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PubMedDate not supplied

Capillary network morphology and capillary flow.

This paper examines the authors' research on capillary network morphology and the heterogeneity of capillary red cell (RBC) perfusion in skeletal muscle with the aim of demonstrating that capillary network structure plays a major role in determining flow distribution. Capillary network morphology was examined by quantifying the heterogeneity of capillary diameters, path and segment lengths, as well as the changes in configuration that occur as vessels accommodate themselves to continual changes of fiber length. Because of the network complexity and the two-phase nature of the perfusing blood, both spatial (i.e. among capillaries) and temporal heterogeneity of capillary perfusion were predicted to result. By means of computer analysis of video images of the microcirculation in vivo, we have demonstrated that more than 70% of the total spatial heterogeneity of capillary RBC perfusion arises from the capillary network as opposed to the arterioles, and that RBC flow continuously redistributes among capillaries. The spatial heterogeneity increases substantially as the arteriolar input to the network falls, and the data predict that during low-flow states, the network will fail to distribute blood properly among its constituent vessels. Thus passive rheological mechanisms and capillary network morphology are important determinants of functional capillary density.

Animals↗
PubMed2006

Social networks and college drinking: probing processes of social influence and selection.

This study used a three-wave panel design (N = 119 complete cases for all waves) to test for social influence and selection in the relation of college students' heavy drinking and that of their social networks. Evidence emerged for both social influence and selection, leading the authors to probe more specialized issues related to each. Results regarding social influence revealed, further, that greater presence in the network of individuals that the focal respondent considered "drinking buddies" was predictive of one's own later drinking, controlling for potential confounds. Network demographic characteristics associated with a "risky" (i.e., heavy-drinking) network also were documented. An additional finding, important for selection, was that wave-to-wave change in average network drinking appeared to result primarily from network members with different drinking levels being added to and dropped from the network. Findings are discussed in the context of the recent renaissance in social-network research.

Adolescent↗
PubMed2003

The association of disability, sociodemographic background, and social network type in later life.

The article examines the association of disability and sociodemographic background with the type of social network in which elders are embedded and considers the implications of such associations for network continuity. METHODS. Secondary analysis of data from a national probability sample of Jewish adults aged 60 and older in Israel (N = 2,079) was used. Multinomial logistic regression analysis was employed to predict membership in different network types. RESULTS. Higher education predicted membership in friends networks. Female gender, Asian/African origin, low income, and disability were associated with neighbors networks. Family networks were predicted by Asian/African origin, less education, younger age, and disability. Restricted networks were associated with lower income and disability, and particularly by the interaction of age and disability. DISCUSSION. The findings suggest that network stability is most jeopardized when the oldest old become disabled. The social care needs of this latter group will continue to demand public attention.

Africa↗
PubMed1985

Social networks and social support: implications for natural helper and community level interventions.

The convincing evidence of the relationship between social support, social networks, and health status has influenced the development of program strategies which are relevant to health education. This article focuses on the linkage between social support and social networks and health education programs which involve interventions at the network and community level. Two broad strategies are addressed: programs enhancing entire networks through natural helpers; and programs strengthening overlapping networks/communities through key opinion and informal leaders who are engaged in the process of community wide problem-solving. Following a brief overview of definitions, this article highlights several network characteristics which are often found to be related to physical and mental health status. Suggestions are made for how these network characteristics can be applied to the two program strategies. Principles of practice for the health educator, and some of the limitations of a social network approach are delineated. The article concludes with a recommendation for engaging in action research--a perspective highly consistent with both the strategies discussed and the concepts of social networks and social support. This approach not only recognizes, but also acts to strengthen indigenous skills and resources.

Adaptation, Psychological↗
PubMed2002

Automated modelling of signal transduction networks.

BACKGROUND: Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved. RESULTS: We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles. CONCLUSION: We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.

Computational Biology↗
PubMed2006

Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition.

BACKGROUND: Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks through characterization of the null space of the stoichiometric matrix (S). Singular value decomposition (SVD) of the ExPa matrix P has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of genome-scale stoichiometric matrices. RESULTS: We show that the SVD of a matrix (W) formed of random samples from the steady-state solution space of the hRBC metabolic network gives similar insights into the regulatory properties of the network as was obtained with SVD of P. This new approach has two main advantages. First, it works with a direct representation of the shape of the metabolic solution space without the confounding factor of a non-uniform distribution of the extreme pathways and second, the SVD procedure can be applied to a very large number of samples, such as will be produced from genome-scale networks. CONCLUSION: These results show that we are now in a position to study the network aspects of the regulatory problem in genome-scale metabolic networks through the use of random sampling.

Algorithms↗
PubMed2006

Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae.

BACKGROUND: Gene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network. RESULTS: We present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based, literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae. Using this method, we predict several hundreds of new transcriptional regulatory TF-gene links, along with experimental conditions in which known and predicted links become active. This approach unravels new links in the yeast gene transcriptional regulatory network by utilizing the known transcriptional regulatory interactions, and is particularly useful for breaking down the composite transcriptional regulatory network to condition specific networks. CONCLUSION: Our methods can facilitate future binding experiments, as they can considerably help focus on the TFs that must be surveyed to understand gene regulation.(Supplemental material and the latest version of the MATLAB implementation of the United Signature Algorithm is available online at 1 or [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).

Algorithms↗
PubMed2007

Least-squares methods for identifying biochemical regulatory networks from noisy measurements.

BACKGROUND: We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS) estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS). The Total Least Squares (TLS) technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks. RESULTS: The superior performance of the CTLS method in identifying network interactions is demonstrated on three examples: a genetic network containing four genes, a network describing p53 activity and mdm2 messenger RNA interactions, and a recently proposed kinetic model for interleukin (IL)-6 and (IL)-12b messenger RNA expression as a function of ATF3 and NF-kappaB promoter binding. For the first example, the CTLS significantly reduces the errors in the estimation of the Jacobian for the gene network. For the second, the CTLS reduces the errors from the measurements that are corrupted by white noise and the effect of neglected kinetics. For the third, it allows the correct identification, from noisy data, of the negative regulation of (IL)-6 and (IL)-12b by ATF3. CONCLUSION: The significant improvements in performance demonstrated by the CTLS method under the wide range of conditions tested here, including different levels and types of measurement noise and different numbers of data points, suggests that its application will enable more accurate and reliable identification and modelling of biochemical networks.

Algorithms↗
PubMed2004

Structure and evolution of protein interaction networks: a statistical model for link dynamics and gene duplications.

BACKGROUND: The structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several large-throughput datasets. Understanding the underlying evolutionary dynamics is crucial for discerning random parts of the network from biologically important properties shaped by natural selection. RESULTS: We present a detailed statistical analysis of the protein interactions in Saccharomyces cerevisiae based on several large-throughput datasets. Protein pairs resulting from gene duplications are used as tracers into the evolutionary past of the network. From this analysis, we infer rate estimates for two key evolutionary processes shaping the network: (i) gene duplications and (ii) gain and loss of interactions through mutations in existing proteins, which are referred to as link dynamics. Importantly, the link dynamics is asymmetric, i.e., the evolutionary steps are mutations in just one of the binding parters. The link turnover is shown to be much faster than gene duplications. Both processes are assembled into an empirically grounded, quantitative model for the evolution of protein interaction networks. CONCLUSIONS: According to this model, the link dynamics is the dominant evolutionary force shaping the statistical structure of the network, while the slower gene duplication dynamics mainly affects its size. Specifically, the model predicts (i) a broad distribution of the connectivities (i.e., the number of binding partners of a protein) and (ii) correlations between the connectivities of interacting proteins, a specific consequence of the asymmetry of the link dynamics. Both features have been observed in the protein interaction network of S. cerevisiae.

Biological Evolution↗
PubMed2006

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae.

BACKGROUND: The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. RESULTS: We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org) and SGD (http://www.yeastgenome.org/) databases. CONCLUSION: Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.

Computational Biology↗
PubMed1999

An epidemiological evaluation of the use of microbiological tools for identifying gonorrhoea infection networks.

We aimed to assess the utility of various techniques for identifying gonorrhoea infection networks. All residents of a non-metropolitan North Carolina county visiting a sexually transmitted disease (STD) clinic during a 17-month period were screened for gonorrhoea. Infection networks were estimated by serovar type combined with antibiotic resistance, arbitrarily primed polymerase chain reaction (AP-PCR), or temporal clustering. The residential addresses of infected patients were geocoded and mapped. Among 2 serovar types, the presence of distinguishing characteristics of a network, based on questionnaire data, was assessed with prevalence ratios and 95% confidence intervals (CIs) relative to those not in the network. Twenty-five serovar types were identified among 759 gonorrhoea infections. In one serovar, the networks further delineated by temporal clusters correlated with particular AP-PCR types. In most instances, however, different typing techniques painted different network pictures. No refined serovar network stood out as having a particular set of characteristics that could be used to shape intervention. Teasing out an individual infection network with unique characteristics will require the development and use of other microbiological tools.

Adult↗
PubMedDate not supplied

An infection control link nurse network in the care home setting.

Link nurse groups or networks are used to enhance practice at clinical level (Cooper, 2001), improve the collaboration and education of nursing staff (MacArthur, 1998) and, therefore, have an effect on patient care. The use of link nurse networks is widespread and applied to a range of nursing specialties, particularly in acute settings. Reference in the literature to link nurse networks in nursing homes is very limited, despite their existence. This article examines the advantages and disadvantages of link nurse networks and the link nurse role as described in the literature. In addition, comparisons are made with an established infection control link nurse network in North Wales nursing homes. The article describes the assessment of the North Wales network using an audit cycle. The efficacy of link nurse networks is rarely considered; however, the process of audit can enable the evaluation of the link nurse network in relation to staff education, monitoring of infection control practice and dissemination of information.

Education, Nursing, Continuing↗
PubMed2003

Social networks of old people in India: research and policy.

This article presents a comparative analysis of the available research on the social networks of older persons in India. Most of this research has been done in North Indian cities. The research foci of the available studies include network size, core networks and beyond, life course changes in networks, impacts of residency in old-age homes, gender differences, and joint and nuclear family residence. This research is discussed in terms of its policy implications. Because the research demonstrates that social networks are important for the welfare of older Indians, one can conclude that social policy that encourages the maintenance of robust networks throughout the life course may be worth pursuing. One aspect of policy is discussed. The analysis of the relationship between social network and gender suggests that current policies that can be seen as supporting gender inequality in terms of property may have a negative impact on the networks of older women.

Aged↗
PubMed2006

Why do hubs tend to be essential in protein networks?

The protein-protein interaction (PPI) network has a small number of highly connected protein nodes (known as hubs) and many poorly connected nodes. Genome-wide studies show that deletion of a hub protein is more likely to be lethal than deletion of a non-hub protein, a phenomenon known as the centrality-lethality rule. This rule is widely believed to reflect the special importance of hubs in organizing the network, which in turn suggests the biological significance of network architectures, a key notion of systems biology. Despite the popularity of this explanation, the underlying cause of the centrality-lethality rule has never been critically examined. We here propose the concept of essential PPIs, which are PPIs that are indispensable for the survival or reproduction of an organism. Our network analysis suggests that the centrality-lethality rule is unrelated to the network architecture, but is explained by the simple fact that hubs have large numbers of PPIs, therefore high probabilities of engaging in essential PPIs. We estimate that approximately 3% of PPIs are essential in the yeast, accounting for approximately 43% of essential genes. As expected, essential PPIs are evolutionarily more conserved than nonessential PPIs. Considering the role of essential PPIs in determining gene essentiality, we find the yeast PPI network functionally more robust than random networks, yet far less robust than the potential optimum. These and other findings provide new perspectives on the biological relevance of network structure and robustness.

Binding Sites↗
PubMed2006

A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs.

Small-world properties have been demonstrated for many complex networks. Here, we applied the discrete wavelet transform to functional magnetic resonance imaging (fMRI) time series, acquired from healthy volunteers in the resting state, to estimate frequency-dependent correlation matrices characterizing functional connectivity between 90 cortical and subcortical regions. After thresholding the wavelet correlation matrices to create undirected graphs of brain functional networks, we found a small-world topology of sparse connections most salient in the low-frequency interval 0.03-0.06 Hz. Global mean path length (2.49) was approximately equivalent to a comparable random network, whereas clustering (0.53) was two times greater; similar parameters have been reported for the network of anatomical connections in the macaque cortex. The human functional network was dominated by a neocortical core of highly connected hubs and had an exponentially truncated power law degree distribution. Hubs included recently evolved regions of the heteromodal association cortex, with long-distance connections to other regions, and more cliquishly connected regions of the unimodal association and primary cortices; paralimbic and limbic regions were topologically more peripheral. The network was more resilient to targeted attack on its hubs than a comparable scale-free network, but about equally resilient to random error. We conclude that correlated, low-frequency oscillations in human fMRI data have a small-world architecture that probably reflects underlying anatomical connectivity of the cortex. Because the major hubs of this network are critical for cognition, its slow dynamics could provide a physiological substrate for segregated and distributed information processing.

Adult↗
PubMed2000

Peer networks among heavy, regular and infrequent drinkers prior to marriage.

OBJECTIVE: Research has consistently demonstrated that, among adolescents, the characteristics of one's peers are important predictors of substance abuse. The impact of the peer network on adult drinking, however, has received considerably less attention. The purpose of the present study was to examine social network characteristics that are associated with heavy drinking in adulthood prior to marriage. METHOD: Couples were recruited at the time of their first marriage. Husbands and wives were each given identical questionnaire packets to complete at home, independently, as well as a postage-paid envelope for packet return. A broad range of constructs was assessed; included were personality characteristics, relationship functioning, drinking behavior and social network characteristics. Complete data were obtained from 471 husbands and 471 wives. RESULTS: The social networks of heavy-drinking men, compared to men drinking regularly or infrequently, were younger, more likely to be male and unmarried and consisted of friends rather than family or others. For both men and women, "drinking buddies" accounted for nearly 75% of the heavy drinkers' peer networks. The overall ratings of support and conflict created by peers did not differ according to drinking group, for either men or women. CONCLUSIONS: Prior to marriage, the social networks of heavy drinkers differ considerably from the networks of regular or infrequent drinkers with regard to the drinking patterns of their peers. An important finding was that heavy drinkers appear to experience a similar level of emotional, financial and practical support from their peer network compared to regular or infrequent drinkers.

Adult↗
PubMed2006

Applications of small-world network theory in alcohol epidemiology.

OBJECTIVE: This study develops a mathematical model of alcohol abuse in structured populations, such as communities and college campuses. The study employs a network model that has the capacity to incorporate a variety of forms of connectivity membership besides personal acquaintance, such as geographic proximity and common organizations. The model also incorporates a resilience dimension that indicates the susceptibility of each individual in a network to alcohol abuse. The model has the capacity to simulate the effect of moving alcohol abusers into networks of nonabusers, either as the result of treatment or membership in self-help organizations. METHOD: The study employs a small-world model. A cubic equation for each person (vertex on a graph) governs the evolution of an individual's state between 0 and 1 with regard to alcohol dependence, with 1 indicating absolute certainty of alcohol dependence. The simulations are dependent on initial conditions, the structure of the network, and the resilience distribution of the network. The simulations incorporate multiple realizations of social networks, showing the effect of different network structures. RESULTS: The model suggests that the prevalence of alcohol abuse can be minimized by treating a relatively small percentage of the study population. In the small populations that we studied, the critical point was 10% or less of the study population, but we emphasize that this is within the limitations and assumptions of this model. CONCLUSIONS: The use of a simple model that incorporates the influence of the social network neighbors in structured populations shows promise for helping to inform treatment and prevention policy.

Alcoholics Anonymous↗
PubMed2005

Kinetic constraints for formation of steady states in biochemical networks.

The constraint-based analysis has emerged as a useful tool for analysis of biochemical networks. This work introduces the concept of kinetic constraints. It is shown that maximal reaction rates are appropriate constraints only for isolated enzymatic reactions. For biochemical networks, it is revealed that constraints for formation of a steady state require specific relationships between maximal reaction rates of all enzymes. The constraints for a branched network are significantly different from those for a cyclic network. Moreover, the constraints do not require Michaelis-Menten constants for most enzymes, and they only require the constants for the enzymes at the branching or cyclic point. Reversibility of reactions at system boundary or branching point may significantly impact on kinetic constraints. When enzymes are regulated, regulations may impose severe kinetic constraints for the formation of steady states. As the complexity of a network increases, kinetic constraints become more severe. In addition, it is demonstrated that kinetic constraints for networks with co-regulation can be analyzed using the approach. In general, co-regulation enhances the constraints and therefore larger fluctuations in fluxes can be accommodated in the networks with co-regulation. As a first example of the application, we derive the kinetic constraints for an actual network that describes sucrose accumulation in the sugar cane culm, and confirm their validity using numerical simulations.

Biochemistry↗