Bow-tie Structure Research Articles

The Network of Global Corporate Control

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers.

Steering the optical response with asymmetric bowtie 2-color controllers in the visible and near infrared range

We have proposed an asymmetric bowtie 2-color controller and analyzed its resonance frequency spectra and temporal responses. The results show improved optical properties of the asymmetric bowtie 2-color controller as compared to symmetric bowties. The improved optical properties are a broad bandwidth of the plasmonic spectrum consisting of two resonant peaks, a high field enhancement in the gap of the bowtie structure, and a large effective enhancement volume. The system might have applications in the generation of XUV light via high-harmonic generation as well as in ultrabroadband sensors and multicolor optoelectronic filters.

Optimizing bowtie structure parameters for specific incident light

We investigate optical properties of a bowtie-shaped aperture using the finite difference time domain method to optimize its geometric parameters for specific incident lights. The influence of the parameters on local field enhancement and resonant wavelength in the visible frequency range is numerically analysed. It is found that the major resonance of the spectrum is exponentially depended on the bowtie angle but independent of the whole aperture size. The simulation also demonstrates that increasing the aperture size raises the local field intensity on the exit plane due to an enlarged interaction area between the light and the metal medium. And the near-field spot size is closely related to the gap. Based on these results, the design rules of the bowtie structure can be optimized for specific wavelengths excited.

Dynamic cross-talk analysis among TNF-R, TLR-4 and IL-1R signalings in TNFα-induced inflammatory responses

BackgroundDevelopment in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways.MethodsIn this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFα-induced inflammation. Next, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R).ResultsThe highly ranked cross-talks which are functionally relevant to the TNFα pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed.ConclusionsA systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.

Integrated cellular network of transcription regulations and protein-protein interactions

BackgroundWith the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway.ResultsIn this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated.ConclusionsWe successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

Reconstruction and Analysis of Human Liver-Specific Metabolic Network Based on CNHLPP Data

Liver is the largest internal organ in the body that takes central roles in metabolic homeostasis, detoxification of various substances, as well as in the synthesis and storage of nutrients. To fulfill these complex tasks, thousands of biochemical reactions are going on in liver to cope with a wide range of foods and environmental variations, which are densely interconnected into an intricate metabolic network. Here, the first human liver-specific metabolic network was reconstructed according to proteomics data from Chinese Human Liver Proteome Project (CNHLPP), and then investigated in the context of the genome-scale metabolic network of Homo sapiens. Topological analysis shows that this organ-specific metabolic network exhibits similar features as organism-specific networks, such as power-law degree distribution, small-world property, and bow-tie structure. Furthermore, the structure of liver network exhibits a modular organization where the modules are formed around precursors from primary metabolism or hub metabolites from derivative metabolism, respectively. Most of the modules are dominated by one major category of metabolisms, while enzymes within same modules have a tendency of being expressed concertedly at protein level. Network decomposition and comparison suggest that the liver network overlays a predominant area in the global metabolic network of H. sapiens genome; meanwhile the human network may develop extra modules to gain more specialized functionality out of liver. The results of this study would permit a high-level interpretation of the metabolite information flow in human liver and provide a basis for modeling the physiological and pathological metabolic states of liver.

The Origin of the Auxetic Effect in Prismanes: Bowtie Structure and the Mechanical Properties of Biprismanes.

Prismanes have been shown to exhibit negative Poisson’s ratio (auxetic behavior) for their internal rings and normal Poisson’s behavior for the terminal rings. Various [3], [4], [5], and [6] biprismanes were studied by using quantum mechanical ab initio calculations. The unusual mechanical behavior of prismanes upon stretching was traced to the relaxation of geometrical distortion existing in the ground state. The increase of the Poissonic effect on the ring size correlates well with the Thorpe−Ingold effect. In biprismanes where the CH units in the external rings are replaced by nitrogen, the effect is accentuated. Substitution of the hydrogens by fluorine atoms markedly reduces the effect, this being an interesting example of substituent effect on mechanical properties. This effect is attenuated in longer prismanes.

Insights into the Organization of Biochemical Regulatory Networks Using Graph Theory Analyses

Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.

Numerical Study of Optical Properties of Ag/SiO<SUB>2</SUB>/Ag Sandwich Nanobowties

A composite nanobowtie with Ag/SiO2/Ag sandwich structure is presented and its optical properties especially the Plasmon property are studied using the discrete dipole approximation (DDA) method in this paper. Our results show that this structure has simply-tunable surface Plasmon resonance (SPR) wavelengths and more hotter spots provided by the sandwich structure, as well as the remarkable enhancement of the localized E-field provided by the bowtie structure. The transverse and longitudinal Plasmon coupling mechanism is employed to explain tunable SPR and strong E-field enhancement respectively. The sandwich nanobowtie possesses the qualities of simply-tunable SPR and great E-field enhancement, thereby holding a great potential in applications such as surface-enhanced Raman scattering (SERS).

On the basic computational structure of gene regulatory networks

Gene regulatory networks constitute the first layer of the cellular computation for cell adaptation and surveillance. In these webs, a set of causal relations is built up from thousands of interactions between transcription factors and their target genes. The large size of these webs and their entangled nature make it difficult to achieve a global view of their internal organisation. Here, this problem has been addressed through a comparative study of Escherichia coli, Bacillus subtilis and Saccharomyces cerevisiae gene regulatory networks. We extract the minimal core of causal relations, uncovering the hierarchical and modular organisation from a novel dynamical/causal perspective. Our results reveal a marked top-down hierarchy containing several small dynamical modules for E. coli and B. subtilis. Conversely, the yeast network displays a single but large dynamical module in the middle of a bow-tie structure. We found that these dynamical modules capture the relevant wiring among both common and organism-specific biological functions such as transcription initiation, metabolic control, signal transduction, response to stress, sporulation and cell cycle. Functional and topological results suggest that two fundamentally different forms of logic organisation may have evolved in bacteria and yeast.

Theory for bowtie plasmonic nanolasers

We develop a fundamental formulation for electrically-pumped plasmonic semiconductor nanolasers based on a metallic bowtie structure. Because of the negative dielectric constant of the metal at optical frequencies, the effective modal volume of the plasmonic mode can be compressed to the nanometer scale. In addition, the curvature effect of the bowtie tips provides additional field enhancement in the bowtie gap and further reduces the modal volume. With this small modal volume, the required volume of the active region is reduced correspondingly, which significantly decreases the threshold current. The huge field enhancement due to the small modal volume at the gap of the bowtie may overcome the material and radiation losses by increasing both the spontaneous and stimulated emission rates, and it makes the lasing action possible.

Mining the inner structure of the Web graph**Partially supported by the EU under contract 001907 (DELIS) and 33555 (COSIN), and by the Italian MIUR under contract ALINWEB.

Despite being the sum of decentralized and uncoordinated efforts by heterogeneous groups and individuals, the World Wide Web exhibits a well-defined structure, characterized by several interesting properties. This structure was clearly revealed by Broder et al (2000 Graph structure in the web Comput. Netw. 33 309) who presented the evocative bow-tie picture of the Web. Although, the bow-tie structure is a relatively clear abstraction of the macroscopic picture of the Web, it is quite uninformative with respect to the finer details of the Web graph. In this paper, we mine the inner structure of the Web graph. We present a series of measurements on the Web, which offer a better understanding of the individual components of the bow-tie. In the process, we develop algorithmic techniques for performing these measurements. We discover that the scale-free properties permeate all the components of the bow-tie which exhibit the same macroscopic properties as the Web graph itself. However, close inspection reveals that their inner structure is quite distinct. We show that the Web graph does not exhibit self similarity within its components, and we propose a possible alternative picture for the Web graph, as it emerges from our experiments.

Reconstructing a Network of Stress-Response Regulators via Dynamic System Modeling of Gene Regulation

Unicellular organisms such as yeasts have evolved mechanisms to respond to environmental stresses by rapidly reorganizing the gene expression program. Although many stress-response genes in yeast have been discovered by DNA microarrays, the stress-response transcription factors (TFs) that regulate these stress-response genes remain to be investigated. In this study, we use a dynamic system model of gene regulation to describe the mechanism of how TFs may control a gene's expression. Then, based on the dynamic system model, we develop the Stress Regulator Identification Algorithm (SRIA) to identify stress-response TFs for six kinds of stresses. We identified some general stress-response TFs that respond to various stresses and some specific stress-response TFs that respond to one specific stress. The biological significance of our findings is validated by the literature. We found that a small number of TFs is probably sufficient to control a wide variety of expression patterns in yeast under different stresses. Two implications can be inferred from this observation. First, the response mechanisms to different stresses may have a bow-tie structure. Second, there may be regulatory cross-talks among different stress responses. In conclusion, this study proposes a network of stress-response regulators and the details of their actions.

Bow-tie topological features of metabolic networks and the functional significance

Exploring the structural topology of genome-based large-scale metabolic network is essential for investigating possible relations between structure and functionality. Visualization would be helpful for obtaining immediate information about structural organization. In this work, metabolic networks of 75 organisms were investigated from a topological point of view. A spread bow-tie model was proposed to give a clear visualization of the bow-tie structure for metabolic networks. The revealed topological pattern helps to design more efficient algorithm specifically for metabolic networks. This coarsegrained graph also visualizes the vulnerable connections in the network, and thus could have important implication for disease studies and drug target identifications. In addition, analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic networks has its own intrinsic and significant features which are significantly different from those of random networks.

The Edinburgh human metabolic network reconstruction and its functional analysis

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts.

Identifying Stress Transcription Factors Using Gene Expression and TF-Gene Association Data

Unicellular organisms such as yeasts have evolved to survive environmental stresses by rapidly reorganizing the genomic expression program to meet the challenges of harsh environments. The complex adaptation mechanisms to stress remain to be elucidated. In this study, we developed Stress Transcription Factor Identification Algorithm (STFIA), which integrates gene expression and TF-gene association data to identify the stress transcription factors (TFs) of six kinds of stresses. We identified some general stress TFs that are in response to various stresses, and some specific stress TFs that are in response to one specific stress. The biological significance of our findings is validated by the literature. We found that a small number of TFs may be sufficient to control a wide variety of expression patterns in yeast under different stresses. Two implications can be inferred from this observation. First, the adaptation mechanisms to different stresses may have a bow-tie structure. Second, there may exist extensive regulatory cross-talk among different stress responses. In conclusion, this study proposes a network of the regulators of stress responses and their mechanism of action.

Optical Near-Field Probe Integrated With Self-Aligned Bow-Tie Antenna and Electrostatic Actuator for Local Field Enhancement

Microelectromechanical systems (MEMS)-based near-field scanning optical microscopy (NSOM) probes with a bow-tie antenna structure consisting of two metal triangular electrodes separated by a narrow gap have been designed and fabricated. An electrostatic actuator is integrated on this bow-tie probe to decrease the gap width for enhancing the optical near-field intensity. A self-alignment process based on deep reactive ion etching and wet anisotropic etching is established to fabricate the symmetric bow-tie structure. The static and dynamic actuations of electrostatic actuators are examined. With the mechanical resonance of the antenna structure to lateral direction, NSOM imaging is performed in the visible range, and the subwavelength resolution beyond the diffraction limit of light is demonstrated

Cluster Fragmentation and Facile Cleavage of Phenyl Groups from the SnPh3Ligand in Reactions of Os3(CO)11(SnPh3)(μ-H) with CO and HSnPh3

Two new cluster complexes, Os3(CO)12(Ph)(μ3-SnPh), 8 (35% yield), and Os4(CO)16(μ4-Sn), 9 (10% yield), were formed when the complex Os3(CO)11(SnPh3)(μ-H), 6, was heated to reflux in toluene solvent under a CO atmosphere. Compound 8 was transformed to 9 in 28% yield when a solution in octane solvent was heated to reflux under a CO atmosphere. Biphenyl was a coproduct in this reaction. Compound 8 contains three osmium atoms with a triply bridging SnPh group. The triosmium cluster has opened and there is only one Os−Os bond between the three osmium atoms. Compound 9 contains two Os2(CO)8 groups held together by a central quadruply bridging tin atom, giving an overall bow-tie structure for the five metal atoms. Two other new compounds, Os2(CO)6(μ-SnPh2)2(SnPh3)2, 10, and HOs(CO)4(SnPh3), 11, were formed in 51% and 20% yields, respectively, from the reaction of 6 with HSnPh3. Compound 10 contains only two osmium atoms linked by an Os−Os bond and two bridging SnPh2 ligands. Each osmium atom also contains one te...

A comprehensive map of the toll‐like receptor signaling network

Recognition of pathogen-associated molecular signatures is critically important in proper activation of the immune system. The toll-like receptor (TLR) signaling network is responsible for innate immune response. In mammalians, there are 11 TLRs that recognize a variety of ligands from pathogens to trigger immunological responses. In this paper, we present a comprehensive map of TLRs and interleukin 1 receptor signaling networks based on papers published so far. The map illustrates the possible existence of a main network subsystem that has a bow-tie structure in which myeloid differentiation primary response gene 88 (MyD88) is a nonredundant core element, two collateral subsystems with small GTPase and phosphatidylinositol signaling, and MyD88-independent pathway. There is extensive crosstalk between the main bow-tie network and subsystems, as well as feedback and feedforward controls. One obvious feature of this network is the fragility against removal of the nonredundant core element, which is MyD88, and involvement of collateral subsystems for generating different reactions and gene expressions for different stimuli.

Probability Bow-Ties: A Transparent Risk Management Tool

The ‘bow-tie’ diagram is a constructive risk management tool, providing a pictorial representation of the relationship between hazards, initiating events, controls and consequences. It is easily understood by management, engineers, OHSE professionals, process operators and maintenance personnel involved in risk management. Rapid risk ranking methods are commonly used to evaluate the risks of simple likelihood-consequence pairs and are straightforward in application. Rapid risk ranking uses descriptors rather than numbers for likelihood, severity and risk, avoiding the common aversion to numerical values. Nonetheless, it is based on an underlying mathematical construction. These two tools have been combined to provide a simple and transparent risk management tool, the Probability Bow-tie, which can be applied to more complicated systems usually found in the process industries. With adherence to conservative assignment rules, the Probability Bow-tie provides a robust method for determining likelihoods for complicated systems, and assists in assessing the adequacy of controls to achieve a company's tolerable risk criteria. This paper presents a practical methodology using probability bow-ties for risk assessment using standard spreadsheet computer programs normally available in even the smallest operating companies. Probability bow-ties were constructed for a new hazardous chemical marine terminal, using spreadsheets to depict the Bow-tie structure and incorporating lookup tables for likelihoods, outcome severities and risk levels. The risk level for individual outcomes and aggregated risk levels for the terminal are calculated by spreadsheet linkages. The methodology is simple in application, is transparent, is easily maintained by the operating company, provides a useful training tool for hazard awareness and presents a realistic appreciation of the value of controls in place.