-01

    The complexity of biochemical intracellular signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the logical mechanism of each node described completely to allow simulation and dynamical analysis. Exposing the network to tens of thousands of random combinations of inputs and analyzing the combined dynamics of multiple outputs revealed a robust system capable of clustering widely varying input combinations into equivalence classes of biologically relevant cellular responses. This capability was nontrivial in that the network performed sharp, nonfuzzy classifications even in the face of added noise, a hallmark of real-world decision-making. PMID:18250321

  7. Evolutionarily conserved herpesviral protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Even Fossum

    2009-09-01

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

  8. Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

    Directory of Open Access Journals (Sweden)

    Tan Kai

    2010-10-01

    Full Text Available Abstract Background Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data. Results We present an algorithm, miPALM (Module Inference by Parametric Local Modularity, to infer protein complexes in a protein-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae. Conclusions miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html.

  9. The PHR proteins: intracellular signaling hubs in neuronal development and axon degeneration.

    Science.gov (United States)

    Grill, Brock; Murphey, Rodney K; Borgen, Melissa A

    2016-03-23

    During development, a coordinated and integrated series of events must be accomplished in order to generate functional neural circuits. Axons must navigate toward target cells, build synaptic connections, and terminate outgrowth. The PHR proteins (consisting of mammalian Phr1/MYCBP2, Drosophila Highwire and C. elegans RPM-1) function in each of these events in development. Here, we review PHR function across species, as well as the myriad of signaling pathways PHR proteins regulate. These findings collectively suggest that the PHR proteins are intracellular signaling hubs, a concept we explore in depth. Consistent with prominent developmental functions, genetic links have begun to emerge between PHR signaling networks and neurodevelopmental disorders, such as autism, schizophrenia and intellectual disability. Finally, we discuss the recent and important finding that PHR proteins regulate axon degeneration, which has further heightened interest in this fascinating group of molecules.

  10. A Signal Processing Method to Explore Similarity in Protein Flexibility

    Directory of Open Access Journals (Sweden)

    Simina Vasilache

    2010-01-01

    Full Text Available Understanding mechanisms of protein flexibility is of great importance to structural biology. The ability to detect similarities between proteins and their patterns is vital in discovering new information about unknown protein functions. A Distance Constraint Model (DCM provides a means to generate a variety of flexibility measures based on a given protein structure. Although information about mechanical properties of flexibility is critical for understanding protein function for a given protein, the question of whether certain characteristics are shared across homologous proteins is difficult to assess. For a proper assessment, a quantified measure of similarity is necessary. This paper begins to explore image processing techniques to quantify similarities in signals and images that characterize protein flexibility. The dataset considered here consists of three different families of proteins, with three proteins in each family. The similarities and differences found within flexibility measures across homologous proteins do not align with sequence-based evolutionary methods.

  11. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Directory of Open Access Journals (Sweden)

    Muhannad T. Suleiman

    2012-11-01

    Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  12. Application of the minimum fuel neural network to music signals

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN...

  13. On the distribution of signal phase in body area networks

    NARCIS (Netherlands)

    Wilson, S.K.; Cotton, Simon L.; Dias, Ugo S.; Scanlon, W.G.; Yacoub, Michel D.

    2010-01-01

    In this letter, we investigate the distribution of the phase component of the complex received signal observed in practical experiments using body area networks. Two phase distributions, the recently proposed κ-μ and η-μ probability densities, which together encompass the most widely used fading

  14. Noise Filtering and Prediction in Biological Signaling Networks

    CERN Document Server

    Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael

    2016-01-01

    Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...

  15. Protein oxidation and proteolytic signalling in aging.

    Science.gov (United States)

    Ott, Christiane; Grune, Tilman

    2014-01-01

    A number of studies reported a relation between longevity, oxidative stress and age-related diseases. Every aerobic organism is inevitably exposed to a permanent flux of free radicals and oxidants. Due to the limited activity of antioxidant and repair mechanisms, levels of reactive oxygen species can increase during aging. Protein damage caused by elevated levels of free radicals or oxidants has an important influence on cellular viability and leads to malfunction of proteins in aged cells. In addition, modified and impaired proteins can cross-link and form the bases of many senescence-associated alterations and also of neurodegenerative diseases. To ensure the maintenance of normal cellular functions, eukaryotic cells exert proteolysis through two systems: the proteasomal system and the lysosomal system, which is degrading cellular components after autophagy. During cellular differentiation and aging, both systems are subject to extensive changes that significantly affect their proteolytic activity. It has been suggested that highly modified proteins and undegradable protein aggregates also affect the intracellular proteolytic systems. Therefore, it is essential to understand the relationship between protein oxidation, intracellular proteolytic systems and cellular defence mechanisms.

  16. Reverse engineering GTPase programming languages with reconstituted signaling networks.

    Science.gov (United States)

    Coyle, Scott M

    2016-07-02

    The Ras superfamily GTPases represent one of the most prolific signaling currencies used in Eukaryotes. With these remarkable molecules, evolution has built GTPase networks that control diverse cellular processes such as growth, morphology, motility and trafficking. (1-4) Our knowledge of the individual players that underlie the function of these networks is deep; decades of biochemical and structural data has provided a mechanistic understanding of the molecules that turn GTPases ON and OFF, as well as how those GTPase states signal by controlling the assembly of downstream effectors. However, we know less about how these different activities work together as a system to specify complex dynamic signaling outcomes. Decoding this molecular "programming language" would help us understand how different species and cell types have used the same GTPase machinery in different ways to accomplish different tasks, and would also provide new insights as to how mutations to these networks can cause disease. We recently developed a bead-based microscopy assay to watch reconstituted H-Ras signaling systems at work under arbitrary configurations of regulators and effectors. (5) Here we highlight key observations and insights from this study and propose extensions to our method to further study this and other GTPase signaling systems.

  17. Signalling network construction for modelling plant defence response.

    Directory of Open Access Journals (Sweden)

    Dragana Miljkovic

    Full Text Available Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2 triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be

  18. Protein phosphorylation and its role in archaeal signal transduction.

    Science.gov (United States)

    Esser, Dominik; Hoffmann, Lena; Pham, Trong Khoa; Bräsen, Christopher; Qiu, Wen; Wright, Phillip C; Albers, Sonja-Verena; Siebers, Bettina

    2016-09-01

    Reversible protein phosphorylation is the main mechanism of signal transduction that enables cells to rapidly respond to environmental changes by controlling the functional properties of proteins in response to external stimuli. However, whereas signal transduction is well studied in Eukaryotes and Bacteria, the knowledge in Archaea is still rather scarce. Archaea are special with regard to protein phosphorylation, due to the fact that the two best studied phyla, the Euryarchaeota and Crenarchaeaota, seem to exhibit fundamental differences in regulatory systems. Euryarchaeota (e.g. halophiles, methanogens, thermophiles), like Bacteria and Eukaryotes, rely on bacterial-type two-component signal transduction systems (phosphorylation on His and Asp), as well as on the protein phosphorylation on Ser, Thr and Tyr by Hanks-type protein kinases. Instead, Crenarchaeota (e.g. acidophiles and (hyper)thermophiles) only depend on Hanks-type protein phosphorylation. In this review, the current knowledge of reversible protein phosphorylation in Archaea is presented. It combines results from identified phosphoproteins, biochemical characterization of protein kinases and protein phosphatases as well as target enzymes and first insights into archaeal signal transduction by biochemical, genetic and polyomic studies. © FEMS 2016.

  19. Quantitative imaging of lymphocyte membrane protein reorganization and signaling.

    Science.gov (United States)

    Kasson, Peter M; Huppa, Johannes B; Krogsgaard, Michelle; Davis, Mark M; Brunger, Axel T

    2005-01-01

    Changes in membrane protein localization are critical to establishing cell polarity and regulating cell signaling. Fluorescence microscopy of labeled proteins allows visualization of these changes, but quantitative analysis is needed to study this aspect of cell signaling in full mechanistic detail. We have developed a novel approach for quantitative assessment of membrane protein redistribution based on four-dimensional video microscopy of fluorescently labeled proteins. Our analytic system provides robust automated methods for cell surface reconstruction, cell shape tracking, cell-surface distance measurement, and cluster formation analysis. These methods permit statistical analyses and testing of mechanistic hypotheses regarding cell signaling. We have used this approach to measure antigen-dependent clustering of signaling molecules in CD4+ T lymphocytes, obtaining clustering velocities consistent with single-particle tracking data. Our system captures quantitative differences in clustering between signaling proteins with distinct biological functions. Our methods can be generalized to a range of cell-signaling phenomena and enable novel applications not feasible with single-particle studies, such as analysis of subcellular protein localization in live organ culture.

  20. Shooting movies of signaling network dynamics with multiparametric cytometry.

    Science.gov (United States)

    Claassen, Manfred

    2014-01-01

    Single-cell technologies like mass cytometry enable researchers to comprehensively monitor signaling network responses in the context of heterogeneous cell populations. Cell-to-cell variability, the possibly nonlinear topology of signaling processes, and the destructive nature of mass cytometry necessitate nontrivial computational approaches to reconstruct and sensibly describe signaling dynamics. Modeling of signaling states depends on a set of coherent examples, that is, a set of cell events representing the same cell state. This requirement is frequently compromized by process asynchrony phenomena or nonlinear process topologies. We discuss various computational deconvolution approaches to define molecular process coordinates and enable compilation of coherent data sets for cell state inference. In addition to the conceptual presentation of these approaches, we discuss the application of these methods to modeling of TRAIL-induced apoptosis. Due to their generic applicability these computational approaches will contribute to the elucidation of dynamic intracellular signaling networks in various settings. The resulting signaling maps constitute a promising source for novel interventions and are expected to be particularly valuable in clinical settings.

  1. Early-warning signals of topological collapse in interbank networks

    CERN Document Server

    Squartini, Tiziano; Garlaschelli, Diego

    2013-01-01

    The financial crisis marked a paradigm shift, from traditional studies of individual risk to recent research on the "systemic risk" generated by whole networks of institutions. However, the reverse effects of realized defaults on network topology are poorly understood. Here we analyze the Dutch interbank network over the period 1998-2008, ending with the global crisis. We find that many topological properties, after controlling for overall density effects, display an abrupt change in 2008, thus providing a clear but unpredictable signature of the crisis. By contrast, if the intrinsic heterogeneity of banks is controlled for, the same properties undergo a slow and continuous transition, gradually connecting the crisis period to a much earlier stationary phase. This early-warning signal begins in 2005, and is preceded by an even earlier period of "risk autocatalysis" characterized by anomalous debt loops. These remarkable precursors are undetectable if the network is reconstructed from partial bank-specific inf...

  2. Computational modeling of signal transduction networks: a pedagogical exposition.

    Science.gov (United States)

    Prasad, Ashok

    2012-01-01

    We give a pedagogical introduction to computational modeling of signal transduction networks, starting from explaining the representations of chemical reactions by differential equations via the law of mass action. We discuss elementary biochemical reactions such as Michaelis-Menten enzyme kinetics and cooperative binding, and show how these allow the representation of large networks as systems of differential equations. We discuss the importance of looking for simpler or reduced models, such as network motifs or dynamical motifs within the larger network, and describe methods to obtain qualitative behavior by bifurcation analysis, using freely available continuation software. We then discuss stochastic kinetics and show how to implement easy-to-use methods of rule-based modeling for stochastic simulations. We finally suggest some methods for comprehensive parameter sensitivity analysis, and discuss the insights that it could yield. Examples, including code to try out, are provided based on a paper that modeled Ras kinetics in thymocytes.

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

    Science.gov (United States)

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

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

  4. The broad spectrum of signaling pathways regulated by unfolded protein response in neuronal homeostasis.

    Science.gov (United States)

    Saito, Atsushi; Imaizumi, Kazunori

    2017-06-28

    The protein folding capabilities in the endoplasmic reticulum (ER) are disturbed by alternations in the cellular homeostasis such as the disruption of calcium ion homeostasis, the expression of mutated proteins and oxidative stress. In response to these ER dysfunctions, eukaryotic cells activate canonical branches of signal transduction cascades to restore the protein folding capacity and avoid irreversible damages, collectively termed the unfolded protein response (UPR). Prolonged ER dysfunctions and the downregulation of UPR signaling pathways have been accepted as a crucial trigger for the pathogenesis of various neurodegenerative diseases. Furthermore, recent studies have revealed that the UPR has a wide spectrum of signaling pathways for unique physiological roles in the diverse developmental, differential and lipidomic processes. A developed and intricate ER network exists in the neurites of neurons. Neuronal ER functions and ER-derived signaling mediate efficient communication between cell soma and distal sites through local protein synthesis, sorting and lipogenesis. However, relevant of ER-derived UPR signaling pathways in the elaborate mechanisms regulating neuronal activities, synaptic functions and protective responses against injury is not fully elucidated. In this review, we summarized our current understanding of how the UPR functions provide the appropriate signals for neuronal capabilities. We also reviewed how UPR dysfunctions lead to the pathogenesis of neurodegenerative diseases, and the possibilities ameliorating their toxic effects by targeting UPR components. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  5. Analysis of core–periphery organization in protein contact networks ...

    Indian Academy of Sciences (India)

    The representation of proteins as networks of interacting amino acids, referred to as protein contact networks (PCN), and their subsequent analyses using graph theoretic tools, can provide novel insights into the key functional roles of specific groups of residues. We have characterized the networks corresponding to the ...

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

    DEFF Research Database (Denmark)

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala

    2016-01-01

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

  7. Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

    Science.gov (United States)

    Kim, Ji Chul; Large, Edward W

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

  8. Network Analysis of Neurodegenerative Disease Highlights a Role of Toll-Like Receptor Signaling

    Directory of Open Access Journals (Sweden)

    Thanh-Phuong Nguyen

    2014-01-01

    Full Text Available Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs, the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computational method based on protein-protein interaction network (PPI to model the functional network of NDs. The aim of this work is fourfold: (i reconstruction of a PPI network relating to the NDs, (ii construction of an association network between diseases based on proximity in the disease PPI network, (iii quantification of disease associations, and (iv inference of potential molecular mechanism involved in the diseases. The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration.

  9. Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information

    Directory of Open Access Journals (Sweden)

    Pedamallu Chandra Sekhar

    2010-08-01

    Full Text Available Abstract Background Protein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several (usually expensive and time consuming experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible. Methods Here we report the development of a simple open source program module (OpenPPI_predictor that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism. Results Results from our predictions can be visualized using the Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans. Availability The OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/.

  10. Network evolution: rewiring and signatures of conservation in signaling.

    Directory of Open Access Journals (Sweden)

    Mark G F Sun

    Full Text Available The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3 domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.

  11. Nonlinear Silicon Photonic Signal Processing Devices for Future Optical Networks

    Directory of Open Access Journals (Sweden)

    Cosimo Lacava

    2017-01-01

    Full Text Available In this paper, we present a review on silicon-based nonlinear devices for all optical nonlinear processing of complex telecommunication signals. We discuss some recent developments achieved by our research group, through extensive collaborations with academic partners across Europe, on optical signal processing using silicon-germanium and amorphous silicon based waveguides as well as novel materials such as silicon rich silicon nitride and tantalum pentoxide. We review the performance of four wave mixing wavelength conversion applied on complex signals such as Differential Phase Shift Keying (DPSK, Quadrature Phase Shift Keying (QPSK, 16-Quadrature Amplitude Modulation (QAM and 64-QAM that dramatically enhance the telecom signal spectral efficiency, paving the way to next generation terabit all-optical networks.

  12. Olfactory G proteins: simple and complex signal transduction.

    Science.gov (United States)

    Ebrahimi, F A; Chess, A

    1998-06-04

    In both vertebrates and invertebrates, olfactory perception is mediated by G-protein-coupled receptors. Recent work, in both mouse and Caenorhabditis elegans, sheds light on the role of specific G proteins in olfactory signal transduction, neuronal morphology and axon guidance.

  13. Signal recognition partcle mediated protein targeting in Escherichia coli

    NARCIS (Netherlands)

    Valent, Q.A.

    2001-01-01

    The signal recognition particle (SRP) is a conserved ribonucleoprotein complex that binds to targeting sequences in nascent secretory and membrane proteins. The SRP guides these proteins to the cytoplasmic membrane in prokaryotes and the endoplasmic reticulum membrane in eukaryotes via an

  14. SPARC fusion protein induces cellular adhesive signaling.

    Directory of Open Access Journals (Sweden)

    Lamei Cheng

    Full Text Available Secreted protein, acidic and rich in cysteine (SPARC has been described as a counteradhesive matricellular protein with a diversity of biological functions associated with morphogenesis, remodeling, cellular migration, and proliferation. We have produced mouse SPARC with a FLAG-tag at the N-terminus of SPARC (Flag-SPARC, FSP in a Bac-to-Bac baculoviral expression system. After affinity purification, this procedure yields SPARC of high purity, with an electrophoretic mobility of ∼44 kDa under reducing conditions, and ∼38-39 kDa under non-reducing conditions. Unexpectedly, FSP adsorbed to plastic supported cell attachment and spreading, in a calcium-dependent manner. The adhesive activity of native FSP was inhibited by prior incubation with anti-SPARC IgG. Cell adhesion to FSP induced the formation of filopodia and lamellipodia but not focal adhesions that were prominent on cells that were attached to fibronectin. In addition, FSP induced the tyrosine phosphorylation of FAK and paxillin in attached epithelial cells. Erk1/2 and Rac were also activated in cells attached to FSP, but at a lower level in comparison to cells on fibronectin. This study provides new insight into the biological functions of SPARC, a matricellular protein with important roles in cell-extracellualr matrix interactions.

  15. Quantitative analysis of HGF and EGF-dependent phosphotyrosine signaling networks

    DEFF Research Database (Denmark)

    Hammond, Dean E; Hyde, Russell; Kratchmarova, Irina

    2010-01-01

    between the respective signaling networks but also clear points of departure. A small number of HGF specific effectors were identified including myosin-X, galectin-1, ELMO2 and EphrinB1, while a larger set of EGF specific effectors (39 proteins) includes both novel (e.g., MAP4K3) and established......We have used stable isotope labeling by amino acids in cell culture (SILAC), in combination with high-resolution mass spectrometry, to identify common and discrete components of the respective receptor tyrosine kinase-dependent phosphotyrosine-associated networks induced by acute stimulation of A...

  16. Design principles of nuclear receptor signaling: How complex networking improves signal transduction

    NARCIS (Netherlands)

    A.N. Kolodkin (Alexey); F.J. Bruggeman (Frank); N. Plant (Nick); M.J. Moné (Martijn); B.M. Bakker (Barbara); M.J. Campbell (Moray); J.P.T.M. van Leeuwen (Hans); C. Carlberg (Carsten); J.L. Snoep (Jacky); H.V. Westerhoff (Hans)

    2010-01-01

    textabstractThe topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of design aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic

  17. Design principles of nuclear receptor signaling : how complex networking improves signal transduction

    NARCIS (Netherlands)

    Kolodkin, Alexey N.; Bruggeman, Frank J.; Plant, Nick; Mone, Martijn J.; Bakker, Barbara M.; Campbell, Moray J.; van Leeuwen, Johannes P. T. M.; Carlberg, Carsten; Snoep, Jacky L.; Westerhoff, Hans V.

    2010-01-01

    The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of 'design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of

  18. Music Signal Processing Using Vector Product Neural Networks

    Science.gov (United States)

    Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

    2017-05-01

    We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

  19. Distributed Signal Processing for Wireless EEG Sensor Networks.

    Science.gov (United States)

    Bertrand, Alexander

    2015-11-01

    Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

  20. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    Directory of Open Access Journals (Sweden)

    Thomas Wallach

    2013-03-01

    Full Text Available Essentially all biological processes depend on protein-protein interactions (PPIs. Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc. contributing to temporal organization of cellular physiology in an unprecedented manner.

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

    Directory of Open Access Journals (Sweden)

    França Gustavo S

    2010-12-01

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

  2. Transduction motif analysis of gastric cancer based on a human signaling network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

    2014-04-04

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

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

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

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

  4. Crosstalk among hormones and signaling networks during stomatal development in Arabidopsis hypocotyls

    Directory of Open Access Journals (Sweden)

    Laura Serna

    2016-09-01

    Full Text Available During development, signaling networks specify stomatal cell fate and patterning in response to phytohormones. A number of studies in the past few years have revealed that brassinosteroids repress the signaling pathway that inactivates SPEECHLESS (SPCH, promoting stomatal cell fate determination in the hypocotyl. These plant hormones also control stomatal patterning specification by regulating genes in the TTG/BHLHs/MYBs/GL2 network. Gibberellins, like brassinosteroids, promote stomatal formation in the embryonic stem, which suggests that their signaling pathways may converge. These phytohormones also regulate LLM-domain B-GATA factors. The involvement of these factors as positive regulators of stomatal formation, which function upstream of SPCH, suggests that the brassinosteroid and gibberellin signaling pathways may converge to control stomatal cell fate specification. In addition, the leucine-rich repeat-containing receptor-like protein TOO MANY MOUTHS acts later than these hormones in the cell division sequence that triggers stomatal formation, and it also appears to control stomatal initiation in response to brassinosteroids. The emerging picture suggests that crosstalk among hormones and signaling networks guides stomatal cell fate determination and patterning in the hypocotyl.

  5. Wireless sensor networks for monitoring physiological signals of multiple patients.

    Science.gov (United States)

    Dilmaghani, R S; Bobarshad, H; Ghavami, M; Choobkar, S; Wolfe, C

    2011-08-01

    This paper presents the design of a novel wireless sensor network structure to monitor patients with chronic diseases in their own homes through a remote monitoring system of physiological signals. Currently, most of the monitoring systems send patients' data to a hospital with the aid of personal computers (PC) located in the patients' home. Here, we present a new design which eliminates the need for a PC. The proposed remote monitoring system is a wireless sensor network with the nodes of the network installed in the patients' homes. These nodes are then connected to a central node located at a hospital through an Internet connection. The nodes of the proposed wireless sensor network are created by using a combination of ECG sensors, MSP430 microcontrollers, a CC2500 low-power wireless radio, and a network protocol called the SimpliciTI protocol. ECG signals are first sampled by a small portable device which each patient carries. The captured signals are then wirelessly transmitted to an access point located within the patients' home. This connectivity is based on wireless data transmission at 2.4-GHz frequency. The access point is also a small box attached to the Internet through a home asynchronous digital subscriber line router. Afterwards, the data are sent to the hospital via the Internet in real time for analysis and/or storage. The benefits of this remote monitoring are wide ranging: the patients can continue their normal lives, they do not need a PC all of the time, their risk of infection is reduced, costs significantly decrease for the hospital, and clinicians can check data in a short time.

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

    Science.gov (United States)

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

    2012-07-01

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

  7. Identifying protein complexes based on density and modularity in protein-protein interaction network.

    Science.gov (United States)

    Ren, Jun; Wang, Jianxin; Li, Min; Wang, Lusheng

    2013-01-01

    Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity. To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, fρ, as fρ= (density)(ρ*)(modularity)(1-ρ), and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0modularity. By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.

  8. Priming GPCR signaling through the synergistic effect of two G proteins.

    Science.gov (United States)

    Gupte, Tejas M; Malik, Rabia U; Sommese, Ruth F; Ritt, Michael; Sivaramakrishnan, Sivaraj

    2017-04-04

    Although individual G-protein-coupled receptors (GPCRs) are known to activate one or more G proteins, the GPCR-G-protein interaction is viewed as a bimolecular event involving the formation of a ternary ligand-GPCR-G-protein complex. Here, we present evidence that individual GPCR-G-protein interactions can reinforce each other to enhance signaling through canonical downstream second messengers, a phenomenon we term "GPCR priming." Specifically, we find that the presence of noncognate Gq protein enhances cAMP stimulated by two Gs-coupled receptors, β2-adrenergic receptor (β2-AR) and D 1 dopamine receptor (D 1 -R). Reciprocally, Gs enhances IP 1 through vasopressin receptor (V 1A -R) but not α1 adrenergic receptor (α1-AR), suggesting that GPCR priming is a receptor-specific phenomenon. The C terminus of either the Gαs or Gαq subunit is sufficient to enhance Gα subunit activation and cAMP levels. Interaction of Gαs or Gαq C termini with the GPCR increases signaling potency, suggesting an altered GPCR conformation as the underlying basis for GPCR priming. We propose three parallel mechanisms involving ( i ) sequential G-protein interactions at the cognate site, ( ii ) G-protein interactions at distinct allosteric and cognate sites on the GPCR, and ( iii ) asymmetric GPCR dimers. GPCR priming suggests another layer of regulation in the classic GPCR ternary-complex model, with broad implications for the multiplicity inherent in signaling networks.

  9. Nonlinear transfer of signal and noise correlations in cortical networks.

    Science.gov (United States)

    Lyamzin, Dmitry R; Barnes, Samuel J; Donato, Roberta; Garcia-Lazaro, Jose A; Keck, Tara; Lesica, Nicholas A

    2015-05-27

    Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks. Copyright © 2015 Lyamzin et al.

  10. CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method.

    Science.gov (United States)

    Wang, Kai; Hu, Fuyan; Xu, Kejia; Cheng, Hua; Jiang, Meng; Feng, Ruili; Li, Jing; Wen, Tieqiao

    2011-05-17

    Signal transduction is an essential biological process involved in cell response to environment changes, by which extracellular signaling initiates intracellular signaling. Many computational methods have been generated in mining signal transduction networks with the increasing of high-throughput genomic and proteomic data. However, more effective means are still needed to understand the complex mechanisms of signaling pathways. We propose a new approach, namely CASCADE_SCAN, for mining signal transduction networks from high-throughput data based on the steepest descent method using indirect protein-protein interactions (PPIs). This method is useful for actual biological application since the given proteins utilized are no longer confined to membrane receptors or transcription factors as in existing methods. The precision and recall values of CASCADE_SCAN are comparable with those of other existing methods. Moreover, functional enrichment analysis of the network components supported the reliability of the results. CASCADE_SCAN is a more suitable method than existing methods for detecting underlying signaling pathways where the membrane receptors or transcription factors are unknown, providing significant insight into the mechanism of cellular signaling in growth, development and cancer. A new tool based on this method is freely available at http://www.genomescience.com.cn/CASCADE_SCAN/.

  11. RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells.

    Science.gov (United States)

    Chia, Joanne; Goh, Germaine; Racine, Victor; Ng, Susanne; Kumar, Pankaj; Bard, Frederic

    2012-01-01

    The Golgi apparatus has many important physiological functions, including sorting of secretory cargo and biosynthesis of complex glycans. These functions depend on the intricate and compartmentalized organization of the Golgi apparatus. To investigate the mechanisms that regulate Golgi architecture, we developed a quantitative morphological assay using three different Golgi compartment markers and quantitative image analysis, and performed a kinome- and phosphatome-wide RNAi screen in HeLa cells. Depletion of 159 signaling genes, nearly 20% of genes assayed, induced strong and varied perturbations in Golgi morphology. Using bioinformatics data, a large regulatory network could be constructed. Specific subnetworks are involved in phosphoinositides regulation, acto-myosin dynamics and mitogen activated protein kinase signaling. Most gene depletion also affected Golgi functions, in particular glycan biosynthesis, suggesting that signaling cascades can control glycosylation directly at the Golgi level. Our results provide a genetic overview of the signaling pathways that control the Golgi apparatus in human cells.

  12. Rap G protein signal in normal and disordered lymphohematopoiesis

    Energy Technology Data Exchange (ETDEWEB)

    Minato, Nagahiro, E-mail: minato@imm.med.kyoto-u.ac.jp

    2013-09-10

    Rap proteins (Rap1, Rap2a, b, c) are small molecular weight GTPases of the Ras family. Rap G proteins mediate diverse cellular events such as cell adhesion, proliferation, and gene activation through various signaling pathways. Activation of Rap signal is regulated tightly by several specific regulatory proteins including guanine nucleotide exchange factors and GTPase-activating proteins. Beyond cell biological studies, increasing attempts have been made in the past decade to define the roles of Rap signal in specific functions of normal tissue systems as well as in cancer. In the immune and hematopoietic systems, Rap signal plays crucial roles in the development and function of essentially all lineages of lymphocytes and hematopoietic cells, and importantly, deregulated Rap signal may lead to unique pathological conditions depending on the affected cell types, including various types of leukemia and autoimmunity. The phenotypical studies have unveiled novel, even unexpected functional aspects of Rap signal in cells from a variety of tissues, providing potentially important clues for controlling human diseases, including malignancy.

  13. Fats and function: protein lipid modifications in plant cell signalling.

    Science.gov (United States)

    Turnbull, Dionne; Hemsley, Piers A

    2017-12-01

    The post-translational lipid modifications N-myristoylation, prenylation and S-acylation are traditionally associated with increasing protein membrane affinity and localisation. However this is an over-simplification, with evidence now implicating these modifications in a variety of roles such as membrane microdomain partitioning, protein trafficking, protein complex assembly and polarity maintenance. Evidence for a regulatory role is also emerging, with changes or manipulation of lipid modifications offering a means of directly controlling various aspects of protein function. Proteomics advances have revealed an enrichment of signalling proteins in the lipid-modified proteome, potentially indicating an important role for these modifications in responding to stimuli. This review highlights some of the key themes and possible functions of lipid modification during signalling processes in plants. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Type I signal peptidase and protein secretion in Staphylococcus epidermidis.

    Science.gov (United States)

    Powers, Michael E; Smith, Peter A; Roberts, Tucker C; Fowler, Bruce J; King, Charles C; Trauger, Sunia A; Siuzdak, Gary; Romesberg, Floyd E

    2011-01-01

    Bacterial protein secretion is a highly orchestrated process that is essential for infection and virulence. Despite extensive efforts to predict or experimentally detect proteins that are secreted, the characterization of the bacterial secretome has remained challenging. A central event in protein secretion is the type I signal peptidase (SPase)-mediated cleavage of the N-terminal signal peptide that targets a protein for secretion via the general secretory pathway, and the arylomycins are a class of natural products that inhibit SPase, suggesting that they may be useful chemical biology tools for characterizing the secretome. Here, using an arylomycin derivative, along with two-dimensional gel electrophoresis and liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identify 11 proteins whose secretion from stationary-phase Staphylococcus epidermidis is dependent on SPase activity, 9 of which are predicted to be translated with canonical N-terminal signal peptides. In addition, we find that the presence of extracellular domains of lipoteichoic acid synthase (LtaS) and the β-lactam response sensor BlaR1 in the medium is dependent on SPase activity, suggesting that they are cleaved at noncanonical sites within the protein. In all, the data define the proteins whose stationary-phase secretion depends on SPase and also suggest that the arylomycins should be valuable chemical biology tools for the study of protein secretion in a wide variety of different bacteria.

  15. Multitask learning of signaling and regulatory networks with application to studying human response to flu.

    Science.gov (United States)

    Jain, Siddhartha; Gitter, Anthony; Bar-Joseph, Ziv

    2014-12-01

    Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

  16. Multitask learning of signaling and regulatory networks with application to studying human response to flu.

    Directory of Open Access Journals (Sweden)

    Siddhartha Jain

    2014-12-01

    Full Text Available Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

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

    Directory of Open Access Journals (Sweden)

    Qiguo Dai

    2014-01-01

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

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

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

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

  19. Random Deep Belief Networks for Recognizing Emotions from Speech Signals

    Directory of Open Access Journals (Sweden)

    Guihua Wen

    2017-01-01

    Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

  20. Speech Subvocal Signal Processing using Packet Wavelet and Neuronal Network

    Directory of Open Access Journals (Sweden)

    Luis E. Mendoza

    2013-11-01

    Full Text Available This paper presents the results obtained from the recording, processing and classification of words in the Spanish language by means of the analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop. In this work, the signals were sensed with surface electrodes placed on the surface of the throat and acquired with a sampling frequency of 50 kHz. The signal conditioning consisted in: the location of area of interest using energy analysis, and filtering using Discrete Wavelet Transform. Finally, the feature extraction was made in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. The classification was carried out with a backpropagation neural network whose training was performed with 70% of the database obtained. The correct classification rate was 75%±2.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ba, Qian [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Li, Junyang; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wu, Yongning, E-mail: wuyongning@cfsa.net.cn [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)

    2015-03-01

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

  2. Signal transduction by a nondissociable heterotrimeric yeast G protein

    OpenAIRE

    Klein, Shoshana; Reuveni, Hadas; Levitzki, Alexander

    2000-01-01

    Many signal transduction pathways involve heterotrimeric G proteins. The accepted model for activation of heterotrimeric G proteins states that the protein dissociates to the free Gα (GTP)-bound subunit and free Gβγ dimer. On GTP hydrolysis, Gα (GDP) then reassociates with Gβγ [Gilman, A. G. (1987) Annu. Rev. Biochem. 56, 615–649]. We reexamined this hypothesis, by using the mating G protein of the yeast Saccharomyces cerevisiae encoded by the genes GPA1, STE4, and STE18. In the absence of ma...

  3. Information processing in network architecture of genome controlled signal transduction circuit. A proposed theoretical explanation.

    Science.gov (United States)

    Chakraborty, Chiranjib; Sarkar, Bimal Kumar; Patel, Pratiksha; Agoramoorthy, Govindasamy

    2012-01-01

    In this paper, Shannon information theory has been applied to elaborate cell signaling. It is proposed that in the cellular network architecture, four components viz. source (DNA), transmitter (mRNA), receiver (protein) and destination (another protein) are involved. The message transmits from source (DNA) to transmitter (mRNA) and then passes through a noisy channel reaching finally the receiver (protein). The protein synthesis process is here considered as the noisy channel. Ultimately, signal is transmitted from receiver to destination (another protein). The genome network architecture elements were compared with genetic alphabet L = {A, C, G, T} with a biophysical model based on the popular Shannon information theory. This study found the channel capacity as maximum for zero error (sigma = 0) and at this condition, transition matrix becomes a unit matrix with rank 4. The transition matrix will be erroneous and finally at sigma = 1 channel capacity will be localized maxima with a value of 0.415 due to the increased value at sigma. On the other hand, minima exists at sigma = 0.75, where all transition probabilities become 0.25 and uncertainty will be maximum resulting in channel capacity with the minima value of zero.

  4. Towards systematic discovery of signaling networks in budding yeast filamentous growth stress response using interventional phosphorylation data.

    Directory of Open Access Journals (Sweden)

    Yan Zhang

    Full Text Available Reversible phosphorylation is one of the major mechanisms of signal transduction, and signaling networks are critical regulators of cell growth and development. However, few of these networks have been delineated completely. Towards this end, quantitative phosphoproteomics is emerging as a useful tool enabling large-scale determination of relative phosphorylation levels. However, phosphoproteomics differs from classical proteomics by a more extensive sampling limitation due to the limited number of detectable sites per protein. Here, we propose a comprehensive quantitative analysis pipeline customized for phosphoproteome data from interventional experiments for identifying key proteins in specific pathways, discovering the protein-protein interactions and inferring the signaling network. We also made an effort to partially compensate for the missing value problem, a chronic issue for proteomics studies. The dataset used for this study was generated using SILAC (Stable Isotope Labeling with Amino acids in Cell culture technique with interventional experiments (kinase-dead mutations. The major components of the pipeline include phosphopeptide meta-analysis, correlation network analysis and causal relationship discovery. We have successfully applied our pipeline to interventional experiments identifying phosphorylation events underlying the transition to a filamentous growth form in Saccharomyces cerevisiae. We identified 5 high-confidence proteins from meta-analysis, and 19 hub proteins from correlation analysis (Pbi2p and Hsp42p were identified by both analyses. All these proteins are involved in stress responses. Nine of them have direct or indirect evidence of involvement in filamentous growth. In addition, we tested four of our predicted proteins, Nth1p, Pbi2p, Pdr12p and Rcn2p, by interventional phenotypic experiments and all of them present differential invasive growth, providing prospective validation of our approach. This comprehensive

  5. Properties of Fibrillar Protein Assemblies and their Percolating Networks

    NARCIS (Netherlands)

    Veerman, C.

    2004-01-01

    Properties of Fibrillar Protein Assemblies and their Percolating Networks. PhD thesis, Wageningen University, The Netherlands Keywords: bovine serum albumin, complex fluids, excluded volume, fibrils, gels, innovation, b-lactoglobulin, ovalbumin, percolation, proteins, rheology, rheo-optics,

  6. Clustering and Network Analysis of Reverse Phase Protein Array Data.

    Science.gov (United States)

    Byron, Adam

    2017-01-01

    Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.

  7. Surface sensing and signaling networks in plant pathogenic fungi.

    Science.gov (United States)

    Kou, Yanjun; Naqvi, Naweed I

    2016-09-01

    Pathogenic fungi have evolved highly varied and remarkable strategies to invade and infect their plant hosts. Typically, such fungal pathogens utilize highly specialized infection structures, morphologies or cell types produced from conidia or ascospores on the cognate host surfaces to gain entry therein. Such diverse infection strategies require intricate coordination in cell signaling and differentiation in phytopathogenic fungi. Here, we present an overview of our current understanding of cell signaling and infection-associated development that primes host penetration in the top ten plant pathogenic fungi, which utilize specific receptors to sense and respond to different surface cues, such as topographic features, hydrophobicity, hardness, plant lipids, phytohormones, and/or secreted enzymes. Subsequently, diverse signaling components such as G proteins, cyclic AMP/Protein Kinase A and MAP kinases are activated to enable the differentiation of infection structures. Recent studies have also provided fascinating insights into the spatio-temporal dynamics and specialized sequestration and trafficking of signaling moieties required for proper development of infection structures in phytopathogenic fungi. Molecular insight in such infection-related morphogenesis and cell signaling holds promise for identifying novel strategies for intervention of fungal diseases in plants. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Hao Wu

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

  9. NT2 derived neuronal and astrocytic network signalling.

    Directory of Open Access Journals (Sweden)

    Eric J Hill

    Full Text Available A major focus of stem cell research is the generation of neurons that may then be implanted to treat neurodegenerative diseases. However, a picture is emerging where astrocytes are partners to neurons in sustaining and modulating brain function. We therefore investigated the functional properties of NT2 derived astrocytes and neurons using electrophysiological and calcium imaging approaches. NT2 neurons (NT2Ns expressed sodium dependent action potentials, as well as responses to depolarisation and the neurotransmitter glutamate. NT2Ns exhibited spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling. Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, NT2 astrocytes (NT2As exhibited morphology and functional properties consistent with this glial cell type. NT2As responded to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. NT2As also generated propagating calcium waves that were gap junction and purinergic signalling dependent. Our results show that NT2 derived astrocytes exhibit appropriate functionality and that NT2N networks interact with NT2A networks in co-culture. These findings underline the utility of such cultures to investigate human brain cell type signalling under controlled conditions. Furthermore, since stem cell derived neuron function and survival is of great importance therapeutically, our findings suggest that the presence of complementary astrocytes may be valuable in supporting stem cell derived neuronal networks. Indeed, this also supports the intriguing possibility of selective therapeutic replacement of astrocytes in diseases where these cells are either lost or lose functionality.

  10. Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.

    Science.gov (United States)

    Shen, Xianjun; Yi, Li; Yi, Yang; Yang, Jincai; He, Tingting; Hu, Xiaohua

    2015-01-01

    The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end. In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively. The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization.

  11. Insights into cellular signalling by G protein coupled receptor transactivation of cell surface protein kinase receptors.

    Science.gov (United States)

    Chaplin, Rebecca; Thach, Lyna; Hollenberg, Morley D; Cao, Yingnan; Little, Peter J; Kamato, Danielle

    2017-06-01

    G protein coupled receptor (GPCR) signalling is mediated by transactivation independent and transactivation dependent pathways. GPCRs transactivate protein tyrosine kinase receptors (PTKRs) and protein serine/threonine kinase receptors (PS/TKR). Since the initial observations of transactivation dependent signalling, there has been an effort to understand the mechanisms behind this phenomena. GPCR signalling has evolved to include biased signalling. Biased signalling, whereby selected ligands can activate the same GPCR that can generate multiple signals, but drive only a unique response. To date, there has been no focus on the ability of biased agonists to activate the PTKR and PS/TKR transactivation pathways differentially. As such, this represents a novel direction for future research. This review will discuss the main mechanisms of GPCR mediated receptor transactivation and the pathways involved in intracellular responses.

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

    Directory of Open Access Journals (Sweden)

    van Helden Jacques

    2006-11-01

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

  13. Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Yongcheng Dong

    2015-01-01

    Full Text Available The human papillomavirus 16 (HPV16 has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.

  14. Modularity in the evolution of yeast protein interaction network.

    Science.gov (United States)

    Ogishima, Soichi; Tanaka, Hiroshi; Nakaya, Jun

    2015-01-01

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

  15. G protein signaling in the parasite Entamoeba histolytica

    Science.gov (United States)

    Bosch, Dustin E; Siderovski, David P

    2013-01-01

    The parasite Entamoeba histolytica causes amebic colitis and systemic amebiasis. Among the known amebic factors contributing to pathogenesis are signaling pathways involving heterotrimeric and Ras superfamily G proteins. Here, we review the current knowledge of the roles of heterotrimeric G protein subunits, Ras, Rho and Rab GTPase families in E. histolytica pathogenesis, as well as of their downstream signaling effectors and nucleotide cycle regulators. Heterotrimeric G protein signaling likely modulates amebic motility and attachment to and killing of host cells, in part through activation of an RGS-RhoGEF (regulator of G protein signaling–Rho guanine nucleotide exchange factor) effector. Rho family GTPases, as well as RhoGEFs and Rho effectors (formins and p21-activated kinases) regulate the dynamic actin cytoskeleton of E. histolytica and associated pathogenesis-related cellular processes, such as migration, invasion, phagocytosis and evasion of the host immune response by surface receptor capping. A remarkably large family of 91 Rab GTPases has multiple roles in a complex amebic vesicular trafficking system required for phagocytosis and pinocytosis and secretion of known virulence factors, such as amebapores and cysteine proteases. Although much remains to be discovered, recent studies of G protein signaling in E. histolytica have enhanced our understanding of parasitic pathogenesis and have also highlighted possible targets for pharmacological manipulation. PMID:23519208

  16. Regulation of Phagocyte Migration by Signal Regulatory Protein-Alpha Signaling.

    Directory of Open Access Journals (Sweden)

    Julian Alvarez-Zarate

    Full Text Available Signaling through the inhibitory receptor signal regulatory protein-alpha (SIRPα controls effector functions in phagocytes. However, there are also indications that interactions between SIRPα and its ligand CD47 are involved in phagocyte transendothelial migration. We have investigated the involvement of SIRPα signaling in phagocyte migration in vitro and in vivo using mice that lack the SIRPα cytoplasmic tail. During thioglycolate-induced peritonitis in SIRPα mutant mice, both neutrophil and macrophage influx were found to occur, but to be significantly delayed. SIRPα signaling appeared to be essential for an optimal transendothelial migration and chemotaxis, and for the amoeboid type of phagocyte migration in 3-dimensional environments. These findings demonstrate, for the first time, that SIRPα signaling can directly control phagocyte migration, and this may contribute to the impaired inflammatory phenotype that has been observed in the absence of SIRPα signaling.

  17. Protein Phosphatase 2A in the Regulation of Wnt Signaling, Stem Cells, and Cancer

    Directory of Open Access Journals (Sweden)

    Joshua J. Thompson

    2018-02-01

    Full Text Available Protein phosphorylation is a ubiquitous cellular process that allows for the nuanced and reversible regulation of protein activity. Protein phosphatase 2A (PP2A is a heterotrimeric serine-threonine phosphatase—composed of a structural, regulatory, and catalytic subunit—that controls a variety of cellular events via protein dephosphorylation. While much is known about PP2A and its basic biochemistry, the diversity of its components—especially the multitude of regulatory subunits—has impeded the determination of PP2A function. As a consequence of this complexity, PP2A has been shown to both positively and negatively regulate signaling networks such as the Wnt pathway. Wnt signaling modulates major developmental processes, and is a dominant mediator of stem cell self-renewal, cell fate, and cancer stem cells. Because PP2A affects Wnt signaling both positively and negatively and at multiple levels, further understanding of this complex dynamic may ultimately provide insight into stem cell biology and how to better treat cancers that result from alterations in Wnt signaling. This review will summarize literature that implicates PP2A as a tumor suppressor, explore PP2A mutations identified in human malignancy, and focus on PP2A in the regulation of Wnt signaling and stem cells so as to better understand how aberrancy in this pathway can contribute to tumorigenesis.

  18. Type I signal peptidase and protein secretion in Staphylococcus aureus.

    Science.gov (United States)

    Schallenberger, Mark A; Niessen, Sherry; Shao, Changxia; Fowler, Bruce J; Romesberg, Floyd E

    2012-05-01

    Staphylococcus aureus is an important human pathogen whose virulence relies on the secretion of many different proteins. In general, the secretion of most proteins in S. aureus, as well as other bacteria, is dependent on the type I signal peptidase (SPase)-mediated cleavage of the N-terminal signal peptide that targets a protein to the general secretory pathway. The arylomycins are a class of natural product antibiotics that inhibit SPase, suggesting that they may be useful chemical biology tools for characterizing the secretome. While wild-type S. aureus (NCTC 8325) is naturally resistant to the arylomycins, sensitivity is conferred via a point mutation in its SPase. Here, we use a synthetic arylomycin along with a sensitized strain of S. aureus and multidimensional protein identification technology (MudPIT) mass spectrometry to identify 46 proteins whose extracellular accumulation requires SPase activity. Forty-four possess identifiable Sec-type signal peptides and thus are likely canonically secreted proteins, while four also appear to possess cell wall retention signals. We also identified the soluble C-terminal domains of two transmembrane proteins, lipoteichoic acid synthase, LtaS, and O-acyteltransferase, OatA, both of which appear to have noncanonical, internal SPase cleavage sites. Lastly, we identified three proteins, HtrA, PrsA, and SAOUHSC_01761, whose secretion is induced by arylomycin treatment. In addition to elucidating fundamental aspects of the physiology and pathology of S. aureus, the data suggest that an arylomycin-based therapeutic would reduce virulence while simultaneously eradicating an infection.

  19. Ras signal triggers β-Amyloid Precursor Protein (APP) expression

    OpenAIRE

    Mora, Natalia; Santa Bárbara Ruiz, Paula; Ferreira, Nuno; Serras, Florenci

    2013-01-01

    It has recently been discovered that the Drosophila β-amyloid protein precursor like (Appl) gene, the ortholog of the human β-Amyloid Precursor Protein (APP) gene, is transcriptionally activated by receptor tyrosine kinase activity that involves Ras/MAPK signaling in vivo. This regulation is specifically controlled in photoreceptor neurons of the Drosophila retina. This suggests that some cases of Alzheimer disease, those which have been associated with high expression of the APP gene, may in...

  20. Controllable Soluble Protein Concentration Gradients in Hydrogel Networks**

    OpenAIRE

    Peret, Brian J.; William L Murphy

    2008-01-01

    Here we report controlled formation of sustained, soluble protein concentration gradients within hydrated polymer networks. The approach involves spatially localizing proteins or biodegradable, protein-loaded microspheres within hydrogels to form a protein-releasing “depot”. Soluble protein concentration gradients are then formed as the released protein diffuses away from the localized source. Control over key gradient parameters, including maximum concentration, gradient magnitude, slope, an...

  1. Lossless Compression Schemes for ECG Signals Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    C. Eswaran

    2007-01-01

    Full Text Available This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction in the first stage and encoding of the residues is done by using lossless entropy encoders in the second stage. Different types of lossless encoders, such as Huffman, arithmetic, and runlength encoders, are used. The performances of the proposed neural network predictor-based compression schemes are evaluated using standard distortion and compression efficiency measures. Selected records from MIT-BIH arrhythmia database are used for performance evaluation. The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based schemes with the same quality and similar setup. They are also compared with other known ECG compression methods and the experimental results show that superior performances in terms of the distortion parameters of the reconstructed signals can be achieved with the proposed schemes.

  2. Signalling design and architecture for a proposed mobile satellite network

    Science.gov (United States)

    Yan, T.-Y.; Cheng, U.; Wang, C.

    1990-01-01

    In a frequency-division/demand-assigned multiple-access (FD/DAMA) architecture, each mobile subscriber must make a connection request to the Network Management Center before transmission for either open-end or closed-end services. Open-end services are for voice calls and long file transfer and are processed on a blocked-call-cleared basis. Closed-end services are for transmitting burst data and are processed on a first-come first-served basis. This paper presents the signalling design and architecture for non-voice services of an FD/DAMA mobile satellite network. The connection requests are made through the recently proposed multiple channel collision resolution scheme which provides a significantly higher throughput than the traditional slotted ALOHA scheme. For non-voice services, it is well known that retransmissions are necessary to ensure the delivery of a message in its entirety from the source to destination. Retransmission protocols for open-end and closed-end data transfer are investigated. The signal structure for the proposed network is derived from X-25 standards with appropriate modifications. The packet types and their usages are described in this paper.

  3. Microglia Control Neuronal Network Excitability via BDNF Signalling

    Directory of Open Access Journals (Sweden)

    Francesco Ferrini

    2013-01-01

    Full Text Available Microglia-neuron interactions play a crucial role in several neurological disorders characterized by altered neural network excitability, such as epilepsy and neuropathic pain. While a series of potential messengers have been postulated as substrates of the communication between microglia and neurons, including cytokines, purines, prostaglandins, and nitric oxide, the specific links between messengers, microglia, neuronal networks, and diseases have remained elusive. Brain-derived neurotrophic factor (BDNF released by microglia emerges as an exception in this riddle. Here, we review the current knowledge on the role played by microglial BDNF in controlling neuronal excitability by causing disinhibition. The efforts made by different laboratories during the last decade have collectively provided a robust mechanistic paradigm which elucidates the mechanisms involved in the synthesis and release of BDNF from microglia, the downstream TrkB-mediated signals in neurons, and the biophysical mechanism by which disinhibition occurs, via the downregulation of the K+-Cl− cotransporter KCC2, dysrupting Cl−homeostasis, and hence the strength of GABAA- and glycine receptor-mediated inhibition. The resulting altered network activity appears to explain several features of the associated pathologies. Targeting the molecular players involved in this canonical signaling pathway may lead to novel therapeutic approach for ameliorating a wide array of neural dysfunctions.

  4. Noise induced changes in the expression of p38/MAPK signaling proteins in the sensory epithelium of the inner ear

    OpenAIRE

    Jamesdaniel, Samson; Hu, Bohua; Kermany, Mohammad Habiby; Jiang, HaiYan; Ding, Dalian; Coling, Donald; Salvi, Richard

    2011-01-01

    Noise exposure is a major cause of hearing loss. Classical methods of studying protein involvement have provided a basis for understanding signaling pathways that mediate hearing loss and damage repair but do not lend themselves to studying large networks of proteins that are likely to increase or decrease during noise trauma. To address this issue, antibody microarrays were used to quantify the very early changes in protein expression in three distinct regions of the chinchilla cochlea 2 h a...

  5. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.

    Science.gov (United States)

    Murakami, Yoichi; Tripathi, Lokesh P; Prathipati, Philip; Mizuguchi, Kenji

    2017-06-01

    Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

    Directory of Open Access Journals (Sweden)

    Feng Li

    Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

  7. Dietary proteins and food-related reward signals

    Directory of Open Access Journals (Sweden)

    Katri Peuhkuri

    2011-06-01

    Full Text Available Proteins play a crucial role in almost all biological processes. Dietary proteins are generally considered as energy yielding nutrients and as a source of amino acids for various purposes. In addition, they may have a role in food-related reward signals. The purpose of this review was to give an overview of the role of dietary proteins in food-related reward and possible mechanisms behind such effects. Dietary proteins may elicit food-related reward by several different postprandial mechanisms, including neural and humoral signals from the gastrointestinal tract to the brain. In order to exert rewarding effects, protein have to be absorbed from the intestine and reach the target cells in sufficient concentrations, or act via receptors ad cell signalling in the gut without absorption. Complex interactions between different possible mechanisms make it very difficult to gain a clear view on the role and intesity of each mechanism. It is concluded that, in principle, dietary proteins may have a role in food-related reward. However, the evidence is based mostly on experiments with animal models and one should be careful in drawing conclusions of clinical relevance.

  8. Endocytic Adaptor Protein Tollip Inhibits Canonical Wnt Signaling.

    Directory of Open Access Journals (Sweden)

    Anna Toruń

    Full Text Available Many adaptor proteins involved in endocytic cargo transport exhibit additional functions in other cellular processes which may be either related to or independent from their trafficking roles. The endosomal adaptor protein Tollip is an example of such a multitasking regulator, as it participates in trafficking and endosomal sorting of receptors, but also in interleukin/Toll/NF-κB signaling, bacterial entry, autophagic clearance of protein aggregates and regulation of sumoylation. Here we describe another role of Tollip in intracellular signaling. By performing a targeted RNAi screen of soluble endocytic proteins for their additional functions in canonical Wnt signaling, we identified Tollip as a potential negative regulator of this pathway in human cells. Depletion of Tollip potentiates the activity of β-catenin/TCF-dependent transcriptional reporter, while its overproduction inhibits the reporter activity and expression of Wnt target genes. These effects are independent of dynamin-mediated endocytosis, but require the ubiquitin-binding CUE domain of Tollip. In Wnt-stimulated cells, Tollip counteracts the activation of β-catenin and its nuclear accumulation, without affecting its total levels. Additionally, under conditions of ligand-independent signaling, Tollip inhibits the pathway after the stage of β-catenin stabilization, as observed in human cancer cell lines, characterized by constitutive β-catenin activity. Finally, the regulation of Wnt signaling by Tollip occurs also during early embryonic development of zebrafish. In summary, our data identify a novel function of Tollip in regulating the canonical Wnt pathway which is evolutionarily conserved between fish and humans. Tollip-mediated inhibition of Wnt signaling may contribute not only to embryonic development, but also to carcinogenesis. Mechanistically, Tollip can potentially coordinate multiple cellular pathways of trafficking and signaling, possibly by exploiting its ability to

  9. G protein-coupled receptor accessory proteins and signaling: pharmacogenomic insights.

    Science.gov (United States)

    Thompson, Miles D; Cole, David E C; Jose, Pedro A; Chidiac, Peter

    2014-01-01

    The identification and characterization of the genes encoding G protein-coupled receptors (GPCRs) and the proteins necessary for the processes of ligand binding, GPCR activation, inactivation, and receptor trafficking to the membrane are discussed in the context of human genetic disease. In addition to functional GPCR variants, the identification of genetic disruptions affecting proteins necessary to GPCR functions have provided insights into the function of these pathways. Gsα and Gβ subunit polymorphisms have been found to result in complex phenotypes. Disruptions in accessory proteins that normally modify or organize heterotrimeric G-protein coupling may also result in disease states. These include the contribution of variants of the regulator of G protein signaling (RGS) protein to hypertension; the role variants of the activator of G protein signaling (AGS) proteins to phenotypes (such as the type III AGS8 variant to hypoxia); the contribution of G protein-coupled receptor kinase (GRK) proteins, such as GRK4, in disorders such as hypertension. The role of accessory proteins in GPCR structure and function is discussed in the context of genetic disorders associated with disruption of the genes that encode them. An understanding of the pharmacogenomics of GPCR and accessory protein signaling provides the basis for examining both GPCR pharmacogenetics and the genetics of monogenic disorders that result from disruption of given receptor systems.

  10. Post-translational control of ABA signalling: the roles of protein phosphorylation and ubiquitination.

    Science.gov (United States)

    Yang, Wenqi; Zhang, Wei; Wang, Xiaoxue

    2017-01-01

    The plant phytohormone abscisic acid (ABA) plays significant roles in integrating environmental signals with embryogenesis, germination, seedling establishment, the floral transition and the adaptation of plants to stressful environments by modulating stomatal movement and stress-responsive gene expression. ABA signalling consists of ABA perception, signal transduction and ABA-induced responses. ABA receptors such as members of the PYR/PYL family, group A type 2C protein phosphatases (as negative regulators), SnRK2 protein kinases (as positive regulators), bZIP transcription factors and ion channels are key components of ABA signalling. Post-translational modifications, including dephosphorylation, phosphorylation and ubiquitination, play important roles in regulating ABA signalling. In this review, we focus on the roles of post-translational modifications in ABA signalling. The studies presented provide a detailed picture of the ABA signalling network. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

  11. Improved Protein Arrays for Quantitative Systems Analysis of the Dynamics of Signaling Pathway Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Chin-Rang [National Inst. of Health (NIH), Bethesda, MD (United States). National Heart, Lung and Blood Inst.

    2013-12-11

    Astronauts and workers in nuclear plants who repeatedly exposed to low doses of ionizing radiation (IR, <10 cGy) are likely to incur specific changes in signal transduction and gene expression in various tissues of their body. Remarkable advances in high throughput genomics and proteomics technologies enable researchers to broaden their focus from examining single gene/protein kinetics to better understanding global gene/protein expression profiling and biological pathway analyses, namely Systems Biology. An ultimate goal of systems biology is to develop dynamic mathematical models of interacting biological systems capable of simulating living systems in a computer. This Glue Grant is to complement Dr. Boothman’s existing DOE grant (No. DE-FG02-06ER64186) entitled “The IGF1/IGF-1R-MAPK-Secretory Clusterin (sCLU) Pathway: Mediator of a Low Dose IR-Inducible Bystander Effect” to develop sensitive and quantitative proteomic technology that suitable for low dose radiobiology researches. An improved version of quantitative protein array platform utilizing linear Quantum dot signaling for systematically measuring protein levels and phosphorylation states for systems biology modeling is presented. The signals are amplified by a confocal laser Quantum dot scanner resulting in ~1000-fold more sensitivity than traditional Western blots and show the good linearity that is impossible for the signals of HRP-amplification. Therefore this improved protein array technology is suitable to detect weak responses of low dose radiation. Software is developed to facilitate the quantitative readout of signaling network activities. Kinetics of EGFRvIII mutant signaling was analyzed to quantify cross-talks between EGFR and other signaling pathways.

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

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

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

  13. Targeting the human cancer pathway protein interaction network by structural genomics.

    Science.gov (United States)

    Huang, Yuanpeng Janet; Hang, Dehua; Lu, Long Jason; Tong, Liang; Gerstein, Mark B; Montelione, Gaetano T

    2008-10-01

    Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

  14. Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics*

    Science.gov (United States)

    Huang, Yuanpeng Janet; Hang, Dehua; Lu, Long Jason; Tong, Liang; Gerstein, Mark B.; Montelione, Gaetano T.

    2008-01-01

    Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as “hubs” or “bottlenecks” in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects. PMID:18487680

  15. Guard Cell Signal Transduction Network: Advances in Understanding Abscisic Acid, CO2, and Ca2+ Signaling

    KAUST Repository

    Kim, Tae-Houn

    2010-05-04

    Stomatal pores are formed by pairs of specialized epidermal guard cells and serve as major gateways for both CO2 influx into plants from the atmosphere and transpirational water loss of plants. Because they regulate stomatal pore apertures via integration of both endogenous hormonal stimuli and environmental signals, guard cells have been highly developed as a model system to dissect the dynamics and mechanisms of plant-cell signaling. The stress hormone ABA and elevated levels of CO2 activate complex signaling pathways in guard cells that are mediated by kinases/phosphatases, secondary messengers, and ion channel regulation. Recent research in guard cells has led to a new hypothesis for how plants achieve specificity in intracellular calcium signaling: CO2 and ABA enhance (prime) the calcium sensitivity of downstream calcium-signaling mechanisms. Recent progress in identification of early stomatal signaling components are reviewed here, including ABA receptors and CO2-binding response proteins, as well as systems approaches that advance our understanding of guard cell-signaling mechanisms.

  16. Enhancing the functional content of eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Gaurav Pandey

    Full Text Available Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.

  17. Artificial neural network based approach to EEG signal simulation.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2012-06-01

    In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

  18. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  19. Deep Space Network Capabilities for Receiving Weak Probe Signals

    Science.gov (United States)

    Asmar, Sami; Johnston, Doug; Preston, Robert

    2005-01-01

    Planetary probes can encounter mission scenarios where communication is not favorable during critical maneuvers or emergencies. Launch, initial acquisition, landing, trajectory corrections, safing. Communication challenges due to sub-optimum antenna pointing or transmitted power, amplitude/frequency dynamics, etc. Prevent lock-up on signal and extraction of telemetry. Examples: loss of Mars Observer, nutation of Ulysses, Galileo antenna, Mars Pathfinder and Mars Exploration Rovers Entry, Descent, and Landing, and the Cassini Saturn Orbit Insertion. A Deep Space Network capability to handle such cases has been used successfully to receive signals to characterize the scenario. This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

  20. Improving protein function prediction using domain and protein complexes in PPI networks.

    Science.gov (United States)

    Peng, Wei; Wang, Jianxin; Cai, Juan; Chen, Lu; Li, Min; Wu, Fang-Xiang

    2014-03-24

    Characterization of unknown proteins through computational approaches is one of the most challenging problems in silico biology, which has attracted world-wide interests and great efforts. There have been some computational methods proposed to address this problem, which are either based on homology mapping or in the context of protein interaction networks. In this paper, two algorithms are proposed by integrating the protein-protein interaction (PPI) network, proteins' domain information and protein complexes. The one is domain combination similarity (DCS), which combines the domain compositions of both proteins and their neighbors. The other is domain combination similarity in context of protein complexes (DSCP), which extends the protein functional similarity definition of DCS by combining the domain compositions of both proteins and the complexes including them. The new algorithms are tested on networks of the model species of Saccharomyces cerevisiae to predict functions of unknown proteins using cross validations. Comparing with other several existing algorithms, the results have demonstrated the effectiveness of our proposed methods in protein function prediction. Furthermore, the algorithm DSCP using experimental determined complex data is robust when a large percentage of the proteins in the network is unknown, and it outperforms DCS and other several existing algorithms. The accuracy of predicting protein function can be improved by integrating the protein-protein interaction (PPI) network, proteins' domain information and protein complexes.

  1. 14-3-3 proteins in guard cell signaling

    Directory of Open Access Journals (Sweden)

    Valérie eCotelle

    2016-01-01

    Full Text Available Guard cells are specialized cells located at the leaf surface delimiting pores which control gas exchanges between the plant and the atmosphere. To optimize the CO2 uptake necessary for photosynthesis while minimizing water loss, guard cells integrate environmental signals to adjust stomatal aperture. The size of the stomatal pore is regulated by movements of the guard cells driven by variations in their volume and turgor. As guard cells perceive and transduce a wide array of environmental cues, they provide an ideal system to elucidate early events of plant signaling. Reversible protein phosphorylation events are known to play a crucial role in the regulation of stomatal movements. However, in some cases, phosphorylation alone is not sufficient to achieve complete protein regulation, but is necessary to mediate the binding of interactors that modulate protein function. Among the phosphopeptide-binding proteins, the 14-3-3 proteins are the best characterized in plants. The 14-3-3s are found as multiple isoforms in eukaryotes and have been shown to be involved in the regulation of stomatal movements. In this review, we describe the current knowledge about 14-3-3 roles in the regulation of their binding partners in guard cells: receptors, ion pumps, channels, protein kinases and some of their substrates. Regulation of these targets by 14-3-3 proteins is discussed and related to their function in guard cells during stomatal movements in response to abiotic or biotic stresses.

  2. The signaling helix: a common functional theme in diverse signaling proteins

    Directory of Open Access Journals (Sweden)

    Aravind L

    2006-09-01

    Full Text Available Abstract Background The mechanism by which the signals are transmitted between receptor and effector domains in multi-domain signaling proteins is poorly understood. Results Using sensitive sequence analysis methods we identify a conserved helical segment of around 40 residues in a wide range of signaling proteins, including numerous sensor histidine kinases such as Sln1p, and receptor guanylyl cyclases such as the atrial natriuretic peptide receptor and nitric oxide receptors. We term this helical segment the signaling (S-helix and present evidence that it forms a novel parallel coiled-coil element, distinct from previously known helical segments in signaling proteins, such as the Dimerization-Histidine phosphotransfer module of histidine kinases, the intra-cellular domains of the chemotaxis receptors, inter-GAF domain helical linkers and the α-helical HAMP module. Analysis of domain architectures allowed us to reconstruct the domain-neighborhood graph for the S-helix, which showed that the S-helix almost always occurs between two signaling domains. Several striking patterns in the domain neighborhood of the S-helix also became evident from the graph. It most often separates diverse N-terminal sensory domains from various C-terminal catalytic signaling domains such as histidine kinases, cNMP cyclase, PP2C phosphatases, NtrC-like AAA+ ATPases and diguanylate cyclases. It might also occur between two sensory domains such as PAS domains and occasionally between a DNA-binding HTH domain and a sensory domain. The sequence conservation pattern of the S-helix revealed the presence of a unique constellation of polar residues in the dimer-interface positions within the central heptad of the coiled-coil formed by the S-helix. Conclusion Combining these observations with previously reported mutagenesis studies on different S-helix-containing proteins we suggest that it functions as a switch that prevents constitutive activation of linked downstream

  3. Chaperones and multitasking proteins in the nucleolus: networking together for survival?

    Science.gov (United States)

    Bański, Piotr; Kodiha, Mohamed; Stochaj, Ursula

    2010-07-01

    The nucleolus has emerged as a key player that regulates cell growth, survival and the recovery from stress. Progress in proteomics made it possible to sequence the nucleolar proteome under different physiological conditions. Together with other research, this work revealed the presence of multiple chaperones and co-chaperones in the nucleolus. Molecular chaperones are components of a larger network that promotes protein homeostasis, thereby providing continuous adaptation to a changing environment. Recent studies suggest that the cellular chaperone network is divided into individual branches which orchestrate specific functions. Input from separate branches is then combined to 'fine-tune' the cellular proteostasis network. Based on the latest developments in nucleolar and chaperone biology, we speculate that a unique network comprising chaperones, co-chaperones and multitasking proteins is located in nucleoli. This network supports and regulates fundamental biological processes, including ribosome biogenesis, cell signaling, and the stress response. Copyright 2010 Elsevier Ltd. All rights reserved.

  4. Gγ recruitment system incorporating a novel signal amplification circuit to screen transient protein-protein interactions.

    Science.gov (United States)

    Fukuda, Nobuo; Ishii, Jun; Kondo, Akihiko

    2011-09-01

    Weak and transient protein-protein interactions are associated with biological processes, but many are still undefined because of the difficulties in their identification. Here, we describe a redesigned method to screen transient protein-protein interactions by using a novel signal amplification circuit, which is incorporated into yeast to artificially magnify the signal responding to the interactions. This refined method is based on the previously established Gγ recruitment system, which utilizes yeast G-protein signaling and mating growth selection to screen interacting protein pairs. In the current study, to test the capability of our method, we chose mutants of the Z-domain derived from Staphylococcus aureus protein A as candidate proteins, and the Fc region of human IgG as the counterpart. By introduction of an artificial signal amplifier into the previous Gγ recruitment system, the signal transduction responding to transient interactions between Z-domain mutants and the Fc region with significantly low affinity (8.0 × 10(3) M(-1)) was successfully amplified in recombinant haploid yeast cells. As a result of zygosis with the opposite mating type of wild-type haploid cells, diploid colonies were vigorously and selectively generated on the screening plates, whereas our previous system rarely produced positive colonies. This new approach will be useful for exploring the numerous transient interactions that remain undefined because of the lack of powerful screening tools for their identification. © 2011 The Authors Journal compilation © 2011 FEBS.

  5. Security Enhancement of Wireless Sensor Networks Using Signal Intervals.

    Science.gov (United States)

    Moon, Jaegeun; Jung, Im Y; Yoo, Jaesoo

    2017-04-02

    Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.

  6. Security Enhancement of Wireless Sensor Networks Using Signal Intervals

    Directory of Open Access Journals (Sweden)

    Jaegeun Moon

    2017-04-01

    Full Text Available Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP, the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.

  7. Security Enhancement of Wireless Sensor Networks Using Signal Intervals

    Science.gov (United States)

    Moon, Jaegeun; Jung, Im Y.; Yoo, Jaesoo

    2017-01-01

    Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users. PMID:28368341

  8. Membrane recruitment of scaffold proteins drives specific signaling.

    Directory of Open Access Journals (Sweden)

    Frédéric Pincet

    Full Text Available Cells must give the right response to each stimulus they receive. Scaffolding, a signaling process mediated by scaffold proteins, participates in the decoding of the cues by specifically directing signal transduction. The aim of this paper is to describe the molecular mechanisms of scaffolding, i.e. the principles by which scaffold proteins drive a specific response of the cell. Since similar scaffold proteins are found in many species, they evolved according to the purpose of each organism. This means they require adaptability. In the usual description of the mechanisms of scaffolding, scaffold proteins are considered as reactors where molecules involved in a cascade of reactions are simultaneously bound with the right orientation to meet and interact. This description is not realistic: (i it is not verified by experiments and (ii timing and orientation constraints make it complex which seems to contradict the required adaptability. A scaffold protein, Ste5, is used in the MAPK pathway of Saccharomyces cerevisiae for the cell to provide a specific response to stimuli. The massive amount of data available for this pathway makes it ideal to investigate the actual mechanisms of scaffolding. Here, a complete treatment of the chemical reactions allows the computation of the distributions of all the proteins involved in the MAPK pathway when the cell receives various cues. These distributions are compared to several experimental results. It turns out that the molecular mechanisms of scaffolding are much simpler and more adaptable than previously thought in the reactor model. Scaffold proteins bind only one molecule at a time. Then, their membrane recruitment automatically drives specific, amplified and localized signal transductions. The mechanisms presented here, which explain how the membrane recruitment of a protein can produce a drastic change in the activity of cells, are generic and may be commonly used in many biological processes.

  9. Predicting protein subcellular location using digital signal processing.

    Science.gov (United States)

    Pan, Yu-Xi; Li, Da-Wei; Duan, Yun; Zhang, Zhi-Zhou; Xu, Ming-Qing; Feng, Guo-Yin; He, Lin

    2005-02-01

    The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.

  10. Biomolecular Simulation of Base Excision Repair and Protein Signaling

    Energy Technology Data Exchange (ETDEWEB)

    Straatsma, TP; McCammon, J A; Miller, John H; Smith, Paul E; Vorpagel, Erich R; Wong, Chung F; Zacharias, Martin W

    2006-03-03

    The goal of the Biomolecular Simulation of Base Excision Repair and Protein Signaling project is to enhance our understanding of the mechanism of human polymerase-β, one of the key enzymes in base excision repair (BER) and the cell-signaling enzymes cyclic-AMP-dependent protein kinase. This work used molecular modeling and simulation studies to specifically focus on the • dynamics of DNA and damaged DNA • dynamics and energetics of base flipping in DNA • mechanism and fidelity of nucleotide insertion by BER enzyme human polymerase-β • mechanism and inhibitor design for cyclic-AMP-dependent protein kinase. Molecular dynamics simulations and electronic structure calculations have been performed using the computer resources at the Molecular Science Computing Facility at the Environmental Molecular Sciences Laboratory.

  11. Modelling intracellular signalling networks using behaviour-based systems and the blackboard architecture

    OpenAIRE

    Perez, Pedro Pablo Gonzalez; Gershenson, Carlos; Garcia, Maura Cardenas; Otero, Jaime Lagunez

    2002-01-01

    This paper proposes to model the intracellular signalling networks using a fusion of behaviour-based systems and the blackboard architecture. In virtue of this fusion, the model developed by us, which has been named Cellulat, allows to take account two essential aspects of the intracellular signalling networks: (1) the cognitive capabilities of certain types of networks' components and (2) the high level of spatial organization of these networks. A simple example of modelling of Ca2+ signalli...

  12. Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.

    Science.gov (United States)

    Du, Donglei; Lee, Connie F; Li, Xiu-Qing

    2012-01-01

    Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell.

  13. Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.

    Directory of Open Access Journals (Sweden)

    Donglei Du

    Full Text Available Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A What is the general difference between signal emitting and receiving in a protein interactome? B Which proteins are among the top ranked in directional ranking? C Are high ranked proteins more evolutionarily conserved than low ranked ones? D Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell.

  14. Systematic Differences in Signal Emitting and Receiving Revealed by PageRank Analysis of a Human Protein Interactome

    Science.gov (United States)

    Li, Xiu-Qing

    2012-01-01

    Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell. PMID:23028653

  15. Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    Full Text Available Aberrant activation of sonic Hegdehog (SHH signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs. To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays. We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

  16. Discovery of nitrate-CPK-NLP signalling in central nutrient-growth networks.

    Science.gov (United States)

    Liu, Kun-Hsiang; Niu, Yajie; Konishi, Mineko; Wu, Yue; Du, Hao; Sun Chung, Hoo; Li, Lei; Boudsocq, Marie; McCormack, Matthew; Maekawa, Shugo; Ishida, Tetsuya; Zhang, Chao; Shokat, Kevan; Yanagisawa, Shuichi; Sheen, Jen

    2017-05-18

    Nutrient signalling integrates and coordinates gene expression, metabolism and growth. However, its primary molecular mechanisms remain incompletely understood in plants and animals. Here we report unique Ca 2+ signalling triggered by nitrate with live imaging of an ultrasensitive biosensor in Arabidopsis leaves and roots. A nitrate-sensitized and targeted functional genomic screen identifies subgroup III Ca 2+ -sensor protein kinases (CPKs) as master regulators that orchestrate primary nitrate responses. A chemical switch with the engineered mutant CPK10(M141G) circumvents embryo lethality and enables conditional analyses of cpk10 cpk30 cpk32 triple mutants to define comprehensive nitrate-associated regulatory and developmental programs. Nitrate-coupled CPK signalling phosphorylates conserved NIN-LIKE PROTEIN (NLP) transcription factors to specify the reprogramming of gene sets for downstream transcription factors, transporters, nitrogen assimilation, carbon/nitrogen metabolism, redox, signalling, hormones and proliferation. Conditional cpk10 cpk30 cpk32 and nlp7 mutants similarly impair nitrate-stimulated system-wide shoot growth and root establishment. The nutrient-coupled Ca 2+ signalling network integrates transcriptome and cellular metabolism with shoot-root coordination and developmental plasticity in shaping organ biomass and architecture.

  17. The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms.

    Science.gov (United States)

    Tripathi, Sushil; Flobak, Åsmund; Chawla, Konika; Baudot, Anaïs; Bruland, Torunn; Thommesen, Liv; Kuiper, Martin; Lægreid, Astrid

    2015-07-24

    The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders. We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO ( http://sblab.celldesigner.org:18080/Payao11/bin/). We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of

  18. Genome-Wide Analysis of the TORC1 and Osmotic Stress Signaling Network in Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Jeremy Worley

    2016-02-01

    Full Text Available The Target of Rapamycin kinase Complex I (TORC1 is a master regulator of cell growth and metabolism in eukaryotes. Studies in yeast and human cells have shown that nitrogen/amino acid starvation signals act through Npr2/Npr3 and the small GTPases Gtr1/Gtr2 (Rags in humans to inhibit TORC1. However, it is unclear how other stress and starvation stimuli inhibit TORC1, and/or act in parallel with the TORC1 pathway, to control cell growth. To help answer these questions, we developed a novel automated pipeline and used it to measure the expression of a TORC1-dependent ribosome biogenesis gene (NSR1 during osmotic stress in 4700 Saccharomyces cerevisiae strains from the yeast knock-out collection. This led to the identification of 440 strains with significant and reproducible defects in NSR1 repression. The cell growth control and stress response proteins deleted in these strains form a highly connected network, including 56 proteins involved in vesicle trafficking and vacuolar function; 53 proteins that act downstream of TORC1 according to a rapamycin assay—including components of the HDAC Rpd3L, Elongator, and the INO80, CAF-1 and SWI/SNF chromatin remodeling complexes; over 100 proteins involved in signaling and metabolism; and 17 proteins that directly interact with TORC1. These data provide an important resource for labs studying cell growth control and stress signaling, and demonstrate the utility of our new, and easily adaptable, method for mapping gene regulatory networks.

  19. Dissection of the cis-2-decenoic acid signaling network in Pseudomonas aeruginosa using microarray technique

    Directory of Open Access Journals (Sweden)

    Azadeh eRahmani-Badi

    2015-04-01

    Full Text Available Many bacterial pathogens use quorum-sensing (QS signaling to regulate the expression of factors contributing to virulence and persistence. Bacteria produce signals of different chemical classes. The signal molecule, known as diffusible signal factor (DSF, is a cis-unsaturated fatty acid that was first described in the plant pathogen Xanthomonas campestris. Previous works have shown that human pathogen, Pseudomonas aeruginosa, also synthesizes a structurally related molecule, characterized as cis-2-decenoic acid (C10: Δ2, CDA that induces biofilm dispersal by multiple types of bacteria. Furthermore, CDA has been shown to be involved in inter-kingdom signaling that modulates fungal behavior. Therefore, an understanding of its signaling mechanism could suggest strategies for interference, with consequences for disease control. To identify the components of CDA signaling pathway in this pathogen, a comparative transcritpome analysis was conducted, in the presence and absence of CDA. A protein-protein interaction (PPI network for differentially expressed (DE genes with known function was then constructed by STRING and Cytoscape. In addition, the effects of CDA in combination with antimicrobial agents on the biofilm surface area and bacteria viability were evaluated using fluorescence microscopy and digital image analysis. Microarray analysis identified 666 differentially expressed genes in the presence of CDA and gene ontology (GO analysis revealed that in P. aeruginosa, CDA mediates dispersion of biofilms through signaling pathways, including enhanced motility, virulence as well as persistence at different temperatures. PPI data suggested that a cluster of five genes (PA4978, PA4979, PA4980, PA4982, PA4983 is involved in the CDA synthesis and perception. Combined treatments using both CDA and antimicrobial agents showed that following exposure of the biofilms to CDA, remaining cells on the surface were easily removed and killed by antimicrobials.

  20. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

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

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

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

  2. Topological properties of four networks in protein structures

    Science.gov (United States)

    Min, Seungsik; Kim, Kyungsik; Chang, Ki-Ho; Ha, Deok-Ho; Lee, Jun-Ho

    2017-11-01

    In this paper, we investigate the complex networks of interacting amino acids in protein structures. The cellular networks and their random controls are treated for the four threshold distances between atoms. The numerical simulation and analysis are relevant to the topological properties of the complex networks in the structural classification of proteins, and we mainly estimate the network's metrics from the resultant network. The cellular network is shown to exhibit a small-world feature regardless of their structural class. The protein structure presents the positive assortative coefficients, when the topological property is described as a tendency for connectivity of high-degree nodes. We particularly show that both the modularity and the small-wordness are significantly followed the increasing function against nodes.

  3. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  4. Proteomic Analysis of the Downstream Signaling Network of PARP1.

    Science.gov (United States)

    Zhen, Yuanli; Yu, Yonghao

    2018-01-19

    Poly-ADP-ribosylation (PARylation) is a protein posttranslational modification (PTM) that is critically involved in many biological processes that are linked to cell stress responses. It is catalyzed by a class of enzymes known as poly-ADP-ribose polymerases (PARPs). In particular, PARP1 is a nuclear protein that is activated upon sensing nicked DNA. Once activated, PARP1 is responsible for the synthesis of a large number of PARylated proteins and initiation of the DNA damage response mechanisms. This observation provided the rationale for developing PARP1 inhibitors for the treatment of human malignancies. Indeed, three PARP1 inhibitors (Olaparib, Rucaparib, and Niraparib) have recently been approved by the Food and Drug Administration for the treatment of ovarian cancer. Moreover, in 2017, both Olaparib and Niraparib have also been approved for the treatment of fallopian tube cancer and primary peritoneal cancer. Despite this very exciting progress in the clinic, the basic signaling mechanism that connects PARP1 to a diverse array of biological processes is still poorly understood. This is, in large part, due to the inherent technical difficulty associated with the analysis of protein PARylation, which is a low-abundance, labile, and heterogeneous PTM. The study of PARylation has been greatly facilitated by the recent advances in mass spectrometry-based proteomic technologies tailored to the analysis of this modification. In this Perspective, we discuss these breakthroughs, including their technical development, and applications that provide a global view of the many biological processes regulated by this important protein modification.

  5. Automated Measurement and Signaling Systems for the Transactional Network

    Energy Technology Data Exchange (ETDEWEB)

    Piette, Mary Ann; Brown, Richard; Price, Phillip; Page, Janie; Granderson, Jessica; Riess, David; Czarnecki, Stephen; Ghatikar, Girish; Lanzisera, Steven

    2013-12-31

    The Transactional Network Project is a multi-lab activity funded by the US Department of Energy?s Building Technologies Office. The project team included staff from Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The team designed, prototyped and tested a transactional network (TN) platform to support energy, operational and financial transactions between any networked entities (equipment, organizations, buildings, grid, etc.). PNNL was responsible for the development of the TN platform, with agents for this platform developed by each of the three labs. LBNL contributed applications to measure the whole-building electric load response to various changes in building operations, particularly energy efficiency improvements and demand response events. We also provide a demand response signaling agent and an agent for cost savings analysis. LBNL and PNNL demonstrated actual transactions between packaged rooftop units and the electric grid using the platform and selected agents. This document describes the agents and applications developed by the LBNL team, and associated tests of the applications.

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

    Science.gov (United States)

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

    2017-07-25

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

  7. The Role of Unfolded Protein Response and Mitogen-Activated Protein Kinase Signaling in Neurodegenerative Diseases with Special Focus on Prion Diseases

    Directory of Open Access Journals (Sweden)

    Lifeng Yang

    2017-05-01

    Full Text Available Prion diseases are neurodegenerative pathologies characterized by the accumulation of a protease-resistant form of the cellular prion protein named prion protein scrapie (PrPSc in the brain. PrPSc accumulation in the endoplasmic reticulum (ER result in a dysregulated calcium (Ca2+ homeostasis and subsequent initiation of unfolded protein response (UPR leading to neuronal dysfunction and apoptosis. The molecular mechanisms for the transition between adaptation to ER stress and ER stress-induced apoptosis are still unclear. Mitogen-activated protein kinases (MAPKs are serine/threonine protein kinases that rule the signaling of many extracellular stimuli from plasma membrane to the nucleus. However the identification of numerous points of cross talk between the UPR and MAPK signaling pathways may contribute to our understanding of the consequences of ER stress in prion diseases. Indeed the MAPK signaling network is known to regulate cell cycle progression and cell survival or death responses following a variety of stresses including misfolded protein response stress. In this article, we review the UPR signaling in prion diseases and discuss the triad of MAPK signaling pathways. We also describe the role played by MAPK signaling cascades in Alzheimer’s (AD and Parkinson’s disease (PD. We will also overview the mechanisms of cell death and the role of MAPK signaling in prion disease progression and highlight potential avenues for therapeutic intervention.

  8. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

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

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

    DEFF Research Database (Denmark)

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

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In......Web_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism....

  10. Mirin: identifying microRNA regulatory modules in protein-protein interaction networks

    National Research Council Canada - National Science Library

    Yang, Ken-Chi; Hsu, Chia-Lang; Lin, Chen-Ching; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-01-01

    .... Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers...

  11. Arabidopsis protein phosphatase DBP1 nucleates a protein network with a role in regulating plant defense.

    Directory of Open Access Journals (Sweden)

    José Luis Carrasco

    Full Text Available Arabidopsis thaliana DBP1 belongs to the plant-specific family of DNA-binding protein phosphatases. Although recently identified as a novel host factor mediating susceptibility to potyvirus, little is known about DBP1 targets and partners and the molecular mechanisms underlying its function. Analyzing changes in the phosphoproteome of a loss-of-function dbp1 mutant enabled the identification of 14-3-3λ isoform (GRF6, a previously reported DBP1 interactor, and MAP kinase (MAPK MPK11 as components of a small protein network nucleated by DBP1, in which GRF6 stability is modulated by MPK11 through phosphorylation, while DBP1 in turn negatively regulates MPK11 activity. Interestingly, grf6 and mpk11 loss-of-function mutants showed altered response to infection by the potyvirus Plum pox virus (PPV, and the described molecular mechanism controlling GRF6 stability was recapitulated upon PPV infection. These results not only contribute to a better knowledge of the biology of DBP factors, but also of MAPK signalling in plants, with the identification of GRF6 as a likely MPK11 substrate and of DBP1 as a protein phosphatase regulating MPK11 activity, and unveils the implication of this protein module in the response to PPV infection in Arabidopsis.

  12. Specific inhibition of GPCR-independent G protein signaling by a rationally engineered protein.

    Science.gov (United States)

    Leyme, Anthony; Marivin, Arthur; Maziarz, Marcin; DiGiacomo, Vincent; Papakonstantinou, Maria P; Patel, Prachi P; Blanco-Canosa, Juan B; Walawalkar, Isha A; Rodriguez-Davila, Gonzalo; Dominguez, Isabel; Garcia-Marcos, Mikel

    2017-11-28

    Activation of heterotrimeric G proteins by cytoplasmic nonreceptor proteins is an alternative to the classical mechanism via G protein-coupled receptors (GPCRs). A subset of nonreceptor G protein activators is characterized by a conserved sequence named the Gα-binding and activating (GBA) motif, which confers guanine nucleotide exchange factor (GEF) activity in vitro and promotes G protein-dependent signaling in cells. GBA proteins have important roles in physiology and disease but remain greatly understudied. This is due, in part, to the lack of efficient tools that specifically disrupt GBA motif function in the context of the large multifunctional proteins in which they are embedded. This hindrance to the study of alternative mechanisms of G protein activation contrasts with the wealth of convenient chemical and genetic tools to manipulate GPCR-dependent activation. Here, we describe the rational design and implementation of a genetically encoded protein that specifically inhibits GBA motifs: GBA inhibitor (GBAi). GBAi was engineered by introducing modifications in Gαi that preclude coupling to every known major binding partner [GPCRs, Gβγ, effectors, guanine nucleotide dissociation inhibitors (GDIs), GTPase-activating proteins (GAPs), or the chaperone/GEF Ric-8A], while favoring high-affinity binding to all known GBA motifs. We demonstrate that GBAi does not interfere with canonical GPCR-G protein signaling but blocks GBA-dependent signaling in cancer cells. Furthermore, by implementing GBAi in vivo, we show that GBA-dependent signaling modulates phenotypes during Xenopus laevis embryonic development. In summary, GBAi is a selective, efficient, and convenient tool to dissect the biological processes controlled by a GPCR-independent mechanism of G protein activation mediated by cytoplasmic factors.

  13. Simulated evolution of protein-protein interaction networks with realistic topology.

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Divya Mistry

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

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

    Science.gov (United States)

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

    2017-01-01

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

  16. Application of computational approaches to study signalling networks of nuclear and Tyrosine kinase receptors

    Directory of Open Access Journals (Sweden)

    Rebaï Ahmed

    2010-10-01

    Full Text Available Abstract Background Nuclear receptors (NRs and Receptor tyrosine kinases (RTKs are essential proteins in many cellular processes and sequence variations in their genes have been reported to be involved in many diseases including cancer. Although crosstalk between RTK and NR signalling and their contribution to the development of endocrine regulated cancers have been areas of intense investigation, the direct coupling of their signalling pathways remains elusive. In our understanding of the role and function of nuclear receptors on the cell membrane the interactions between nuclear receptors and tyrosine kinase receptors deserve further attention. Results We constructed a human signalling network containing nuclear receptors and tyrosine kinase receptors that identified a network topology involving eleven highly connected hubs. We further developed an integrated knowledge database, denominated NR-RTK database dedicated to human RTKs and NRs and their vertebrate orthologs and their interactions. These interactions were inferred using computational tools and those supported by literature evidence are indicated. NR-RTK database contains links to other relevant resources and includes data on receptor ligands. It aims to provide a comprehensive interaction map that identifies complex dynamics and potential crosstalk involved. Availability: NR-RTK database is accessible at http://www.bioinfo-cbs.org/NR-RTK/ Conclusions We infer that the NR-RTK interaction network is scale-free topology. We also uncovered the key receptors mediating the signal transduction between these two types of receptors. Furthermore, NR-RTK database is expected to be useful for researchers working on various aspects of the molecular basis of signal transduction by RTKs and NRs. Reviewers This article was reviewed by Professor Paul Harrison (nominated by Dr. Mark Gerstein, Dr. Arcady Mushegian and Dr. Anthony Almudevar.

  17. Going the distance for protein function prediction: a new distance metric for protein interaction networks.

    Science.gov (United States)

    Cao, Mengfei; Zhang, Hao; Park, Jisoo; Daniels, Noah M; Crovella, Mark E; Cowen, Lenore J; Hescott, Benjamin

    2013-01-01

    In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.

  18. The role of protein interaction networks in systems biomedicine

    Directory of Open Access Journals (Sweden)

    Tuba Sevimoglu

    2014-08-01

    Full Text Available The challenging task of studying and modeling complex dynamics of biological systems in order to describe various human diseases has gathered great interest in recent years. Major biological processes are mediated through protein interactions, hence there is a need to understand the chaotic network that forms these processes in pursuance of understanding human diseases. The applications of protein interaction networks to disease datasets allow the identification of genes and proteins associated with diseases, the study of network properties, identification of subnetworks, and network-based disease gene classification. Although various protein interaction network analysis strategies have been employed, grand challenges are still existing. Global understanding of protein interaction networks via integration of high-throughput functional genomics data from different levels will allow researchers to examine the disease pathways and identify strategies to control them. As a result, it seems likely that more personalized, more accurate and more rapid disease gene diagnostic techniques will be devised in the future, as well as novel strategies that are more personalized. This mini-review summarizes the current practice of protein interaction networks in medical research as well as challenges to be overcome.

  19. G-protein coupled receptor signaling architecture of mammalian immune cells.

    Directory of Open Access Journals (Sweden)

    Natalia Polouliakh

    Full Text Available A series of recent studies on large-scale networks of signaling and metabolic systems revealed that a certain network structure often called "bow-tie network" are observed. In signaling systems, bow-tie network takes a form with diverse and redundant inputs and outputs connected via a small numbers of core molecules. While arguments have been made that such network architecture enhances robustness and evolvability of biological systems, its functional role at a cellular level remains obscure. A hypothesis was proposed that such a network function as a stimuli-reaction classifier where dynamics of core molecules dictate downstream transcriptional activities, hence physiological responses against stimuli. In this study, we examined whether such hypothesis can be verified using experimental data from Alliance for Cellular Signaling (AfCS that comprehensively measured GPCR related ligands response for B-cell and macrophage. In a GPCR signaling system, cAMP and Ca2+ act as core molecules. Stimuli-response for 32 ligands to B-Cells and 23 ligands to macrophages has been measured. We found that ligands with correlated changes of cAMP and Ca2+ tend to cluster closely together within the hyperspaces of both cell types and they induced genes involved in the same cellular processes. It was found that ligands inducing cAMP synthesis activate genes involved in cell growth and proliferation; cAMP and Ca2+ molecules that increased together form a feedback loop and induce immune cells to migrate and adhere together. In contrast, ligands without a core molecules response are scattered throughout the hyperspace and do not share clusters. G-protein coupling receptors together with immune response specific receptors were found in cAMP and Ca2+ activated clusters. Analyses have been done on the original software applicable for discovering 'bow-tie' network architectures within the complex network of intracellular signaling where ab initio clustering has been

  20. Calcium-Dependent Protein Kinases in Phytohormone Signaling Pathways

    OpenAIRE

    Wuwu Xu; Wenchao Huang

    2017-01-01

    Calcium-dependent protein kinases (CPKs/CDPKs) are Ca2+-sensors that decode Ca2+ signals into specific physiological responses. Research has reported that CDPKs constitute a large multigene family in various plant species, and play diverse roles in plant growth, development, and stress responses. Although numerous CDPKs have been exhaustively studied, and many of them have been found to be involved in plant hormone biosynthesis and response mechanisms, a comprehensive overview of the manner i...

  1. Protein interaction networks--more than mere modules.

    Directory of Open Access Journals (Sweden)

    Stefan Pinkert

    2010-01-01

    Full Text Available It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a "module" in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a "module". In a self-consistent manner, proteins are grouped into "functional roles" if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network's structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function

  2. A novel function prediction approach using protein overlap networks.

    Science.gov (United States)

    Liang, Shide; Zheng, Dandan; Standley, Daron M; Guo, Huarong; Zhang, Chi

    2013-07-17

    Construction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database. The function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein. The PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction.

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

    Science.gov (United States)

    Wise, Roger P.; Dickerson, Julie A.

    2017-01-01

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

  4. Detecting malicious chaotic signals in wireless sensor network

    Science.gov (United States)

    Upadhyay, Ranjit Kumar; Kumari, Sangeeta

    2018-02-01

    In this paper, an e-epidemic Susceptible-Infected-Vaccinated (SIV) model has been proposed to analyze the effect of node immunization and worms attacking dynamics in wireless sensor network. A modified nonlinear incidence rate with cyrtoid type functional response has been considered using sleep and active mode approach. Detailed stability analysis and the sufficient criteria for the persistence of the model system have been established. We also established different types of bifurcation analysis for different equilibria at different critical points of the control parameters. We performed a detailed Hopf bifurcation analysis and determine the direction and stability of the bifurcating periodic solutions using center manifold theorem. Numerical simulations are carried out to confirm the theoretical results. The impact of the control parameters on the dynamics of the model system has been investigated and malicious chaotic signals are detected. Finally, we have analyzed the effect of time delay on the dynamics of the model system.

  5. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2017-08-01

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  6. The Hedgehog Signal Induced Modulation of Bone Morphogenetic Protein Signaling: An Essential Signaling Relay for Urinary Tract Morphogenesis

    Science.gov (United States)

    Nakagata, Naomi; Miyagawa, Shinichi; Suzuki, Kentaro; Kitazawa, Sohei; Yamada, Gen

    2012-01-01

    Background Congenital diseases of the urinary tract are frequently observed in infants. Such diseases present a number of developmental anomalies such as hydroureter and hydronephrosis. Although some genetically-modified mouse models of growth factor signaling genes reproduce urinary phenotypes, the pathogenic mechanisms remain obscure. Previous studies suggest that a portion of the cells in the external genitalia and bladder are derived from peri-cloacal mesenchymal cells that receive Hedgehog (Hh) signaling in the early developmental stages. We hypothesized that defects in such progenitor cells, which give rise to urinary tract tissues, may be a cause of such diseases. Methodology/Principal Findings To elucidate the pathogenic mechanisms of upper urinary tract malformations, we analyzed a series of Sonic hedgehog (Shh) deficient mice. Shh−/− displayed hydroureter and hydronephrosis phenotypes and reduced expression of several developmental markers. In addition, we suggested that Shh modulation at an early embryonic stage is responsible for such phenotypes by analyzing the Shh conditional mutants. Tissue contribution assays of Hh-responsive cells revealed that peri-cloacal mesenchymal cells, which received Hh signal secreted from cloacal epithelium, could contribute to the ureteral mesenchyme. Gain- and loss-of-functional mutants for Hh signaling revealed a correlation between Hh signaling and Bone morphogenetic protein (Bmp) signaling. Finally, a conditional ablation of Bmp receptor type IA (BmprIA) gene was examined in Hh-responsive cell lineages. This system thus made it possible to analyze the primary functions of the growth factor signaling relay. The defective Hh-to-Bmp signaling relay resulted in severe urinary tract phenotypes with a decrease in the number of Hh-responsive cells. Conclusions/Significance This study identified the essential embryonic stages for the pathogenesis of urinary tract phenotypes. These results suggested that Hh

  7. Type One Protein Phosphatase 1 and Its Regulatory Protein Inhibitor 2 Negatively Regulate ABA Signaling

    Science.gov (United States)

    Zhao, Yang; Xie, Shaojun; Batelli, Giorgia; Wang, Bangshing; Duan, Cheng-Guo; Wang, Xingang; Xing, Lu; Lei, Mingguang; Yan, Jun; Zhu, Xiaohong; Zhu, Jian-Kang

    2016-01-01

    The phytohormone abscisic acid (ABA) regulates plant growth, development and responses to biotic and abiotic stresses. The core ABA signaling pathway consists of three major components: ABA receptor (PYR1/PYLs), type 2C Protein Phosphatase (PP2C) and SNF1-related protein kinase 2 (SnRK2). Nevertheless, the complexity of ABA signaling remains to be explored. To uncover new components of ABA signal transduction pathways, we performed a yeast two-hybrid screen for SnRK2-interacting proteins. We found that Type One Protein Phosphatase 1 (TOPP1) and its regulatory protein, At Inhibitor-2 (AtI-2), physically interact with SnRK2s and also with PYLs. TOPP1 inhibited the kinase activity of SnRK2.6, and this inhibition could be enhanced by AtI-2. Transactivation assays showed that TOPP1 and AtI-2 negatively regulated the SnRK2.2/3/6-mediated activation of the ABA responsive reporter gene RD29B, supporting a negative role of TOPP1 and AtI-2 in ABA signaling. Consistent with these findings, topp1 and ati-2 mutant plants displayed hypersensitivities to ABA and salt treatments, and transcriptome analysis of TOPP1 and AtI-2 knockout plants revealed an increased expression of multiple ABA-responsive genes in the mutants. Taken together, our results uncover TOPP1 and AtI-2 as negative regulators of ABA signaling. PMID:26943172

  8. An Evolutionarily Conserved Innate Immunity Protein Interaction Network*

    Science.gov (United States)

    De Arras, Lesly; Seng, Amara; Lackford, Brad; Keikhaee, Mohammad R.; Bowerman, Bruce; Freedman, Jonathan H.; Schwartz, David A.; Alper, Scott

    2013-01-01

    The innate immune response plays a critical role in fighting infection; however, innate immunity also can affect the pathogenesis of a variety of diseases, including sepsis, asthma, cancer, and atherosclerosis. To identify novel regulators of innate immunity, we performed comparative genomics RNA interference screens in the nematode Caenorhabditis elegans and mouse macrophages. These screens have uncovered many candidate regulators of the response to lipopolysaccharide (LPS), several of which interact physically in multiple species to form an innate immunity protein interaction network. This protein interaction network contains several proteins in the canonical LPS-responsive TLR4 pathway as well as many novel interacting proteins. Using RNAi and overexpression studies, we show that almost every gene in this network can modulate the innate immune response in mouse cell lines. We validate the importance of this network in innate immunity regulation in vivo using available mutants in C. elegans and mice. PMID:23209288

  9. Neural Network Algorithm for Prediction of Secondary Protein Structure

    National Research Council Canada - National Science Library

    Zikrija Avdagic; Elvir Purisevic; Emir Buza; Zlatan Coralic

    2009-01-01

    .... In this paper we describe the method and results of using CB513 as a dataset suitable for development of artificial neural network algorithms for prediction of secondary protein structure with MATLAB...

  10. Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

    Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

  11. PADPIN: protein-protein interaction networks of angiogenesis, arteriogenesis, and inflammation in peripheral arterial disease

    Science.gov (United States)

    Vijay, Chaitanya G.; Annex, Brian H.; Bader, Joel S.; Popel, Aleksander S.

    2015-01-01

    Peripheral arterial disease (PAD) results from an obstruction of blood flow in the arteries other than the heart, most commonly the arteries that supply the legs. The complexity of the known signaling pathways involved in PAD, including various growth factor pathways and their cross talks, suggests that analyses of high-throughput experimental data could lead to a new level of understanding of the disease as well as novel and heretofore unanticipated potential targets. Such bioinformatic analyses have not been systematically performed for PAD. We constructed global protein-protein interaction networks of angiogenesis (Angiome), immune response (Immunome), and arteriogenesis (Arteriome) using our previously developed algorithm GeneHits. The term “PADPIN” refers to the angiome, immunome, and arteriome in PAD. Here we analyze four microarray gene expression datasets from ischemic and nonischemic gastrocnemius muscles at day 3 posthindlimb ischemia (HLI) in two genetically different C57BL/6 and BALB/c mouse strains that display differential susceptibility to HLI to identify potential targets and signaling pathways in angiogenesis, immune, and arteriogenesis networks. We hypothesize that identification of the differentially expressed genes in ischemic and nonischemic muscles between the strains that recovers better (C57BL/6) vs. the strain that recovers more poorly (BALB/c) will help for the prediction of target genes in PAD. Our bioinformatics analysis identified several genes that are differentially expressed between the two mouse strains with known functions in PAD including TLR4, THBS1, and PRKAA2 and several genes with unknown functions in PAD including EphA4, TSPAN7, SLC22A4, and EIF2a. PMID:26058837

  12. The Medicago truncatula DMI1 protein modulates cytosolic calcium signaling

    DEFF Research Database (Denmark)

    Peiter, Edgar; Sun, Jongho; Heckmann, Anne Birgitte Lau

    2007-01-01

    nodulation have been cloned in model legumes. Among them, Medicago truncatula DMI1 (DOESN'T MAKE INFECTIONS1) is required for the generation of nucleus-associated calcium spikes in response to the rhizobial signaling molecule Nod factor. DMI1 encodes a membrane protein with striking similarities...... to the Methanobacterium thermoautotrophicum potassium channel (MthK). The cytosolic C terminus of DMI1 contains a RCK (regulator of the conductance of K+) domain that in MthK acts as a calcium-regulated gating ring controlling the activity of the channel. Here we show that a dmi1 mutant lacking the entire C terminus acts...... as a dominant-negative allele interfering with the formation of nitrogen-fixing nodules and abolishing the induction of calcium spikes by the G-protein agonist Mastoparan. Using both the full-length DMI1 and this dominant-negative mutant protein we show that DMI1 increases the sensitivity of a sodium...

  13. S-Glutathionylation and Redox Protein Signaling in Drug Addiction.

    Science.gov (United States)

    Womersley, Jacqueline S; Uys, Joachim D

    2016-01-01

    Drug addiction is a chronic relapsing disorder that comes at a high cost to individuals and society. Therefore understanding the mechanisms by which drugs exert their effects is of prime importance. Drugs of abuse increase the production of reactive oxygen and nitrogen species resulting in oxidative stress. This change in redox homeostasis increases the conjugation of glutathione to protein cysteine residues; a process called S-glutathionylation. Although traditionally regarded as a protective mechanism against irreversible protein oxidation, accumulated evidence suggests a more nuanced role for S-glutathionylation, namely as a mediator in redox-sensitive protein signaling. The reversible modification of protein thiols leading to alteration in function under different physiologic/pathologic conditions provides a mechanism whereby change in redox status can be translated into a functional response. As such, S-glutathionylation represents an understudied means of post-translational protein modification that may be important in the mechanisms underlying drug addiction. This review will discuss the evidence for S-glutathionylation as a redox-sensing mechanism and how this may be involved in the response to drug-induced oxidative stress. The function of S-glutathionylated proteins involved in neurotransmission, dendritic spine structure, and drug-induced behavioral outputs will be reviewed with specific reference to alcohol, cocaine, and heroin. Copyright © 2016. Published by Elsevier Inc.

  14. Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2010-01-01

    Full Text Available Abstract Background We present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely social, as revealed based on the local sociality parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: a priori knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network. Results We applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced social proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. Intrinsically disordered are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity. Conclusions The approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.

  15. Consensus-based sparse signal reconstruction algorithm for wireless sensor networks

    National Research Council Canada - National Science Library

    Peng, Bao; Zhao, Zhi; Han, Guangjie; Shen, Jian

    2016-01-01

    This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter...

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

    Science.gov (United States)

    Cao, Renzhi; Cheng, Jianlin

    2016-01-15

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

  17. Architecture of the human interactome defines protein communities and disease networks.

    Science.gov (United States)

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

    2017-05-25

    The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

  18. A quantitative approach to study indirect effects among disease proteins in the human protein interaction network

    Directory of Open Access Journals (Sweden)

    Jordán Ferenc

    2010-07-01

    Full Text Available Abstract Background Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases. Results Based on the i2d and OMIM databases, we have constructed (i a network of proteins causing five selected diseases (DP, disease proteins plus their interacting partners (IP, non-disease proteins, the DPIP network and (ii a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1 various cancers, (2 heart diseases, (3 obesity, (4 diabetes and (5 autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins. Conclusions We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand

  19. A quantitative approach to study indirect effects among disease proteins in the human protein interaction network.

    Science.gov (United States)

    Nguyen, Thanh-Phuong; Jordán, Ferenc

    2010-07-29

    Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases. Based on the i2d and OMIM databases, we have constructed (i) a network of proteins causing five selected diseases (DP, disease proteins) plus their interacting partners (IP, non-disease proteins), the DPIP network and (ii) a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1) various cancers, (2) heart diseases, (3) obesity, (4) diabetes and (5) autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins. We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand particular pathways. We have found that the mediators

  20. Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks.

    Science.gov (United States)

    Neapolitan, Richard; Xue, Diyang; Jiang, Xia

    2014-01-01

    This paper concerns a study indicating that the expression levels of genes in signaling pathways can be modeled using a causal Bayesian network (BN) that is altered in tumorous tissue. These results open up promising areas of future research that can help identify driver genes and therapeutic targets. So, it is most appropriate for the cancer informatics community. Our central hypothesis is that the expression levels of genes that code for proteins on a signal transduction network (STP) are causally related and that this causal structure is altered when the STP is involved in cancer. To test this hypothesis, we analyzed 5 STPs associated with breast cancer, 7 STPs associated with other cancers, and 10 randomly chosen pathways, using a breast cancer gene expression level dataset containing 529 cases and 61 controls. We identified all the genes related to each of the 22 pathways and developed separate gene expression datasets for each pathway. We obtained significant results indicating that the causal structure of the expression levels of genes coding for proteins on STPs, which are believed to be implicated in both breast cancer and in all cancers, is more altered in the cases relative to the controls than the causal structure of the randomly chosen pathways.

  1. Gene essentiality and the topology of protein interaction networks

    Science.gov (United States)

    Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

    2005-01-01

    The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

  2. Protein complex detection with semi-supervised learning in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zhang Aidong

    2011-10-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection. Results Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future. Conclusions Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological

  3. Identification of Top-ranked Proteins within a Directional Protein Interaction Network using the PageRank Algorithm: Applications in Humans and Plants.

    Science.gov (United States)

    Li, Xiu-Qing; Xing, Tim; Du, Donglei

    2016-01-01

    Somatic mutation of signal transduction genes or key nodes of the cellular protein network can cause severe diseases in humans but can sometimes genetically improve plants, likely because growth is determinate in animals but indeterminate in plants. This article reviews protein networks; human protein ranking; the mitogen-activated protein kinase (MAPK) and insulin (phospho- inositide 3kinase [PI3K]/phosphatase and tensin homolog [PTEN]/protein kinase B [AKT]) signaling pathways; human diseases caused by somatic mutations to the PI3K/PTEN/ AKT pathway; use of the MAPK pathway in plant molecular breeding; and protein domain evolution. Casitas B-lineage lymphoma (CBL), PTEN, MAPK1 and PIK3CA are among PIK3CA the top-ranked proteins in directional rankings. Eight proteins (ACVR1, CDC42, RAC1, RAF1, RHOA, TGFBR1, TRAF2, and TRAF6) are ranked in the top 50 key players in both signal emission and signal reception and in interaction with many other proteins. Top-ranked proteins likely have major impacts on the network function. Such proteins are targets for drug discovery, because their mutations are implicated in various cancers and overgrowth syndromes. Appropriately managing food intake may help reduce the growth of tumors or malformation of tissues. The role of the protein kinase C/ fatty acid synthase pathway in fat deposition in PTEN/PI3K patients should be investigated. Both the MAPK and insulin signaling pathways exist in plants, and MAPK pathway engineering can improve plant tolerance to biotic and abiotic stresses such as salinity.

  4. Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

    Science.gov (United States)

    Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

    2015-01-01

    Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

  5. Regulation of Nuclear Localization of Signaling Proteins by Cytokinin

    Energy Technology Data Exchange (ETDEWEB)

    Kieber, J.J.

    2010-05-01

    Cytokinins are a class of mitogenic plant hormones that play an important role in most aspects of plant development, including shoot and root growth, vascular and photomorphogenic development and leaf senescence. A model for cytokinin perception and signaling has emerged that is similar to bacterial two-component phosphorelays. In this model, binding of cytokinin to the extracellular domain of the Arabidopsis histidine kinase (AHKs) receptors induces autophosphorylation within the intracellular histidine-kinase domain. The phosphoryl group is subsequently transferred to cytosolic Arabidopsis histidine phosphotransfer proteins (AHPs), which have been suggested to translocate to the nucleus in response to cytokinin treatment, where they then transfer the phosphoryl group to nuclear-localized response regulators (Type-A and Type-B ARRs). We examined the effects of cytokinin on AHP subcellular localization in Arabidopsis and, contrary to expectations, the AHPs maintained a constant nuclear/cytosolic distribution following cytokinin treatment. Furthermore, mutation of the conserved phosphoacceptor histidine residue of the AHP, as well as disruption of multiple cytokinin signaling elements, did not affect the subcellular localization of the AHP proteins. Finally, we present data indicating that AHPs maintain a nuclear/cytosolic distribution by balancing active transport into and out of the nucleus. Our findings suggest that the current models indicating relocalization of AHP protein into the nucleus in response to cytokinin are incorrect. Rather, AHPs actively maintain a consistent nuclear/cytosolic distribution regardless of the status of the cytokinin response pathway.

  6. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

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

  7. Exploring function prediction in protein interaction networks via clustering methods.

    Science.gov (United States)

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

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

  8. Exploring function prediction in protein interaction networks via clustering methods.

    Directory of Open Access Journals (Sweden)

    Kire Trivodaliev

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

  9. Analysis of protein folds using protein contact networks

    Indian Academy of Sciences (India)

    Proteins are important biomolecules, which perform diverse structural and functional roles in living systems. Starting from a linear chain of amino acids, proteins fold to different secondary structures, which then fold through short- and long-range interactions to give rise to the final three-dimensional shapes useful to carry out ...

  10. Protein Tyrosine Kinase Signaling During Oocyte Maturation and Fertilization

    Science.gov (United States)

    McGinnis, Lynda K.; Carroll, David J.; Kinsey, William H.

    2011-01-01

    The oocyte is a highly specialized cell capable of accumulating and storing energy supplies as well as maternal transcripts and pre-positioned signal transduction components needed for zygotic development, undergoing meiosis under control of paracrine signals from the follicle, fusing with a single sperm during fertilization, and zygotic development. The oocyte accomplishes this diverse series of events by establishing an array of signal transduction pathway components that include a select collection of protein tyrosine kinases (PTKs) that are expressed at levels significantly higher than most other cell types. This array of PTKs includes cytosolic kinases such as SRC-family PTKs (FYN and YES), and FAK kinases, as well as FER. These kinases typically exhibit distinct patterns of localization and in some cases are translocated from one subcellular compartment to another during meiosis. Significant differences exist in the extent to which PTK-mediated pathways are used by oocytes from species that fertilize externally versus internally. The PTK activation profiles as well as calcium signaling pattern seems to correlate with the extent to which a rapid block to polyspermy is required by the biology of each species. Suppression of each of the SRC-family PTKs as well as FER kinase results in failure of meiotic maturation or zygote development, indicating that these PTKs are important for oocyte quality and developmental potential. Future studies will hopefully reveal the extent to which these factors impact clinical assisted reproductive techniques in domestic animals and humans. PMID:21681843

  11. Regulation of longevity by regulator of G-protein signaling protein, Loco.

    Science.gov (United States)

    Lin, Yuh-Ru; Kim, Keetae; Yang, Yanfei; Ivessa, Andreas; Sadoshima, Junichi; Park, Yongkyu

    2011-06-01

    Regulator of G-protein signaling (RGS) proteins contribute to G-protein signaling pathways as activators or repressors with GTPase-activating protein (GAP) activity. To characterize whether regulation of RGS proteins influences longevity in several species, we measured stress responses and lifespan of RGS-overexpressing and RGS-lacking mutants. Reduced expression of Loco, a RGS protein of Drosophila melanogaster, resulted in a longer lifespan for both male and female flies, also exhibiting stronger resistance to three different stressors (starvation, oxidation, and heat) and higher manganese-containing superoxide dismutase (MnSOD) activity. In addition, this reduction in Loco expression increased fat content and diminished cAMP levels. In contrast, overexpression of both genomic and cDNA loco gene significantly shortened the lifespan with weaker stress resistance and lower fat content. Deletion analysis of the Loco demonstrated that its RGS domain is required for the regulation of longevity. Consistently, when expression of RGS14, mammalian homologue of Loco, was reduced in rat fibroblast cells, the resistance to oxidative stress increased with higher MnSOD expression. The changes of yeast Rgs2 expression, which shares a conserved RGS domain with the fly Loco protein, also altered lifespan and stress resistance in Saccharomyces cerevisiae. Here, we provide the first evidence that RGS proteins with GAP activity affect both stress resistance and longevity in several species. © 2011 The Authors. Aging Cell © 2011 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland.

  12. Semantic and layered protein function prediction from PPI networks.

    Science.gov (United States)

    Zhu, Wei; Hou, Jingyu; Chen, Yi-Ping Phoebe

    2010-11-21

    The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness. We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions. The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins. Copyright © 2010 Elsevier Ltd. All rights reserved.

  13. 5'-AMP-Activated Protein Kinase Signaling in Caenorhabditis elegans.

    Science.gov (United States)

    Ahmadi, Moloud; Roy, Richard

    AMP-activated protein kinase (AMPK) is one of the central regulators of cellular and organismal metabolism in eukaryotes. Once activated by decreased energy levels, it induces ATP production by promoting catabolic pathways while conserving ATP by inhibiting anabolic pathways. AMPK plays a crucial role in various aspects of cellular function such as regulating growth, reprogramming metabolism, autophagy, and cell polarity. In this chapter, we focus on how recent breakthroughs made using the model organism Caenorhabditis elegans have contributed to our understanding of AMPK function and how it can be utilized in the future to elucidate hitherto unknown aspects of AMPK signaling.

  14. Mitogen Activated Protein kinase signal transduction pathways in the prostate

    Directory of Open Access Journals (Sweden)

    Koul Sweaty

    2004-06-01

    Full Text Available Abstract The biochemistry of the mitogen activated protein kinases ERK, JNK, and p38 have been studied in prostate physiology in an attempt to elucidate novel mechanisms and pathways for the treatment of prostatic disease. We reviewed articles examining mitogen-activated protein kinases using prostate tissue or cell lines. As with other tissue types, these signaling modules are links/transmitters for important pathways in prostate cells that can result in cellular survival or apoptosis. While the activation of the ERK pathway appears to primarily result in survival, the roles of JNK and p38 are less clear. Manipulation of these pathways could have important implications for the treatment of prostate cancer and benign prostatic hypertrophy.

  15. Computational Modeling of Complex Protein Activity Networks

    NARCIS (Netherlands)

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

    2017-01-01

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

  16. Heparan sulfate proteoglycans: structure, protein interactions and cell signaling

    Directory of Open Access Journals (Sweden)

    Juliana L. Dreyfuss

    2009-09-01

    Full Text Available Heparan sulfate proteoglycans are ubiquitously found at the cell surface and extracellular matrix in all the animal species. This review will focus on the structural characteristics of the heparan sulfate proteoglycans related to protein interactions leading to cell signaling. The heparan sulfate chains due to their vast structural diversity are able to bind and interact with a wide variety of proteins, such as growth factors, chemokines, morphogens, extracellular matrix components, enzymes, among others. There is a specificity directing the interactions of heparan sulfates and target proteins, regarding both the fine structure of the polysaccharide chain as well precise protein motifs. Heparan sulfates play a role in cellular signaling either as receptor or co-receptor for different ligands, and the activation of downstream pathways is related to phosphorylation of different cytosolic proteins either directly or involving cytoskeleton interactions leading to gene regulation. The role of the heparan sulfate proteoglycans in cellular signaling and endocytic uptake pathways is also discussed.Proteoglicanos de heparam sulfato são encontrados tanto superfície celular quanto na matriz extracelular em todas as espécies animais. Esta revisão tem enfoque nas características estruturais dos proteoglicanos de heparam sulfato e nas interações destes proteoglicanos com proteínas que levam à sinalização celular. As cadeias de heparam sulfato, devido a sua variedade estrutural, são capazes de se ligar e interagir com ampla gama de proteínas, como fatores de crescimento, quimiocinas, morfógenos, componentes da matriz extracelular, enzimas, entreoutros. Existe uma especificidade estrutural que direciona as interações dos heparam sulfatos e proteínas alvo. Esta especificidade está relacionada com a estrutura da cadeia do polissacarídeo e os motivos conservados da cadeia polipeptídica das proteínas envolvidas nesta interação. Os heparam

  17. Analysis of protein folds using protein contact networks

    Indian Academy of Sciences (India)

    - range non-local interactions among these secondary structural elements lead the protein to fold into a functionally active, native, three-dimensional, tertiary struc- ture. Often more than one tertiary subunits interact among themselves to form.

  18. Creative elements: network-based predictions of active centres in proteins, cellular and social networks

    CERN Document Server

    Csermely, Peter

    2008-01-01

    Active centres and hot spots of proteins have a paramount importance in enzyme action, protein complex formation and drug design. Recently a number of publications successfully applied the analysis of residue networks to predict active centres in proteins. Most real-world networks show a number of properties, such as small-worldness or scale-free degree distribution, which are rather general features of networks from molecules to the society. Based on extensive analogies I propose that the existing findings and methodology enable us to detect active centres in cells, social networks and ecosystems. Members of these active centres are creative elements of the respective networks, which may help them to survive unprecedented, novel challenges, and play a key role in the development, survival and evolvability of complex systems.

  19. p53 as the main traffic controller of the cell signaling network.

    Science.gov (United States)

    Sebastian, Sinto; Azzariti, Amalia; Silvestris, Nicola; Porcelli, Letizia; Russo, Antonio; Paradiso, Angelo

    2010-06-01

    Among different pathological conditions that affect human beings, cancer has received a great deal of attention primarily because it leads to significant morbidity and mortality. This is essentially due to increasing world-wide incidence of this disease and the inability to discover the cause and molecular mechanisms by which normal human cells acquire the characteristics that define cancer cells. Since the discovery of p53 over a quarter of a century ago, it is now recognized that virtually all cell fate pathways of live cells and the decision to die are under the control of p53. Such extensive involvement indicates that p53 protein is acting as a major traffic controller in the cell signaling network. In cancer cells, many cell signaling pathways of normal human cells are rerouted towards immortalization and this is accomplished by the corruption of the main controllers of cell signaling pathways such as p53. This review highlights how p53 signaling activity is altered in cancer cells so that cells acquire the hallmarks of cancer including deregulated infinite self replicative potential.

  20. Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections

    Directory of Open Access Journals (Sweden)

    Vahid Emamian

    2003-03-01

    Full Text Available Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA, and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

  1. Cherry Valley ducks mitochondrial antiviral-signaling protein (MAVS mediated signaling pathway and antiviral activity research

    Directory of Open Access Journals (Sweden)

    Ning Li

    2016-09-01

    Full Text Available Mitochondrial antiviral-signaling protein (MAVS, an adaptor protein of retinoic acid-inducible gene I (RIG-I like receptors (RLRs-mediated signal pathway, is involved in innate immunity. In this study, Cherry Valley duck MAVS (duMAVS was cloned from the spleen and analyzed. duMAVS was determined to have a caspase activation and recruitment domain at N-terminal, followed by a proline rich domain and a transmembrane domain at C-terminal. Quantitative real time PCR indicated that duMAVS was expressed in all tissues tested across a broad expression spectrum. The expression of duMAVS was significantly up-regulated after infection with duck Tembusu virus. Overexpression of duMAVS could drive the activation of interferon-β, nuclear factor-κB, interferon regulatory factor 7, and many downstream factors (such as Mx, PKR, OAS, and IL-8 in duck embryo fibroblast cells. What’s more, RNA interference further confirmed that duMAVS was an important adaptor for IFN-β activation. The antiviral assay showed that duMAVS could suppress the various viral replications (duck Tembusu virus, novel reovirus, and duck plague virus at early stages of infection. Overall, these results showed that the main signal pathway mediated by duMAVS and it had a broad-spectrum antiviral ability. This research will be helpful to better understanding the innate immune system of ducks.

  2. Influence of the complex-shape light signal on the neural network

    Science.gov (United States)

    Melnikov, Leonid A.; Novosselova, Anna V.; Blinova, Nadejda V.

    1999-03-01

    The effect of external signals of different shapes (constant, serrated and others) on the ring neural network modeling the visual perception is investigated numerically. New specific features in the dynamics of the neural network, such as the excitation, the swapping and the depression, were observed. The cooperative amplication of the external signal and the memory effect have been observed.

  3. Interaction of silver nanoparticles with proteins: a characteristic protein concentration dependent profile of SPR signal.

    Science.gov (United States)

    Banerjee, Victor; Das, K P

    2013-11-01

    Silver nanoparticles are finding increasing applications in biological systems, for example as antimicrobial agents and potential candidates for control drug release systems. In all such applications, silver nanoparticles interact with proteins and other biomolecules. Hence, the study of such interactions is of considerable importance. While BSA has been extensively used as a model protein for the study of interaction with the silver nanoparticles, studies using other proteins are rather limited. The interaction of silver nanoparticles with light leads to collective oscillation of the conducting electrons giving rise to surface plasmon resonance (SPR). Here, we have studied the protein concentration dependence of the SPR band profiles for a number of proteins. We found that for all the proteins, with increase in concentration, the SPR band intensity initially decreased, reaching minima and then increased again leading to a characteristic "dip and rise" pattern. Minimum point of the pattern appeared to be related to the isoelectric point of the proteins. Detailed dynamic light scattering and transmission electron microscopy studies revealed that the consistency of SPR profile was dependent on the average particle size and state of association of the silver nanoparticles with the change in the protein concentration. Fluorescence spectroscopic studies showed the binding constants of the proteins with the silver nanoparticles were in the nano molar range with more than one nanoparticle binding to protein molecule. Structural studies demonstrate that protein retains its native-like structure on the nanoparticle surface unless the molar ratio of silver nanoparticles to protein exceeds 10. Our study reveals that nature of the protein concentration dependent profile of SPR signal is a general phenomena and mostly independent of the size and structure of the proteins. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Distinctive Behaviors of Druggable Proteins in Cellular Networks.

    Directory of Open Access Journals (Sweden)

    Costas Mitsopoulos

    2015-12-01

    Full Text Available The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.

  5. Regulation of heterotrimeric G-protein signaling by NDPK/NME proteins and caveolins: an update.

    Science.gov (United States)

    Abu-Taha, Issam H; Heijman, Jordi; Feng, Yuxi; Vettel, Christiane; Dobrev, Dobromir; Wieland, Thomas

    2017-10-16

    Heterotrimeric G proteins are pivotal mediators of cellular signal transduction in eukaryotic cells and abnormal G-protein signaling plays an important role in numerous diseases. During the last two decades it has become evident that the activation status of heterotrimeric G proteins is both highly localized and strongly regulated by a number of factors, including a receptor-independent activation pathway of heterotrimeric G proteins that does not involve the classical GDP/GTP exchange and relies on nucleoside diphosphate kinases (NDPKs). NDPKs are NTP/NDP transphosphorylases encoded by the nme/nm23 genes that are involved in a variety of cellular events such as proliferation, migration, and apoptosis. They therefore contribute, for example, to tumor metastasis, angiogenesis, retinopathy, and heart failure. Interestingly, NDPKs are translocated and/or upregulated in human heart failure. Here we describe recent advances in the current understanding of NDPK functions and how they have an impact on local regulation of G-protein signaling.Laboratory Investigation advance online publication, 16 October 2017; doi:10.1038/labinvest.2017.103.

  6. Engineering spatial gradients of signaling proteins using magnetic nanoparticles.

    Science.gov (United States)

    Bonnemay, L; Hostachy, S; Hoffmann, C; Gautier, J; Gueroui, Z

    2013-11-13

    Intracellular biochemical reactions are often localized in space and time, inducing gradients of enzymatic activity that may play decisive roles in determining cell's fate and functions. However, the techniques available to examine such enzymatic gradients of activity remain limited. Here, we propose a new method to engineer a spatial gradient of signaling protein concentration within Xenopus egg extracts using superparamagnetic nanoparticles. We show that, upon the application of a magnetic field, a concentration gradient of nanoparticles with a tunable length extension is established within confined egg extracts. We then conjugate the nanoparticles to RanGTP, a small G-protein controlling microtubule assembly. We found that the generation of an artificial gradient of Ran-nanoparticles modifies the spatial positioning of microtubule assemblies. Furthermore, the spatial control of the level of Ran concentration allows us to correlate the local fold increase in Ran-nanoparticle concentration with the spatial positioning of the microtubule-asters. Our assay provides a bottom-up approach to examine the minimum ingredients generating polarization and symmetry breaking within cells. More generally, these results show how magnetic nanoparticles and magnetogenetic tools can be used to control the spatiotemporal dynamics of signaling pathways.

  7. Heterotrimeric G protein signaling governs the cortical stability during apical constriction in Drosophila gastrulation.

    Science.gov (United States)

    Kanesaki, Takuma; Hirose, Susumu; Grosshans, Joerg; Fuse, Naoyuki

    2013-02-01

    During gastrulation in Drosophila melanogaster, coordinated apical constriction of the cellular surface drives invagination of the mesoderm anlage. Forces generated by the cortical cytoskeletal network have a pivotal role in this cellular shape change. Here, we show that the organisation of cortical actin is essential for stabilisation of the cellular surface against contraction. We found that mutation of genes related to heterotrimeric G protein (HGP) signaling, such as Gβ13F, Gγ1, and ric-8, results in formation of blebs on the ventral cellular surface. The formation of blebs is caused by perturbation of cortical actin and induced by local surface contraction. HGP signaling mediated by two Gα subunits, Concertina and G-iα65A, constitutively regulates actin organisation. We propose that the organisation of cortical actin by HGP is required to reinforce the cortex so that the cells can endure hydrostatic stress during tissue folding. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  8. Proceedings of the IEEE 2003 Neural Networks for Signal Processing Workshop

    DEFF Research Database (Denmark)

    Larsen, Jan

    methodology and real-world application domains and is widely entering into everyday solutions adopted by research and industry, going far beyond “traditional” neural networks and academic examples. As reflected in this collection, contemporary neural networks for signal processing combine many ideas from......This proceeding contains refereed papers presented at the thirteenth IEEE Workshop on Neural Networks for Signal Processing (NNSP’2003), held at the Atria-Mercure Conference Center, Toulouse, France, September 17-19, 2003. The Neural Networks for Signal Processing Technical Committee of the IEEE...... Signal Processing Society organized the workshop with sponsorship of the Signal Processing Society and the co-operation of the IEEE Neural Networks Society. The IEEE Press published the previous twelve volumes of the NNSP Workshop proceedings in a hardbound volume. This year, the bound volume...

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

    Science.gov (United States)

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

    2010-07-01

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

  10. Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

    Science.gov (United States)

    Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G

    2012-01-01

    Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

  11. Non Linear Programming (NLP formulation for quantitative modeling of protein signal transduction pathways.

    Directory of Open Access Journals (Sweden)

    Alexander Mitsos

    Full Text Available Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i excessive CPU time requirements and ii loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

  12. Effect of placement of droop based generators in distribution network on small signal stability margin and network loss

    DEFF Research Database (Denmark)

    Dheer, D.K.; Doolla, S.; Bandyopadhyay, S.

    2017-01-01

    For a utility-connected system, issues related to small signal stability with Distributed Generators (DGs) are insignificant due to the presence of a very strong grid. Optimally placed sources in utility connected microgrid system may not be optimal/stable in islanded condition. Among others issues......, small signal stability margin is on the fore. The present research studied the effect of location of droop-controlled DGs on small signal stability margin and network loss on a modified IEEE 13 bus system, an IEEE 33-bus distribution system and a practical 22-bus radial distribution network. A complete...... dynamic model of an islanded microgrid was developed. From stability analysis, the study reports that both location of DGs and choice of droop coefficient have a significant effect on small signal stability, transient response of the system and network losses. The trade-off associated with the network...

  13. Specificity and evolvability in eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2007-02-01

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

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

    Science.gov (United States)

    Bywater, Robert P

    2017-05-01

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

  15. A tensegrity model for hydrogen bond networks in proteins

    Directory of Open Access Journals (Sweden)

    Robert P. Bywater

    2017-05-01

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

  16. INDEX: Incremental depth extension approach for protein-protein interaction networks alignment.

    Science.gov (United States)

    Mir, Abolfazl; Naghibzadeh, Mahmoud; Saadati, Nayyereh

    2017-12-01

    High-throughput methods have provided us with a large amount of data pertaining to protein-protein interaction networks. The alignment of these networks enables us to better understand biological systems. Given the fact that the alignment of networks is computationally intractable, it is important to introduce a more efficient and accurate algorithm which finds as large as possible similar areas among networks. This paper proposes a new algorithm named INDEX for the global alignment of protein-protein interaction networks. INDEX has multiple phases. First, it computes topological and biological scores of proteins and creates the initial alignment based on the proposed matching score strategy. Using networks topologies and aligned proteins, it then selects a set of high scoring proteins in each phase and extends new alignments around them until final alignment is obtained. Proposing a new alignment strategy, detailed consideration of matching scores, and growth of the alignment core has led INDEX to obtain a larger common connected subgraph with a much greater number of edges compared with previous methods. Regarding other measures such as edge correctness, symmetric substructure score, and runtime, the proposed algorithm performed considerably better than existing popular methods. Our results show that INDEX can be a promising method for identifying functionally conserved interactions. The INDEX executable file is available at https://github.com/a-mir/index/. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Customising the therapeutic response of signalling networks to promote antitumor responses by drug combinations

    Directory of Open Access Journals (Sweden)

    Alexey eGoltsov

    2014-02-01

    Full Text Available Drug resistance, de novo and acquired, pervades cellular signalling networks from one signalling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anticancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potency. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of drug combinations and design methods to determine advanced targets for drug combination therapy. Based on a joint systems analysis of cellular signalling network (SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyse the targets of drug combinations. The method explores mechanisms of sensitizing the SN through combination of two drugs targeting vertical signalling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to the customization of the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the downstream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects along with the

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

    Directory of Open Access Journals (Sweden)

    Sandip Chakraborty

    2016-01-01

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

  19. Dense fibrillar collagen is a potent inducer of invadopodia via a specific signaling network.

    Science.gov (United States)

    Artym, Vira V; Swatkoski, Stephen; Matsumoto, Kazue; Campbell, Catherine B; Petrie, Ryan J; Dimitriadis, Emilios K; Li, Xin; Mueller, Susette C; Bugge, Thomas H; Gucek, Marjan; Yamada, Kenneth M

    2015-02-02

    Cell interactions with the extracellular matrix (ECM) can regulate multiple cellular activities and the matrix itself in dynamic, bidirectional processes. One such process is local proteolytic modification of the ECM. Invadopodia of tumor cells are actin-rich proteolytic protrusions that locally degrade matrix molecules and mediate invasion. We report that a novel high-density fibrillar collagen (HDFC) matrix is a potent inducer of invadopodia, both in carcinoma cell lines and in primary human fibroblasts. In carcinoma cells, HDFC matrix induced formation of invadopodia via a specific integrin signaling pathway that did not require growth factors or even altered gene and protein expression. In contrast, phosphoproteomics identified major changes in a complex phosphosignaling network with kindlin2 serine phosphorylation as a key regulatory element. This kindlin2-dependent signal transduction network was required for efficient induction of invadopodia on dense fibrillar collagen and for local degradation of collagen. This novel phosphosignaling mechanism regulates cell surface invadopodia via kindlin2 for local proteolytic remodeling of the ECM.

  20. Dense fibrillar collagen is a potent inducer of invadopodia via a specific signaling network

    Science.gov (United States)

    Swatkoski, Stephen; Matsumoto, Kazue; Campbell, Catherine B.; Petrie, Ryan J.; Dimitriadis, Emilios K.; Li, Xin; Mueller, Susette C.; Bugge, Thomas H.; Gucek, Marjan

    2015-01-01

    Cell interactions with the extracellular matrix (ECM) can regulate multiple cellular activities and the matrix itself in dynamic, bidirectional processes. One such process is local proteolytic modification of the ECM. Invadopodia of tumor cells are actin-rich proteolytic protrusions that locally degrade matrix molecules and mediate invasion. We report that a novel high-density fibrillar collagen (HDFC) matrix is a potent inducer of invadopodia, both in carcinoma cell lines and in primary human fibroblasts. In carcinoma cells, HDFC matrix induced formation of invadopodia via a specific integrin signaling pathway that did not require growth factors or even altered gene and protein expression. In contrast, phosphoproteomics identified major changes in a complex phosphosignaling network with kindlin2 serine phosphorylation as a key regulatory element. This kindlin2-dependent signal transduction network was required for efficient induction of invadopodia on dense fibrillar collagen and for local degradation of collagen. This novel phosphosignaling mechanism regulates cell surface invadopodia via kindlin2 for local proteolytic remodeling of the ECM. PMID:25646088

  1. Minireview: Role of Intracellular Scaffolding Proteins in the Regulation of Endocrine G Protein-Coupled Receptor Signaling

    Science.gov (United States)

    Walther, Cornelia

    2015-01-01

    The majority of hormones stimulates and mediates their signal transduction via G protein-coupled receptors (GPCRs). The signal is transmitted into the cell due to the association of the GPCRs with heterotrimeric G proteins, which in turn activates an extensive array of signaling pathways to regulate cell physiology. However, GPCRs also function as scaffolds for the recruitment of a variety of cytoplasmic protein-interacting proteins that bind to both the intracellular face and protein interaction motifs encoded by GPCRs. The structural scaffolding of these proteins allows GPCRs to recruit large functional complexes that serve to modulate both G protein-dependent and -independent cellular signaling pathways and modulate GPCR intracellular trafficking. This review focuses on GPCR interacting PSD95-disc large-zona occludens domain containing scaffolds in the regulation of endocrine receptor signaling as well as their potential role as therapeutic targets for the treatment of endocrinopathies. PMID:25942107

  2. Protein function prediction by collective classification with explicit and implicit edges in protein-protein interaction networks.

    Science.gov (United States)

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

    2013-01-01

    Protein function prediction is an important problem in the post-genomic era. Recent advances in experimental biology have enabled the production of vast amounts of protein-protein interaction (PPI) data. Thus, using PPI data to functionally annotate proteins has been extensively studied. However, most existing network-based approaches do not work well when annotation and interaction information is inadequate in the networks. In this paper, we proposed a new method that combines PPI information and protein sequence information to boost the prediction performance based on collective classification. Our method divides function prediction into two phases: First, the original PPI network is enriched by adding a number of edges that are inferred from protein sequence information. We call the added edges implicit edges, and the existing ones explicit edges correspondingly. Second, a collective classification algorithm is employed on the new network to predict protein function. We conducted extensive experiments on two real, publicly available PPI datasets. Compared to four existing protein function prediction approaches, our method performs better in many situations, which shows that adding implicit edges can indeed improve the prediction performance. Furthermore, the experimental results also indicate that our method is significantly better than the compared approaches in sparsely-labeled networks, and it is robust to the change of the proportion of annotated proteins.

  3. Activated protein synthesis and suppressed protein breakdown signaling in skeletal muscle of critically ill patients

    DEFF Research Database (Denmark)

    Jespersen, Jakob G; Nedergaard, Anders; Reitelseder, Søren

    2011-01-01

    Skeletal muscle mass is controlled by myostatin and Akt-dependent signaling on mammalian target of rapamycin (mTOR), glycogen synthase kinase 3β (GSK3β) and forkhead box O (FoxO) pathways, but it is unknown how these pathways are regulated in critically ill human muscle. To describe factors...... involved in muscle mass regulation, we investigated the phosphorylation and expression of key factors in these protein synthesis and breakdown signaling pathways in thigh skeletal muscle of critically ill intensive care unit (ICU) patients compared with healthy controls....

  4. The photosensor protein Ppr of Rhodocista centenaria is linked to the chemotaxis signalling pathway

    Directory of Open Access Journals (Sweden)

    Kiefer Dorothee

    2010-11-01

    Full Text Available Abstract Background Rhodocista centenaria is a phototrophic α-proteobacterium exhibiting a phototactic behaviour visible as colony movement on agar plates directed to red light. As many phototrophic purple bacteria R. centenaria possesses a soluble photoactive yellow protein (Pyp. It exists as a long fusion protein, designated Ppr, consisting of three domains, the Pyp domain, a putative bilin binding domain (Bbd and a histidine kinase domain (Pph. The Ppr protein is involved in the regulation of polyketide synthesis but it is still unclear, how this is connected to phototaxis and chemotaxis. Results To elucidate the possible role of Ppr and Pph in the chemotactic network we studied the interaction with chemotactic proteins in vitro as well as in vivo. Matrix-assisted coelution experiments were performed to study the possible communication of the different putative binding partners. The kinase domain of the Ppr protein was found to interact with the chemotactic linker protein CheW. The formation of this complex was clearly ATP-dependent. Further results indicated that the Pph histidine kinase domain and CheW may form a complex with the chemotactic kinase CheAY suggesting a role of Ppr in the chemotaxis signalling pathway. In addition, when Ppr or Pph were expressed in Escherichia coli, the chemotactic response of the cells was dramatically affected. Conclusions The Ppr protein of Rhodocista centenaria directly interacts with the chemotactic protein CheW. This suggests a role of the Ppr protein in the regulation of the chemotactic response in addition to its role in chalcone synthesis.

  5. Associating genes and protein complexes with disease via network propagation.

    Directory of Open Access Journals (Sweden)

    Oron Vanunu

    2010-01-01

    Full Text Available A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.

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

    Science.gov (United States)

    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.

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

    KAUST Repository

    Chowdhary, Rajesh

    2012-04-06

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

  8. Experimental evolution of protein–protein interaction networks

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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. © 2016 Wiley Periodicals, Inc.

  10. Efficient Tor signaling requires a functional class C Vps protein complex in Saccharomyces cerevisiae.

    Science.gov (United States)

    Zurita-Martinez, Sara A; Puria, Rekha; Pan, Xuewen; Boeke, Jef D; Cardenas, Maria E

    2007-08-01

    The Tor kinases regulate responses to nutrients and control cell growth. Unlike most organisms that only contain one Tor protein, Saccharomyces cerevisiae expresses two, Tor1 and Tor2, which are thought to share all of the rapamycin-sensitive functions attributable to Tor signaling. Here we conducted a genetic screen that defined the global TOR1 synthetic fitness or lethal interaction gene network. This screen identified mutations in distinctive functional categories that impaired vacuolar function, including components of the EGO/Gse and PAS complexes that reduce fitness. In addition, tor1 is lethal in combination with mutations in class C Vps complex components. We find that Tor1 does not regulate the known function of the class C Vps complex in protein sorting. Instead class C vps mutants fail to recover from rapamycin-induced growth arrest or to survive nitrogen starvation and have low levels of amino acids. Remarkably, addition of glutamate or glutamine restores viability to a tor1 pep3 mutant strain. We conclude that Tor1 is more effective than Tor2 at providing rapamycin-sensitive Tor signaling under conditions of amino acid limitation, and that an intact class C Vps complex is required to mediate intracellular amino acid homeostasis for efficient Tor signaling.

  11. MATI, a Novel Protein Involved in the Regulation of Herbivore-Associated Signaling Pathways

    Directory of Open Access Journals (Sweden)

    M. Estrella Santamaría

    2017-06-01

    Full Text Available The defense response of the plants against herbivores relies on a complex network of interconnected signaling pathways. In this work, we characterized a new key player in the response of Arabidopsis against the two-spotted spider mite Tetranychus urticae, the MATI (Mite Attack Triggered Immunity gene. This gene was differentially induced in resistant Bla-2 strain relative to susceptible Kon Arabidopsis accessions after mite attack, suggesting a potential role in the control of spider mites. To study the MATI gene function, it has been performed a deep molecular characterization of the gene combined with feeding bioassays using modified Arabidopsis lines and phytophagous arthropods. The MATI gene belongs to a new gene family that had not been previously characterized. Biotic assays showed that it confers a high tolerance not only to T. urticae, but also to the chewing lepidopteran Spodoptera exigua. Biochemical analyses suggest that MATI encodes a protein involved in the accumulation of reducing agents upon herbivore attack to control plant redox homeostasis avoiding oxidative damage and cell death. Besides, molecular analyses demonstrated that MATI is involved in the modulation of different hormonal signaling pathways, affecting the expression of genes involved in biosynthesis and signaling of the jasmonic acid and salicylic acid hormones. The fact that MATI is also involved in defense through the modulation of the levels of photosynthetic pigments highlights the potential of MATI proteins to be exploited as biotechnological tools for pest control.

  12. Plasticity of the MAPK signaling network in response to mechanical stress.

    Directory of Open Access Journals (Sweden)

    Andrea M Pereira

    Full Text Available Cells display versatile responses to mechanical inputs and recent studies have identified the mitogen-activated protein kinase (MAPK cascades mediating the biological effects observed upon mechanical stimulation. Although, MAPK pathways can act insulated from each other, several mechanisms facilitate the crosstalk between the components of these cascades. Yet, the combinatorial complexity of potential molecular interactions between these elements have prevented the understanding of their concerted functions. To analyze the plasticity of the MAPK signaling network in response to mechanical stress we performed a non-saturating epistatic screen in resting and stretched conditions employing as readout a JNK responsive dJun-FRET biosensor. By knocking down MAPKs, and JNK pathway regulators, singly or in pairs in Drosophila S2R+ cells, we have uncovered unexpected regulatory links between JNK cascade kinases, Rho GTPases, MAPKs and the JNK phosphatase Puc. These relationships have been integrated in a system network model at equilibrium accounting for all experimentally validated interactions. This model allows predicting the global reaction of the network to its modulation in response to mechanical stress. It also highlights its context-dependent sensitivity.

  13. Reconstruction and Application of Protein–Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Tong Hao

    2016-06-01

    Full Text Available 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.

  14. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    Full Text Available We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of

  15. Proteins involved in platelet signaling are differentially regulated in acute coronary syndrome: a proteomic study.

    Directory of Open Access Journals (Sweden)

    Andrés Fernández Parguiña

    Full Text Available BACKGROUND: Platelets play a fundamental role in pathological events underlying acute coronary syndrome (ACS. Because platelets do not have a nucleus, proteomics constitutes an optimal approach to follow platelet molecular events associated with the onset of the acute episode. METHODOLOGY/PRINCIPAL FINDINGS: We performed the first high-resolution two-dimensional gel electrophoresis-based proteome analysis of circulating platelets from patients with non-ST segment elevation ACS (NSTE-ACS. Proteins were identified by mass spectrometry and validations were by western blotting. Forty protein features (corresponding to 22 unique genes were found to be differentially regulated between NSTE-ACS patients and matched controls with chronic ischemic cardiopathy. The number of differences decreased at day 5 (28 and 6 months after the acute event (5. Interestingly, a systems biology approach demonstrated that 16 of the 22 differentially regulated proteins identified are interconnected as part of a common network related to cell assembly and organization and cell morphology, processes very related to platelet activation. Indeed, 14 of those proteins are either signaling or cytoskeletal, and nine of them are known to participate in platelet activation by αIIbβ3 and/or GPVI receptors. Several of the proteins identified participate in platelet activation through post-translational modifications, as shown here for ILK, Src and Talin. Interestingly, the platelet-secreted glycoprotein SPARC was down-regulated in NSTE-ACS patients compared to stable controls, which is consistent with a secretion process from activated platelets. CONCLUSIONS/SIGNIFICANCE: The present study provides novel information on platelet proteome changes associated with platelet activation in NSTE-ACS, highlighting the presence of proteins involved in platelet signaling. This investigation paves the way for future studies in the search for novel platelet-related biomarkers and drug targets

  16. A unique microRNA profile in end-stage heart failure indicates alterations in specific cardiovascular signaling networks.

    Directory of Open Access Journals (Sweden)

    Sathyamangla V Naga Prasad

    Full Text Available It is well established that the gene expression patterns are substantially altered in cardiac hypertrophy and heart failure, however, less is known about the reasons behind such global differences. MicroRNAs (miRNAs are short non-coding RNAs that can target multiple molecules to regulate wide array of proteins in diverse pathways. The goal of the study was to profile alterations in miRNA expression using end-stage human heart failure samples with an aim to build signaling network pathways using predicted targets for the altered miRNA and to determine nodal molecules regulating individual networks. Profiling of miRNAs using custom designed microarray and validation with an independent set of samples identified eight miRNAs that are altered in human heart failure including one novel miRNA yet to be implicated in cardiac pathology. To gain an unbiased perspective on global regulation by top eight altered miRNAs, functional relationship of predicted targets for these eight miRNAs were examined by network analysis. Ingenuity Pathways Analysis network algorithm was used to build global signaling networks based on the targets of altered miRNAs which allowed us to identify participating networks and nodal molecules that could contribute to cardiac pathophysiology. Majority of the nodal molecules identified in our analysis are targets of altered miRNAs and known regulators of cardiovascular signaling. Cardio-genomics heart failure gene expression public data base was used to analyze trends in expression pattern for target nodal molecules and indeed changes in expression of nodal molecules inversely correlated to miRNA alterations. We have used NF kappa B network as an example to show that targeting other molecules in the network could alter the nodal NF kappa B despite not being a miRNA target suggesting an integrated network response. Thus, using network analysis we show that altering key functional target proteins may regulate expression of the myriad

  17. Cell growth and cell cycle in Saccharomyces cerevisiae: basic regulatory design and protein-protein interaction network.

    Science.gov (United States)

    Alberghina, Lilia; Mavelli, Gabriella; Drovandi, Guido; Palumbo, Pasquale; Pessina, Stefania; Tripodi, Farida; Coccetti, Paola; Vanoni, Marco

    2012-01-01

    In this review we summarize the major connections between cell growth and cell cycle in the model eukaryote Saccharomyces cerevisiae. In S. cerevisiae regulation of cell cycle progression is achieved predominantly during a narrow interval in the late G1 phase known as START (Pringle and Hartwell, 1981). At START a yeast cell integrates environmental and internal signals (such as nutrient availability, presence of pheromone, attainment of a critical size, status of the metabolic machinery) and decides whether to enter a new cell cycle or to undertake an alternative developmental program. Several signaling pathways, that act to connect the nutritional status to cellular actions, are briefly outlined. A Growth & Cycle interaction network has been manually curated. More than one fifth of the edges within the Growth & Cycle network connect Growth and Cycle proteins, indicating a strong interconnection between the processes of cell growth and cell cycle. The backbone of the Growth & Cycle network is composed of middle-degree nodes suggesting that it shares some properties with HOT networks. The development of multi-scale modeling and simulation analysis will help to elucidate relevant central features of growth and cycle as well as to identify their system-level properties. Confident collaborative efforts involving different expertises will allow to construct consensus, integrated models effectively linking the processes of cell growth and cell cycle, ultimately contributing to shed more light also on diseases in which an altered proliferation ability is observed, such as cancer. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data.

    Directory of Open Access Journals (Sweden)

    Jason W Locasale

    2009-08-01

    Full Text Available Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemical signaling network whose dynamics are governed by a large set of coupled differential equations. We found that in a simulated signaling system, the method detects interactions with significant accuracy. We then analyzed a growth factor mediated signaling network in a human mammary epithelial cell line that we inferred from mass spectrometry data and observe a biologically interpretable, small-world structure of signaling nodes, as well as a catalog of predictions regarding the interactions among previously uncharacterized phosphotyrosine sites. For example, the calculation places a recently identified tumor suppressor pathway through ARHGEF7 and Scribble, in the context of growth factor signaling. Our findings suggest that maximum entropy derived network models are an important tool for interpreting quantitative proteomics data.

  19. Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space.

    Science.gov (United States)

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

    2017-01-01

    Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf-combining a gene duplication/neofunctionalization model and scale-free model-has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.

  20. A review on models and algorithms for motif discovery in protein-protein interaction networks.

    Science.gov (United States)

    Ciriello, Giovanni; Guerra, Concettina

    2008-03-01

    Several algorithms have been recently designed to identify motifs in biological networks, particularly in protein-protein interaction networks. Motifs correspond to repeated modules in the network that may be of biological interest. The approaches proposed in the literature often differ in the definition of a motif, the way the occurrences of a motif are counted and the way their statistical significance is assessed. This has strong implications on the computational complexity of the discovery process and on the type of results that can be expected. This review presents in a systematic way the different computational settings outlining their main features and limitations.

  1. Review: Mitogen-Activated Protein kinases in nutritional signaling in Arabidopsis

    KAUST Repository

    Chardin, Camille

    2017-04-14

    Mitogen-Activated Protein Kinase (MAPK) cascades are functional modules widespread among eukaryotic organisms. In plants, these modules are encoded by large multigenic families and are involved in many biological processes ranging from stress responses to cellular differentiation and organ development. Furthermore, MAPK pathways are involved in the perception of environmental and physiological modifications. Interestingly, some MAPKs play a role in several signaling networks and could have an integrative function for the response of plants to their environment. In this review, we describe the classification of MAPKs and highlight some of their biochemical actions. We performed an in silico analysis of MAPK gene expression in response to nutrients supporting their involvement in nutritional signaling. While several MAPKs have been identified as players in sugar, nitrogen, phosphate, iron and potassium-related signaling pathways, their biochemical functions are yet mainly unknown. The integration of these regulatory cascades in the current understanding of nutrient signaling is discussed and potential new avenues for approaches toward plants with higher nutrient use efficiencies are evoked.

  2. Deciphering the protein-protein interaction network regulating hepatocellular carcinoma metastasis.

    Science.gov (United States)

    Qin, Guoxuan; Dang, Mengjiao; Gao, Huajun; Wang, Hao; Luo, Fengting; Chen, Ruibing

    2017-09-01

    Hepatocellular carcinoma (HCC) is one of the leading causes of mortality related to cancer all over the world. To better understand the molecular mechanisms of HCC metastasis, we analyzed the proteome of three HCC cell lines with different metastasis potentials by quantitative proteomics and bioinformatics analysis. As a result, we identified 378 cellular proteins potentially associated to HCC metastasis, and constructed a highly connected protein-protein interaction (PPI) network. Functional annotation of the network uncovered prominent pathways and key roles of these proteins, suggesting that the metabolism and cytoskeleton biological processes are greatly involved with HCC metastasis. Furthermore, the integrative network analysis revealed a rich-club organization within the PPI network, indicating a hub center of connections. The rich-club nodes include several well-known cancer-related proteins, such as proto-oncogene non-receptor tyrosine kinase (SRC) and pyruvate kinase M2 (PKM2). Moreover, the differential expressions of two identified proteins, including PKM2 and actin-related protein 2/3 complex subunit 4 (ARPC4), were validated using Western blotting. These two proteins were revealed as potential prognostic markers for HCC as shown by survival rate analysis. Copyright © 2017. Published by Elsevier B.V.

  3. Signal peptide hydrophobicity is critical for early stages in protein export by Bacillus subtilis

    NARCIS (Netherlands)

    van Zanen, L.F.; Houben, E.N.G.; Meima, R.B.; Tjalsma, H.; Jongbloed, J.D.H.; Westers, T.M.; Oudega, B.; Luirink, S.; Dijl, J.M.; Quax, W.J.

    2005-01-01

    Signal peptides that direct protein export in Bacillus subtilis are overall more hydrophobic than signal peptides in Escherichia coli. To study the importance of signal peptide hydrophobicity for protein export in both organisms, the α-amylase AmyQ was provided with leucine-rich (high

  4. Signal peptide hydrophobicity is critical for early stages in protein export by Bacillus subtilis.

    NARCIS (Netherlands)

    Zanen, G.; Houben, E.N.; Meima, R.; Tjalsma, H.; Jongbloed, J.D.; Westers, H.; Oudega, B.; Luirink, J.; Dijl, J.M. van; Quax, W.J.

    2005-01-01

    Signal peptides that direct protein export in Bacillus subtilis are overall more hydrophobic than signal peptides in Escherichia coli. To study the importance of signal peptide hydrophobicity for protein export in both organisms, the alpha-amylase AmyQ was provided with leucine-rich (high

  5. Signal peptide hydrophobicity is critical for early stages in protein export by Bacillus subtilis

    NARCIS (Netherlands)

    Zanen, G; Meima, R; Tjalsma, H; Jongbloed, JDH; Westers, H; Oudega, B; Luirink, J; van Dijl, JM; Quax, WJ; Houben, E.N.G.

    Signal peptides that direct protein export in Bacillus subtilis are overall more hydrophobic than signal peptides in Escherichia coli. To study the importance of signal peptide hydrophobicity for protein export in both organisms, the alpha-amylase AmyQ was provided with leucine-rich (high

  6. Protein kinase C signaling and cell cycle regulation

    Science.gov (United States)

    Black, Adrian R.; Black, Jennifer D.

    2013-01-01

    A link between T cell proliferation and the protein kinase C (PKC) family of serine/threonine kinases has been recognized for about 30 years. However, despite the wealth of information on PKC-mediated control of, T cell activation, understanding of the effects of PKCs on the cell cycle machinery in this cell type remains limited. Studies in other systems have revealed important cell cycle-specific effects of PKC signaling that can either positively or negatively impact proliferation. The outcome of PKC activation is highly context-dependent, with the precise cell cycle target(s) and overall effects determined by the specific isozyme involved, the timing of PKC activation, the cell type, and the signaling environment. Although PKCs can regulate all stages of the cell cycle, they appear to predominantly affect G0/G1 and G2. PKCs can modulate multiple cell cycle regulatory molecules, including cyclins, cyclin-dependent kinases (cdks), cdk inhibitors and cdc25 phosphatases; however, evidence points to Cip/Kip cdk inhibitors and D-type cyclins as key mediators of PKC-regulated cell cycle-specific effects. Several PKC isozymes can target Cip/Kip proteins to control G0/G1 → S and/or G2 → M transit, while effects on D-type cyclins regulate entry into and progression through G1. Analysis of PKC signaling in T cells has largely focused on its roles in T cell activation; thus, observed cell cycle effects are mainly positive. A prominent role is emerging for PKCθ, with non-redundant functions of other isozymes also described. Additional evidence points to PKCδ as a negative regulator of the cell cycle in these cells. As in other cell types, context-dependent effects of individual isozymes have been noted in T cells, and Cip/Kip cdk inhibitors and D-type cyclins appear to be major PKC targets. Future studies are anticipated to take advantage of the similarities between these various systems to enhance understanding of PKC-mediated cell cycle regulation in T cells. PMID

  7. Discovering protein complexes in protein interaction networks via exploring the weak ties effect.

    Science.gov (United States)

    Ma, Xiaoke; Gao, Lin

    2012-01-01

    Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting

  8. The topology of the bacterial co-conserved protein network and its implications for predicting protein function

    Directory of Open Access Journals (Sweden)

    Leach Sonia M

    2008-06-01

    Full Text Available Abstract Background Protein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins. Results Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the

  9. Digital Signal Processing and Control for the Study of Gene Networks.

    Science.gov (United States)

    Shin, Yong-Jun

    2016-04-22

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  10. Dual Coordination of Post Translational Modifications in Human Protein Networks

    Science.gov (United States)

    Woodsmith, Jonathan; Kamburov, Atanas; Stelzl, Ulrich

    2013-01-01

    Post-translational modifications (PTMs) regulate protein activity, stability and interaction profiles and are critical for cellular functioning. Further regulation is gained through PTM interplay whereby modifications modulate the occurrence of other PTMs or act in combination. Integration of global acetylation, ubiquitination and tyrosine or serine/threonine phosphorylation datasets with protein interaction data identified hundreds of protein complexes that selectively accumulate each PTM, indicating coordinated targeting of specific molecular functions. A second layer of PTM coordination exists in these complexes, mediated by PTM integration (PTMi) spots. PTMi spots represent very dense modification patterns in disordered protein regions and showed an equally high mutation rate as functional protein domains in cancer, inferring equivocal importance for cellular functioning. Systematic PTMi spot identification highlighted more than 300 candidate proteins for combinatorial PTM regulation. This study reveals two global PTM coordination mechanisms and emphasizes dataset integration as requisite in proteomic PTM studies to better predict modification impact on cellular signaling. PMID:23505349

  11. Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

    OpenAIRE

    Kale, S. N.; Dudul, S. V.

    2009-01-01

    Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN) can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors d...

  12. Quantitative Models of Imperfect Deception in Network Security using Signaling Games with Evidence

    OpenAIRE

    Pawlick, Jeffrey; Zhu, Quanyan

    2017-01-01

    Deception plays a critical role in many interactions in communication and network security. Game-theoretic models called "cheap talk signaling games" capture the dynamic and information asymmetric nature of deceptive interactions. But signaling games inherently model undetectable deception. In this paper, we investigate a model of signaling games in which the receiver can detect deception with some probability. This model nests traditional signaling games and complete information Stackelberg ...

  13. Analysis of core–periphery organization in protein contact networks ...

    Indian Academy of Sciences (India)

    2015-09-29

    Sep 29, 2015 ... Caenorhabditis elegans (Chatterjee and Sinha 2007) and the protein interaction network of Escherichia coli (Lin et al. 2009). Recently, this decomposition technique has been used to disentangle the hierarchical structure of Internet router-level connection topology (Zhang et al. 2009), to show that software ...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2012-01-01

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

  16. Mirin: identifying microRNA regulatory modules in protein-protein interaction networks.

    Science.gov (United States)

    Yang, Ken-Chi; Hsu, Chia-Lang; Lin, Chen-Ching; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-09-01

    Exploring microRNA (miRNA) regulations and protein-protein interactions could reveal the molecular mechanisms responsible for complex biological processes. Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers. The analysis involves combining miRNA regulations, protein-protein interactions between target genes, as well as mRNA and miRNA expression profiles provided by users. Mirin has successfully uncovered oncomirs and their regulatory networks in various cancers, such as gastric and breast cancer. Mirin is freely available at http://mirin.ym.edu.tw/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  17. Centralities in simplicial complexes. Applications to protein interaction networks.

    Science.gov (United States)

    Estrada, Ernesto; Ross, Grant J

    2018-02-07

    Complex networks can be used to represent complex systems which originate in the real world. Here we study a transformation of these complex networks into simplicial complexes, where cliques represent the simplices of the complex. We extend the concept of node centrality to that of simplicial centrality and study several mathematical properties of degree, closeness, betweenness, eigenvector, Katz, and subgraph centrality for simplicial complexes. We study the degree distributions of these centralities at the different levels. We also compare and describe the differences between the centralities at the different levels. Using these centralities we study a method for detecting essential proteins in PPI networks of cells and explain the varying abilities of the centrality measures at the different levels in identifying these essential proteins. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Mapping transcription factor interactome networks using HaloTag protein arrays.

    Science.gov (United States)

    Yazaki, Junshi; Galli, Mary; Kim, Alice Y; Nito, Kazumasa; Aleman, Fernando; Chang, Katherine N; Carvunis, Anne-Ruxandra; Quan, Rosa; Nguyen, Hien; Song, Liang; Alvarez, José M; Huang, Shao-Shan Carol; Chen, Huaming; Ramachandran, Niroshan; Altmann, Stefan; Gutiérrez, Rodrigo A; Hill, David E; Schroeder, Julian I; Chory, Joanne; LaBaer, Joshua; Vidal, Marc; Braun, Pascal; Ecker, Joseph R

    2016-07-19

    Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.

  19. G-protein-coupled receptors and tyrosine kinases: crossroads in cell signaling and regulation.

    Science.gov (United States)

    Gavi, Shai; Shumay, Elena; Wang, Hsien-yu; Malbon, Craig C

    2006-03-01

    G-protein-coupled receptors and protein tyrosine kinases represent two prominent pathways for cellular signaling. As our knowledge of cell signaling pathways mediated by the superfamily of G-protein-coupled receptors and the smaller family of receptor tyrosine kinases expands, so does our appreciation of how these two major signaling platforms share information and modulate each other, otherwise termed "cross-talk". Cross-talk between G-protein-coupled receptors and tyrosine kinases can occur at several levels, including the receptor-to-receptor level, and at crucial downstream points (e.g. phosphatidylinositol-3-kinase, Akt/protein kinase B and the mitogen-activated protein kinase cascade). Regulation of G-protein-coupled receptors by non-receptor tyrosine kinases, such as Src family members, also operates in signaling. A broader understanding of how G-protein-coupled receptors and tyrosine kinases cross-talk reveals new insights into signaling modalities in both health and disease.

  20. Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns.

    Science.gov (United States)

    Lee, Sangmook; Zemianek, Jill M; Shultz, Abraham; Vo, Anh; Maron, Ben Y; Therrien, Mikaela; Courtright, Christina; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B

    2014-11-01

    Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks. Copyright © 2014. Published by Elsevier Ltd.

  1. DMPD: The interferon signaling network and transcription factor C/EBP-beta. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 18163952 The interferon signaling network and transcription factor C/EBP-beta. Li H... The interferon signaling network and transcription factor C/EBP-beta. PubmedID 18163952 Title The interfero...n signaling network and transcription factor C/EBP-beta. Authors Li H, Gade P, Xi

  2. Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

    Directory of Open Access Journals (Sweden)

    Neto Armando

    2012-11-01

    Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug

  3. Identification of diverse archaeal proteins with class III signal peptides cleaved by distinct archaeal prepilin peptidases

    NARCIS (Netherlands)

    Szabó, Zalán; Oliveira Stahl, Adriana; Albers, Sonja-V.; Kissinger, Jessica C.; Driessen, Arnold J.M.; Pohlschröder, Mechthild; Pohlschroder, M.

    2007-01-01

    Most secreted archaeal proteins are targeted to the membrane via a tripartite signal composed of a charged N terminus and a hydrophobic domain, followed by a signal peptidase-processing site. Signal peptides of archaeal flagellins, similar to class III signal peptides of bacterial type IV pilins,

  4. AtPIN: Arabidopsis thaliana Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Silva-Filho Marcio C

    2009-12-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets. Description All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C3 which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS (AT5G26710 we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630, a disease resistance protein (AT3G50950 and a zinc finger protein (AT5G24930, which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening. Conclusions AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.

  5. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David

    2009-01-01

    in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may...

  6. Systematic discovery of new recognition peptides mediating protein interaction networks

    DEFF Research Database (Denmark)

    Neduva, Victor; Linding, Rune; Su-Angrand, Isabelle

    2005-01-01

    Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP). Many domains are kn...

  7. Mitogen activated protein kinase signaling in the kidney: Target for intervention?

    NARCIS (Netherlands)

    de Borst, M.H.; Wassef, L.; Kelly, D.J.; van Goor, H.; Navis, Ger Jan

    2006-01-01

    Mitogen activated protein kinases (MAPKs) are intracellular signal transduction molecules, which connect cell-surface receptor signals to intracellular processes. MAPKs regulate a range of cellular activities including cell proliferation, gene expression, apoptosis, cell differentiation and cytokine

  8. Prediction of Protein-Protein Interacting Sites: How to Bridge Molecular Events to Large Scale Protein Interaction Networks

    Science.gov (United States)

    Bartoli, Lisa; Martelli, Pier Luigi; Rossi, Ivan; Fariselli, Piero; Casadio, Rita

    Most of the cellular functions are the result of the concerted action of protein complexes forming pathways and networks. For this reason, efforts were devoted to the study of protein-protein interactions. Large-scale experiments on whole genomes allowed the identification of interacting protein pairs. However residues involved in the interaction are generally not known and the majority of the interactions still lack a structural characterization. A crucial step towards the deciphering of the interaction mechanism of proteins is the recognition of their interacting surfaces, particularly in those structures for which also the most recent interaction network resources do not contain information. To this purpose, we developed a neural network-based method that is able to characterize protein complexes, by predicting amino acid residues that mediate the interactions. All the Protein Data Bank (PDB) chains, both in the unbound and in the complexed form, are predicted and the results are stored in a database of interaction surfaces (http://gpcr.biocomp.unibo.it/zenpatches). Finally, we performed a survey on the different computational methods for protein-protein interaction prediction and on their training/testing sets in order to highlight the most informative properties of protein interfaces.

  9. Digital Signal Processing for a Sliceable Transceiver for Optical Access Networks

    DEFF Research Database (Denmark)

    Saldaña Cercos, Silvia; Wagner, Christoph; Vegas Olmos, Juan José

    2015-01-01

    also for implementing full signal path symmetry in real-time oscilloscopes to provide performance and signal fidelity (i.e. lower noise and jitter). In this paper the key digital signal processing (DSP) subsystems required to achieve signal slicing are surveyed. It also presents, for the first time......Methods to upgrade the network infrastructure to cope with current traffic demands has attracted increasing research efforts. A promising alternative is signal slicing. Signal slicing aims at re-using low bandwidth equipment to satisfy high bandwidth traffic demands. This technique has been used...... penalty is reported for 10 Gbps. Power savings of the order of hundreds of Watts can be obtained when using signal slicing as an alternative to 10 Gbps implemented access networks....

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

    Science.gov (United States)

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

    2017-05-15

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

  11. Convergent Evolution of Pathogen Effectors toward Reactive Oxygen Species Signaling Networks in Plants

    Directory of Open Access Journals (Sweden)

    Nam-Soo Jwa

    2017-09-01

    Full Text Available Microbial pathogens have evolved protein effectors to promote virulence and cause disease in host plants. Pathogen effectors delivered into plant cells suppress plant immune responses and modulate host metabolism to support the infection processes of pathogens. Reactive oxygen species (ROS act as cellular signaling molecules to trigger plant immune responses, such as pathogen-associated molecular pattern (PAMP-triggered immunity (PTI and effector-triggered immunity. In this review, we discuss recent insights into the molecular functions of pathogen effectors that target multiple steps in the ROS signaling pathway in plants. The perception of PAMPs by pattern recognition receptors leads to the rapid and strong production of ROS through activation of NADPH oxidase Respiratory Burst Oxidase Homologs (RBOHs as well as peroxidases. Specific pathogen effectors directly or indirectly interact with plant nucleotide-binding leucine-rich repeat receptors to induce ROS production and the hypersensitive response in plant cells. By contrast, virulent pathogens possess effectors capable of suppressing plant ROS bursts in different ways during infection. PAMP-triggered ROS bursts are suppressed by pathogen effectors that target mitogen-activated protein kinase cascades. Moreover, pathogen effectors target vesicle trafficking or metabolic priming, leading to the suppression of ROS production. Secreted pathogen effectors block the metabolic coenzyme NADP-malic enzyme, inhibiting the transfer of electrons to the NADPH oxidases (RBOHs responsible for ROS generation. Collectively, pathogen effectors may have evolved to converge on a common host protein network to suppress the common plant immune system, including the ROS burst and cell death response in plants.

  12. Convergent Evolution of Pathogen Effectors toward Reactive Oxygen Species Signaling Networks in Plants.

    Science.gov (United States)

    Jwa, Nam-Soo; Hwang, Byung Kook

    2017-01-01

    Microbial pathogens have evolved protein effectors to promote virulence and cause disease in host plants. Pathogen effectors delivered into plant cells suppress plant immune responses and modulate host metabolism to support the infection processes of pathogens. Reactive oxygen species (ROS) act as cellular signaling molecules to trigger plant immune responses, such as pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity. In this review, we discuss recent insights into the molecular functions of pathogen effectors that target multiple steps in the ROS signaling pathway in plants. The perception of PAMPs by pattern recognition receptors leads to the rapid and strong production of ROS through activation of NADPH oxidase Respiratory Burst Oxidase Homologs (RBOHs) as well as peroxidases. Specific pathogen effectors directly or indirectly interact with plant nucleotide-binding leucine-rich repeat receptors to induce ROS production and the hypersensitive response in plant cells. By contrast, virulent pathogens possess effectors capable of suppressing plant ROS bursts in different ways during infection. PAMP-triggered ROS bursts are suppressed by pathogen effectors that target mitogen-activated protein kinase cascades. Moreover, pathogen effectors target vesicle trafficking or metabolic priming, leading to the suppression of ROS production. Secreted pathogen effectors block the metabolic coenzyme NADP-malic enzyme, inhibiting the transfer of electrons to the NADPH oxidases (RBOHs) responsible for ROS generation. Collectively, pathogen effectors may have evolved to converge on a common host protein network to suppress the common plant immune system, including the ROS burst and cell death response in plants.

  13. Model-based design of self-Adapting networked signal processing systems

    NARCIS (Netherlands)

    Oliveira Filho, J.A. de; Papp, Z.; Djapic, R.; Oostveen, J.C.

    2013-01-01

    The paper describes a model based approach for architecture design of runtime reconfigurable, large-scale, networked signal processing applications. A graph based modeling formalism is introduced to describe all relevant aspects of the design (functional, concurrency, hardware, communication,

  14. Jasmonate Signalling Network in Arabidopsis thaliana: Crucial Regulatory Nodes and New Physiological Scenarios

    National Research Council Canada - National Science Library

    Virginia Balbi; Alessandra Devoto

    2008-01-01

    .... In this review, we focus on the latest published work on jasmonate (JA) signalling components and new regulatory nodes in the transcriptional network that regulates a number of diverse plant responses to developmental and environmental cues...

  15. Research on the Wire Network Signal Prediction Based on the Improved NNARX Model

    Science.gov (United States)

    Zhang, Zipeng; Fan, Tao; Wang, Shuqing

    It is difficult to obtain accurately the wire net signal of power system's high voltage power transmission lines in the process of monitoring and repairing. In order to solve this problem, the signal measured in remote substation or laboratory is employed to make multipoint prediction to gain the needed data. But, the obtained power grid frequency signal is delay. In order to solve the problem, an improved NNARX network which can predict frequency signal based on multi-point data collected by remote substation PMU is describes in this paper. As the error curved surface of the NNARX network is more complicated, this paper uses L-M algorithm to train the network. The result of the simulation shows that the NNARX network has preferable predication performance which provides accurate real time data for field testing and maintenance.

  16. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  17. Simulation of mixed switched-capacitor/digital networks with signal-driven switches

    Science.gov (United States)

    Suyama, Ken; Tsividis, Yannis P.; Fang, San-Chin

    1990-12-01

    The simulation of mixed switched-capacitor/digital (SC/D) networks containing capacitors, independent and linear-dependent voltage sources, switches controlled either by periodic or nonperiodic Boolean signals, latched comparators, and logic gates is considered. A unified linear switched-capacitor network (SCN) and mixed SC/D network simulator, SWITCAP2, and its applications to several widely used and novel nonlinear SCNs are discussed. The switches may be controlled by periodic waveforms and by nonperiodic waveforms from the outputs of comparators and logic gates. The signal-dependent modification of network topology through the comparators, logic gates, and signal-driven switches makes the modeling of various nonlinear switched-capacitor circuits possible. Simulation results for a pulse-code modulation (PCM) voice encoder, a sigma-delta modulator, a neural network, and a phase-locked loop (PLL) are presented to demonstrate the flexibility of the approach.

  18. Complex networks of self-incompatibility signaling in the Brassicaceae.

    Science.gov (United States)

    Tantikanjana, Titima; Nasrallah, Mikhail E; Nasrallah, June B

    2010-10-01

    The self-pollination barrier of self-incompatibility in the Brassicaceae is based on the activity of a polymorphic stigma receptor and its pollen ligand, whose allele-specific interaction triggers a signaling cascade within the stigma epidermal cell that culminates in the inhibition of pollen tube development. Recent analyses have identified signaling intermediates and revealed unexpected cross-talk between self-incompatibility signaling and pistil development. The self-incompatibility response is now thought to be based on a phosphorylation and ubiquitin-mediated degradation pathway that inhibits the secretion of factors required for successful pollination. Because manipulation of the identified signaling intermediates results in only partial disruption of the self-incompatibility reaction, this pathway likely functions in conjunction with other as-yet unidentified signaling pathways to effect complete inhibition of self-pollen. Copyright © 2010 Elsevier Ltd. All rights reserved.

  19. Experimental Demonstration of Mixed Formats and Bit Rates Signal Allocation for Spectrum-flexible Optical Networking

    DEFF Research Database (Denmark)

    Borkowski, Robert; Karinou, Fotini; Angelou, Marianna

    2012-01-01

    We report on an extensive experimental study for adaptive allocation of 16-QAM and QPSK signals inside spectrum flexible heterogeneous superchannel. Physical-layer performance parameters are extracted for use in resource allocation mechanisms of future flexible networks.......We report on an extensive experimental study for adaptive allocation of 16-QAM and QPSK signals inside spectrum flexible heterogeneous superchannel. Physical-layer performance parameters are extracted for use in resource allocation mechanisms of future flexible networks....

  20. ErbB2-Driven Breast Cancer Cell Invasion Depends on a Complex Signaling Network Activating Myeloid Zinc Finger-1-Dependent Cathepsin B Expression

    DEFF Research Database (Denmark)

    Rafn, Bo; Nielsen, Christian Thomas Friberg; Andersen, Sofie Hagel

    2012-01-01

    signaling network activates the transcription of cathepsin B gene (CTSB) via myeloid zinc finger-1 transcription factor that binds to an ErbB2-responsive enhancer element in the first intron of CTSB. This work provides a model system for ErbB2-induced breast cancer cell invasiveness, reveals a signaling...... as effectors of ErbB2-induced invasion in vitro. We identify Cdc42-binding protein kinase beta, extracellular regulated kinase 2, p21-activated protein kinase 4, and protein kinase C alpha as essential mediators of ErbB2-induced cysteine cathepsin expression and breast cancer cell invasiveness. The identified...

  1. Validation of protein structure models using network similarity score.

    Science.gov (United States)

    Ghosh, Sambit; Gadiyaram, Vasundhara; Vishveshwara, Saraswathi

    2017-09-01

    Accurate structural validation of proteins is of extreme importance in studies like protein structure prediction, analysis of molecular dynamic simulation trajectories and finding subtle changes in very similar structures. The benchmarks for today's structure validation are scoring methods like global distance test-total structure (GDT-TS), TM-score and root mean square deviations (RMSD). However, there is a lack of methods that look at both the protein backbone and side-chain structures at the global connectivity level and provide information about the differences in connectivity. To address this gap, a graph spectral based method (NSS-network similarity score) which has been recently developed to rigorously compare networks in diverse fields, is adopted to compare protein structures both at the backbone and at the side-chain noncovalent connectivity levels. In this study, we validate the performance of NSS by investigating protein structures from X-ray structures, modeling (including CASP models), and molecular dynamics simulations. Further, we systematically identify the local and the global regions of the structures contributing to the difference in NSS, through the components of the score, a feature unique to this spectral based scoring scheme. It is demonstrated that the method can quantify subtle differences in connectivity compared to a reference protein structure and can form a robust basis for protein structure comparison. Additionally, we have also introduced a network-based method to analyze fluctuations in side chain interactions (edge-weights) in an ensemble of structures, which can be an useful tool for the analysis of MD trajectories. © 2017 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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. Copyright © 2011 Wiley-Liss, Inc.

  3. Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery.

    Science.gov (United States)

    Fadhal, Emad; Mwambene, Eric C; Gamieldien, Junaid

    2014-06-14

    We have recently shown by formally modelling human protein interaction networks (PINs) as metric spaces and classified proteins into zones based on their distance from the topological centre that hub proteins are primarily centrally located. We also showed that zones closest to the network centre are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping' functions. We proposed that proteins closest to the network centre may present good therapeutic targets. Here, we present multiple pieces of novel functional evidence that provides strong support for this hypothesis. We found that the human PINs has a highly connected signalling core, with the majority of proteins involved in signalling located in the two zones closest to the topological centre. The majority of essential, disease related, tumour suppressor, oncogenic and approved drug target proteins were found to be centrally located. Similarly, the majority of proteins consistently expressed in 13 types of cancer are also predominantly located in zones closest to the centre. Proteins from zones 1 and 2 were also found to comprise the majority of proteins in key KEGG pathways such as MAPK-signalling, the cell cycle, apoptosis and also pathways in cancer, with very similar patterns seen in pathways that lead to cancers such as melanoma and glioma, and non-neoplastic diseases such as measles, inflammatory bowel disease and Alzheimer's disease. Based on the diversity of evidence uncovered, we propose that when considered holistically, proteins located centrally in the human PINs that also have similar functions to existing drug targets are good candidate targets for novel therapeutics. Similarly, since disease pathways are dominated by centrally located proteins, candidates shortlisted in genome scale disease studies can be further prioritized and contextualised based on whether they occupy central positions in the human PINs.

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

    Directory of Open Access Journals (Sweden)

    Wan Li

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

  5. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network

    Directory of Open Access Journals (Sweden)

    Benjamin eKeith

    2014-12-01

    Full Text Available Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorised according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest novel genes that may also contribute to diseases with locus heterogeneity.

  6. Protein tyrosine kinase and mitogen-activated protein kinase signalling pathways contribute to differences in heterophil-mediated innate immune responsiveness between two lines of broilers

    Science.gov (United States)

    Protein tyrosine phosphorylation mediates signal transduction of cellular processes, with protein tyrosine kinases (PTKs) regulating virtually all signaling events. The mitogen-activated protein kinase (MAPK) super-family consists of three conserved pathways that convert receptor activation into ce...

  7. Differential Protein Network Analysis of the Immune Cell Lineage

    Directory of Open Access Journals (Sweden)

    Trevor Clancy

    2014-01-01

    Full Text Available Recently, the Immunological Genome Project (ImmGen completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.

  8. Probing the Extent of Randomness in Protein Interaction Networks

    Science.gov (United States)

    2008-07-11

    scale-free networks are born equal: the role of the seed graph in PPI network evolution. PLoS Comput Biol 3: e118. doi:10.1371/journal.pcbi.0030118. 57...from seeds [56]. In the degree-conserving degree-weighted (DCDW) model, each node is considered once, in a random order, and a set number of edges are...gene duplication in fungi . Nature 449: 54–61. 63. Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW (2002) Evolutionary rate in the protein

  9. Protein sorting by lipid phase-like domains supports emergent signaling function in B lymphocyte plasma membranes.

    Science.gov (United States)

    Stone, Matthew B; Shelby, Sarah A; Núñez, Marcos F; Wisser, Kathleen; Veatch, Sarah L

    2017-02-01

    Diverse cellular signaling events, including B cell receptor (BCR) activation, are hypothesized to be facilitated by domains enriched in specific plasma membrane lipids and proteins that resemble liquid-ordered phase-separated domains in model membranes. This concept remains controversial and lacks direct experimental support in intact cells. Here, we visualize ordered and disordered domains in mouse B lymphoma cell membranes using super-resolution fluorescence localization microscopy, demonstrate that clustered BCR resides within ordered phase-like domains capable of sorting key regulators of BCR activation, and present a minimal, predictive model where clustering receptors leads to their collective activation by stabilizing an extended ordered domain. These results provide evidence for the role of membrane domains in BCR signaling and a plausible mechanism of BCR activation via receptor clustering that could be generalized to other signaling pathways. Overall, these studies demonstrate that lipid mediated forces can bias biochemical networks in ways that broadly impact signal transduction.

  10. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  11. Fitting a geometric graph to a protein-protein interaction network.

    Science.gov (United States)

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

    2008-04-15

    Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs. We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N(2)) where N is the number of proteins. The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a

  12. PIN-G – A novel reporter for imaging and defining the effects of trafficking signals in membrane proteins

    Directory of Open Access Journals (Sweden)

    Hu Weiwen

    2006-03-01

    Full Text Available Abstract Background The identification of protein trafficking signals, and their interacting mechanisms, is a fundamental objective of modern biology. Unfortunately, the analysis of trafficking signals is complicated by their topography, hierarchical nature and regulation. Powerful strategies to test candidate motifs include their ability to direct simpler reporter proteins, to which they are fused, to the appropriate cellular compartment. However, present reporters are limited by their endogenous expression, paucity of cloning sites, and difficult detection in live cells. Results Consequently, we have engineered a mammalian expression vector encoding a novel trafficking reporter – pIN-G – consisting of a simple, type I integral protein bearing permissive intra/extracellular cloning sites, green fluorescent protein (GFP, cMyc and HA epitope tags. Fluorescence imaging, flow cytometry and biochemical assays of transfected HEK293 cells, confirm the size, topology and surface expression of PIN-G. Moreover, a pIN-G fusion construct, containing a Trans-Golgi Network (TGN targeting determinant, internalises rapidly from the cell surface and localises to the TGN. Additionally, another PIN-G fusion protein and its mutants reveal trafficking determinants in the cytoplasmic carboxy terminus of Kv1.4 voltage-gated potassium channels. Conclusion Together, these data indicate that pIN-G is a versatile, powerful, new reporter for analysing signals controlling membrane protein trafficking, surface expression and dynamics.

  13. lpNet: a linear programming approach to reconstruct signal transduction networks.

    Science.gov (United States)

    Matos, Marta R A; Knapp, Bettina; Kaderali, Lars

    2015-10-01

    With the widespread availability of high-throughput experimental technologies it has become possible to study hundreds to thousands of cellular factors simultaneously, such as coding- or non-coding mRNA or protein concentrations. Still, extracting information about the underlying regulatory or signaling interactions from these data remains a difficult challenge. We present a flexible approach towards network inference based on linear programming. Our method reconstructs the interactions of factors from a combination of perturbation/non-perturbation and steady-state/time-series data. We show both on simulated and real data that our methods are able to reconstruct the underlying networks fast and efficiently, thus shedding new light on biological processes and, in particular, into disease's mechanisms of action. We have implemented the approach as an R package available through bioconductor. This R package is freely available under the Gnu Public License (GPL-3) from bioconductor.org (http://bioconductor.org/packages/release/bioc/html/lpNet.html) and is compatible with most operating systems (Windows, Linux, Mac OS) and hardware architectures. bettina.knapp@helmholtz-muenchen.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan

    1999-01-01

    This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...

  15. Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

    DEFF Research Database (Denmark)

    Postnov, D.E.; Koreshkov, R.N.; Brazhe, N.A.

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission...... in astrocytic-neuronal networks. We reproduce local and global dynamical patterns observed experimentally....

  16. Hub nodes in the network of human Mitogen-Activated Protein Kinase (MAPK pathways: Characteristics and potential as drug targets

    Directory of Open Access Journals (Sweden)

    V.K. MD Aksam

    2017-01-01

    Full Text Available Proteins involved in the cross-talk between ERK1/2, ERK5, JNK, and P38 signalling pathways integrate the network of Mitogen-Activated Protein Kinase (MAPK pathways. Graph theory-based approach is used to construct the network of MAPK pathways, and to observe the network organisational principles. Connectivity pattern reveals rich-club among the hubs, enabling structural ordering. A positive correlation between the degree of the nodes and percentage of essential protein showed hubs are central to the network architecture and function. Furthermore, attributes like connectivity, inter/intra-pathway class, position in the pathway, protein type and subcellular localization of the essential and non-essential proteins are characterizing complex functional roles. Shared properties of 34 cancerous essential proteins lack to be drug targets. We identified the seven nodes overlapping properties of the hub, essential and causing side effects on targeting them. We exploit the strategy of cancerous, non-hub and non-essential proteins as potential drug targets and identified 4EBP1, BAD, CHOP10, GADD45, HSP27, MKP1, RNPK, MLTKa/b, cPLA2, eEF2K and elF4E. We have illustrated the implication of targeting hub nodes and proposed network-based drug targets which would cause less side effect.

  17. Array signal processing in the NASA Deep Space Network

    Science.gov (United States)

    Pham, Timothy T.; Jongeling, Andre P.

    2004-01-01

    In this paper, we will describe the benefits of arraying and past as well as expected future use of this application. The signal processing aspects of array system are described. Field measurements via actual tracking spacecraft are also presented.

  18. Stability of multispecies bacterial communities: signaling networks may stabilize microbiomes.

    Directory of Open Access Journals (Sweden)

    Ádám Kerényi

    Full Text Available Multispecies bacterial communities can be remarkably stable and resilient even though they consist of cells and species that compete for environmental resources. In silico models suggest that common signals released into the environment may help selected bacterial species cluster at common locations and that sharing of public goods (i.e. molecules produced and released for mutual benefit can stabilize this coexistence. In contrast, unilateral eavesdropping on signals produced by a potentially invading species may protect a community by keeping invaders away from limited resources. Shared bacterial signals, such as those found in quorum sensing systems, may thus play a key role in fine tuning competition and cooperation within multi-bacterial communities. We suggest that in addition to metabolic complementarity, signaling dynamics may be important in further understanding complex bacterial communities such as the human, animal as well as plant microbiomes.

  19. Signaling Network of Environmental Sensing and Adaptation in Plants:. Key Roles of Calcium Ion

    Science.gov (United States)

    Kurusu, Takamitsu; Kuchitsu, Kazuyuki

    2011-01-01

    Considering the important issues concerning food, environment, and energy that humans are facing in the 21st century, humans mostly depend on plants. Unlike animals which move from an inappropriate environment, plants do not move, but rapidly sense diverse environmental changes or invasion by other organisms such as pathogens and insects in the place they root, and adapt themselves by changing their own bodies, through which they developed adaptability. Whole genetic information corresponding to the blueprints of many biological systems has recently been analyzed, and comparative genomic studies facilitated tracing strategies of each organism in their evolutional processes. Comparison of factors involved in intracellular signal transduction between animals and plants indicated diversification of different gene sets. Reversible binding of Ca2+ to sensor proteins play key roles as a molecular switch both in animals and plants. Molecular mechanisms for signaling network of environmental sensing and adaptation in plants will be discussed with special reference to Ca2+ as a key element in information processing.

  20. Network dynamics determine the autocrine and paracrine signaling functions of TNF

    Science.gov (United States)

    Caldwell, Andrew B.; Cheng, Zhang; Vargas, Jesse D.; Birnbaum, Harry A.

    2014-01-01

    A hallmark of the inflammatory response to pathogen exposure is the production of tumor necrosis factor (TNF) that coordinates innate and adaptive immune responses by functioning in an autocrine or paracrine manner. Numerous molecular mechanisms contributing to TNF production have been identified, but how they function together in macrophages remains unclear. Here, we pursued an iterative systems biology approach to develop a quantitative understanding of the regulatory modules that control TNF mRNA synthesis and processing, mRNA half-life and translation, and protein processing and secretion. By linking the resulting model of TNF production to models of the TLR-, the TNFR-, and the NFκB signaling modules, we were able to study TNF’s functions during the inflammatory response to diverse TLR agonists. Contrary to expectation, we predicted and then experimentally con