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PubMed2004

The interactome as a tree--an attempt to visualize the protein-protein interaction network in yeast.

The refinement and high-throughput of protein interaction detection methods offer us a protein-protein interaction network in yeast. The challenge coming along with the network is to find better ways to make it accessible for biological investigation. Visualization would be helpful for extraction of meaningful biological information from the network. However, traditional ways of visualizing the network are unsuitable because of the large number of proteins. Here, we provide a simple but information-rich approach for visualization which integrates topological and biological information. In our method, the topological information such as quasi-cliques or spoke-like modules of the network is extracted into a clustering tree, where biological information spanning from protein functional annotation to expression profile correlations can be annotated onto the representation of it. We have developed a software named PINC based on our approach. Compared with previous clustering methods, our clustering method ADJW performs well both in retaining a meaningful image of the protein interaction network as well as in enriching the image with biological information, therefore is more suitable in visualization of the network.

Algorithms↗
PubMed1985

Depressive symptoms, social networks and social support of elderly women.

A total of 1,144 white married women aged 65-75 years living in Washington County, Maryland were interviewed during February-August 1979 as part of a larger study. This cross-sectional analysis was undertaken to investigate the question of whether or not selected demographic, social network, and social support characteristics of these women were related to their level of depressive symptoms. Women at the low end of the socioeconomic scale were found more likely to have a high level of depressive symptoms than were women at the high end. Two structural characteristics, size and homogeneity of the social network, were also found to be related to symptoms of depression, although only homogeneity of the social network reached statistical significance. There was a larger percentage of women with a high level of depressive symptoms among those with small networks and among those with heterogeneous networks. Those women with good quality networks, which offer the opportunity for social support, were much less likely to have a high level of depressive symptoms than others. Cross-sectionally, social network factors were related to level of depressive symptoms; this relationship now needs to be demonstrated prospectively.

Aged↗
PubMed1981

Network characteristics related to the well-being of normals: a comparative base.

An efficient method of mapping the networks of members of the general population is described. The method permits examination of the effect of several social participation and social network variables on the well-being of 1,050 subjects. Specifically, an index of avowed happiness is regressed on measures of network size, network density, number of instrumental supporters, number of confidants, kin as a major network component, number of dependent others, number of social contexts, and range of socializing. The regression procedure is carried out separately for male and female subjects. Results show that network size is the best predictor of the well-being of men while range of socializing is the best predictor of the well-being of women. These findings are explained in terms of men's and women's differing social responsibility. In conclusion it is suggested that mental health workers may benefit from an understanding of the differing stresses and supports that networks offer men and women.

Adolescent↗
PubMed1996

Colloid determination of fibrin network permeability.

The effects of polymers, dextran and polyvinylpyrolidone (PVP) and of albumin on the permeability of thrombin-induced fibrin networks developed in plasma were examined. Both PVP and dextran increased the network permeability and turbidity and increased the fibrin fibre thickness. The effect was molecular weight dependent. Derivation of the dimensionless permeability (permeability/fibre radius2) indicated that the increase in network permeability was mainly from altered arrangement of fibres and not from increased fibre thickness. The effects of albumin on network structure were similar to those of the polymers. Scanning electron microscopy of networks developed in plasma under the influence of dextran and poloxamer 188 showed fibres with increased thickness and a coarse nodular appearance. There was an increased tendency for fibres to be aggregated into clumps. It is suggested that during polymerization fibrin fibres and fibrin polymerization intermediaries behave as colloidal particles. Attractive forces between the particles are generated by soluble macromolecules such as plasma proteins or polymers. Attractive forces increase the thickness of fibrin fibres and induce a more permeable arrangement of the fibres in the network. The most likely colloidal mechanism is depletion flocculation. This would account for (1) the molecular weight dependence and concentration dependence of the effects of macromolecules, (2) the effects of macromolecules which do not bind to fibrin, (3) the effects of the surfactant poloxamer 188. Depletion flocculation may be a significant mechanism for biological regulation of fibrin network permeability by non-specific macromolecules such as soluble proteins or fibrin intermediaries.

Albumins↗
PubMed2000

The Atlanta Urban Networks Study: a blueprint for endemic transmission.

OBJECTIVE: To study prospectively social networks and behavior in a group of persons at risk for HIV because of their drug-using and sexual practices, with particular emphasis on the interaction of risks and concomitant network structure. METHODS: A longitudinal study was conducted of 228 respondents in Atlanta, Georgia in six inner-city community chains of connected persons, interviewing primary respondents and a sample of their contacts every 6 months for 2 years. Ascertained were: HIV and immunologic status; demographic, medical, and behavioral factors; and the composition of the social, sexual, and drug-using networks. RESULTS: The prevalence of HIV in this group was 13.3% and the incidence density was 1.8% per year. Substantial simultaneity of risk-taking was observed, with a high level of both non-injecting (crack, 82%) and injecting (heroin, cocaine or both, 16 30%) drug use, the exchange of sex or money for drugs by men (approximately 35%) and women (57-71%), and high frequency of same-sex sexual activity by men (9.4%) and women (33%). The intensity of interaction, as measured by network features such as microstructures and concurrency, was significantly greater than that observed in a low prevalence area with little endemic transmission. CONCLUSION: The traditional hierarchical classification of risk for HIV may impede our understanding of transmission dynamics, which, in the setting of an inner-city population, is characterized by simultaneity of risk-taking, and moderately intense network interactions. The study provides further evidence for the relationship of network structure to transmission dynamics, but highlights the difficulties of using network information for prediction of individual seroconversion.

Adult↗
PubMed2002

The role of social support networks for rural hospital nurses: supporting and sustaining the rural nursing work force.

OBJECTIVE: This survey study describes the workplace social support networks of rural hospital nurses in one geographic region of Colorado. This rural sample was compared with data from a sample of urban hospital nurses in Colorado. BACKGROUND: Social support networks influence nurses' satisfaction and retention. Nursing recruitment and retention is especially critical in rural areas where it takes healthcare facilities 60% longer than urban facilities to fill nursing vacancies. Little is known about rural nurses' social support networks. METHODS: The Social Network Questionnaire (SNQ) was mailed to all nurses within one rural region of Colorado. Descriptive, correlational and comparative statistics were used to evaluate the structure and function of these nurses' networks. RESULTS: The nurses' networks were predominantly peer-based, but managers provided significant functional supports. The types of support provided by managers depended on the managers' placement in the nurses' networks. Some managers provided only performance feedback, and other managers offered physical assistance and emotional support. Rural nurses expected more guidance from management than did their urban counterparts. IMPLICATIONS: This sample of nurses clearly delineated the types of leadership styles most beneficial to them. Communication of roles and expectations between staff and management can enhance nursing satisfaction in the workplace setting.

Adult↗
PubMed1985

The impact of social stressors and social networks on pediatric medical care use.

Substantial differences in the use of pediatric medical resources reinforce the need for identifying and understanding factors that influence the use of medical services for children. This research assesses the simultaneous impact of sociodemographic characteristics, health attitudes and beliefs, psychologic distress, social stressors, and social networks on the use of pediatric acute care services during a 12-month period. Using a prospective longitudinal study design, data were obtained on 513 children and their families enrolled in a prepaid group practice. Linear modeling results showed that health attitudes and social networks were important predictors of acute care utilization in addition to child's age, birth order, baseline health status, and ethnic group. The authors were able to show significant effects for network size, dispersion, and tendency to use one's network members. Individuals with large nondispersed networks are more likely to use pediatric health services, apparently due to the transmission of the networks' pro-medical care health beliefs. Also the tendency to call on network members modifies an individual's propensity to seek care for minor pediatric medical problems and can make a difference by as much as 1.6 visits per year per child for acute care episodes.

Adolescent↗
PubMed2002

Social network effects on the transmission of sexually transmitted diseases.

BACKGROUND: Only a limited number of studies have examined the effects of social networks on sexually transmitted diseases (STDs), although the findings of such studies would be helpful in designing more effective prevention strategies. GOAL: This study attempts (1) to determine whether there are any social network effects on STD transmission after controlling for other risk factors and (2) to explore the possible mechanisms by which social networks exert their effects. STUDY DESIGN: Logistic regressions examine the effects of friendship networks on STD transmission using data drawn from the Chicago Health and Social Life Survey (CHSLS). Additional logistic regressions probe the possible mechanisms (social control versus information and dyadic versus third party) that account for how the observed effects of networks might arise. RESULTS AND CONCLUSION: Among people who had fewer than 13 lifetime sex partners, those with no social friends were only 0.4 times as likely to be infected as those with one or more social friends. Among people who had 13 or more lifetime partners, those with many friends (5 or 6) but weak ties to them (talking less than once a day with them) were only 0.2 times as likely to be infected as those with either fewer friends or stronger ties to their friends. Additional regressions suggest that network effects resting on dyadic control prevail among those with fewer than 13 lifetime sex partners. In contrast, network effects resting on information exchange among third parties prevail among those with 13 or more lifetime partners.

Adult↗
PubMed2005

The evolution of network topology by selective removal.

The topology of large social, technical and biological networks such as the World Wide Web or protein interaction networks has caught considerable attention in the past few years (reviewed in Newman 2003), and analysis of the structure of such networks revealed that many of them can be classified as broad-tailed, scale-free-like networks, since their vertex connectivities follow approximately a power-law. Preferential attachment of new vertices to highly connected vertices is commonly seen as the main mechanism that can generate scale-free connectivity in growing networks (Watts 2004). Here, we propose a new model that can generate broad-tailed networks even in the absence of network growth, by not only adding vertices, but also selectively eliminating vertices with a probability that is inversely related to the sum of their first- and second order connectivity.

Animals↗
PubMed2006

Infectious disease control using contact tracing in random and scale-free networks.

Contact tracing aims to identify and isolate individuals that have been in contact with infectious individuals. The efficacy of contact tracing and the hierarchy of traced nodes-nodes with higher degree traced first-is investigated and compared on random and scale-free (SF) networks with the same number of nodes N and average connection K. For values of the transmission rate larger than a threshold, the final epidemic size on SF networks is smaller than that on corresponding random networks. While in random networks new infectious and traced nodes from all classes have similar average degrees, in SF networks the average degree of nodes that are in more advanced stages of the disease is higher at any given time. On SF networks tracing removes possible sources of infection with high average degree. However a higher tracing effort is required to control the epidemic than on corresponding random networks due to the high initial velocity of spread towards the highly connected nodes. An increased latency period fails to significantly improve contact tracing efficacy. Contact tracing has a limited effect if the removal rate of susceptible nodes is relatively high, due to the fast local depletion of susceptible nodes.

Animals↗
PubMed2001

The small world inside large metabolic networks.

The metabolic network of the catabolic, energy and biosynthetic metabolism of Escherichia coli is a paradigmatic case for the large genetic and metabolic networks that functional genomics efforts are beginning to elucidate. To analyse the structure of previously unknown networks involving hundreds or thousands of components by simple visual inspection is impossible, and quantitative approaches are needed to analyse them. We have undertaken a graph theoretical analysis of the E. coli metabolic network and find that this network is a small-world graph, a type of graph distinct from both regular and random networks and observed in a variety of seemingly unrelated areas, such as friendship networks in sociology, the structure of electrical power grids, and the nervous system of Caenorhabditis elegans. Moreover, the connectivity of the metabolites follows a power law, another unusual but by no means rare statistical distribution. This provides an objective criterion for the centrality of the tricarboxylic acid cycle to metabolism. The small-world architecture may serve to minimize transition times between metabolic states, and contains evidence about the evolutionary history of metabolism.

Escherichia coli↗
PubMed2003

Disease evolution on networks: the role of contact structure.

Owing to their rapid reproductive rate and the severe penalties for reduced fitness, diseases are under immense evolutionary pressure. Understanding the evolutionary response of diseases in new situations has clear public-health consequences, given the changes in social and movement patterns over recent decades and the increased use of antibiotics. This paper investigates how a disease may adapt in response to the routes of transmission available between infected and susceptible individuals. The potential transmission routes are defined by a computer-generated contact network, which we describe as either local (highly clustered networks where connected individuals are likely to share common contacts) or global (unclustered networks with a high proportion of long-range connections). Evolution towards stable strategies operates through the gradual random mutation of disease traits (transmission rate and infectious period) whenever new infections occur. In contrast to mean-field models, the use of contact networks greatly constrains the evolutionary dynamics. In the local networks, high transmission rates are selected for, as there is intense competition for susceptible hosts between disease progeny. By contrast, global networks select for moderate transmission rates because direct competition between progeny is minimal and a premium is placed upon persistence. All networks show a very slow but steady rise in the infectious period.

Biological Evolution↗
PubMed2003

Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network.

The metabolic network in the yeast Saccharomyces cerevisiae was reconstructed using currently available genomic, biochemical, and physiological information. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, and transport steps between the compartments and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network containing 1175 metabolic reactions and 584 metabolites. The number of gene functions included in the reconstructed network corresponds to approximately 16% of all characterized ORFs in S. cerevisiae. Using the reconstructed network, the metabolic capabilities of S. cerevisiae were calculated and compared with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions.

Amino Acids↗
PubMedDate not supplied

Theta/gamma networks with slow NMDA channels learn sequences and encode episodic memory: role of NMDA channels in recall.

This paper examines the role of slow N-methyl-D-aspartate (NMDA) channels (deactivation approximately 150 msec) in networks that multiplex different memories in different gamma subcycles of a low frequency theta oscillation. The NMDA channels are in the synapses of recurrent collaterals and govern synaptic modification in accord with known physiological properties. Because slow NMDA channels have a time constant that spans several gamma cycles, synaptic connections will form between cells that represent different memories. This enables brain structures that have slow NMDA channels to store heteroassociative sequence information in long-term memory. Recall of this stored sequence information can be initiated by presentation of initial elements of the sequence. The remaining sequence is then recalled at a rate of one memory every gamma cycle. A new role for the NMDA channel suggested by our finding is that recall at gamma frequency works well if slow NMDA channels provide the dominant component of the EPSP at the synapse of recurrent collaterals: The slow onset of these channels and their long duration allows the firing of one memory during one gamma cycle to trigger the next memory during the subsequent gamma cycle. An interesting feature of the readout mechanism is that the activation of a given memory is due to cumulative input from multiple previous memories in the stored sequence, not just the previous one. The network thus stores sequence information in a doubly redundant way: Activation of a memory depends on the strength of synaptic inputs from multiple cells of multiple previous memories. The cumulative property of sequence storage has support from the psychophysical literature. Cumulative learning also provides a solution to the disambiguation problem that occurs when different sequences have a region of overlap. In a final set of simulations, we show how coupling an autoassociative network to a heteroassociative network allows the storage of episodic memories (a unique sequence of briefly occurring known items). The autoassociative network (cortex) captures the sequence in short-term memory and provides the accurate, time-compressed repetition required to drive synaptic modification in the heteroassociative network (hippocampus). This is the first mechanistically detailed model showing how known brain properties, including network oscillations, recurrent collaterals, AMPA channels, NMDA channel subtypes, the ADP, and the AHP can act together to accomplish memory storage and recall.

Association Learning↗
PubMed1999

Mechanical and structural model of fractal networks of fat crystals at low deformations.

Fat-crystal networks demonstrate viscoelastic behavior at very small deformations. A structural model of these networks is described and supported by polarized light and atomic-force microscopy. A mechanical model is described which allows the shear elastic modulus (G') of the system to be correlated with forces acting within the network. The fractal arrangement of the network at certain length scales is taken into consideration. It is assumed that the forces acting are due to van der Waals forces. The final expression for G' is related to the volume fraction of solid fat (Phi) via the mass fractal dimension (D) of the network, which agrees with the experimental verification of the scaling behavior of fat-crystal networks [S. S. Narine and A. G. Marangoni, Phys. Rev. E 59, 1908 (1999)]. G' was also found to be inversely proportional to the diameter of the primary particles (sigma approximately equal to 6 microm) within the network (microstructural elements) as well as to the diameter of the microstructures (xi approximately equal to 100 microm) and inversely proportional to the cube of the intermicrostructural element distance (d(0)). This formulation of the elastic modulus agrees well with experimental observations.

Journal Article↗
PubMed2000

Geometry of river networks. II. Distributions of component size and number.

The structure of a river network may be seen as a discrete set of nested subnetworks built out of individual stream segments. These network components are assigned an integral stream order via a hierarchical and discrete ordering method. Exponential relationships, known as Horton's laws, between stream order and ensemble-averaged quantities pertaining to network components are observed. We extend these observations to incorporate fluctuations and all higher moments by developing functional relationships between distributions. The relationships determined are drawn from a combination of theoretical analysis, analysis of real river networks including the Mississippi, Amazon, and Nile, and numerical simulations on a model of directed, random networks. Underlying distributions of stream segment lengths are identified as exponential. Combinations of these distributions form single-humped distributions with exponential tails, the sums of which are in turn shown to give power-law distributions of stream lengths. Distributions of basin area and stream segment frequency are also addressed. The calculations identify a single length scale as a measure of size fluctuations in network components. This article is the second in a series of three addressing the geometry of river networks.

Ecosystem↗
PubMed2002

Highly clustered scale-free networks.

We propose a model for growing networks based on a finite memory of the nodes. The model shows stylized features of real-world networks: power-law distribution of degree, linear preferential attachment of new links, and a negative correlation between the age of a node and its link attachment rate. Notably, the degree distribution is conserved even though only the most recently grown part of the network is considered. As the network grows, the clustering reaches an asymptotic value larger than that for regular lattices of the same average connectivity and similar to the one observed in the networks of movie actors, coauthorship in science, and word synonyms. These highly clustered scale-free networks indicate that memory effects are crucial for a correct description of the dynamics of growing networks.

Journal Article↗
PubMed2002

Morphological characterization of in vitro neuronal networks.

We use in vitro neuronal networks as a model system for studying self-organization processes in the nervous system. We follow the neuronal growth process, from isolated neurons to fully connected two-dimensional networks. The mature networks are mapped into connected graphs and their morphological characteristics are measured. The distributions of segment lengths, node connectivity, and path length between nodes, and the clustering coefficient of the networks are used to characterize network morphology and to demonstrate that our networks fall into the category of small-world networks.

Animals↗