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At least 793 records · Page 44Linked to original sources
PubMed2003

Solvent-induced polymorphism of three-dimensional hydrogen-bonded networks of hexakis(4-carbamoylphenyl)benzene.

The crystal structures for three types of three-dimensional (3-D) hydrogen-bonded networks of hexakis(4-carbamoylphenyl)benzene (1), the network morphologies of which depend greatly on crystallization conditions, have been determined. When this compound is crystallized from hot DMSO, the resulting crystals, 1.12DMSO (orthorhombic, Pca2(1)), showed a 3-D hydrogen-bonded porous network (type A) via 1-D catemer chains as a hydrogen-bonding motif of six primary amide groups. The type A network creates chambers surrounded by six molecules of 1 and channels along the c axis to give the highest porosity among the network polymorphs of 1 investigated here. Crystallization from a boiling mixture of n-PrOH and water gave 1.6n-PrOH (monoclinic, P2(1)/c), which exhibits another type of 3-D hydrogen-bonded porous network (type B) via cyclic dimers as another hydrogen-bonding motif of six primary amide groups. The type B network leads to triangle-like channels along the a axis having a cross section of ca. 9.2 x 9.7 x 9.7 A (including van der Waals radii). The crystal structure of 1.H(2)O (monoclinic, P2(1)/c), which was produced under hydrothermal conditions, showed a nonporous 3-D hydrogen-bonded network chain of amide groups (type C) composed of a mixed hydrogen bonding motif of helical catemer chains/cyclic dimer/catemer. Solvent-induced topological isomerism of these 3-D hydrogen-bonded networks of 1 arises from (i) the guest inclusion ability based on a radially functionalized hexagonal structure of 1, (ii) the correlation between the hydrogen bond donor ability of the syn and anti protons of the primary amide group in host 1 and the hydrogen bond acceptor ability of the oxygen atoms of 1 and guest solvents, and (iii) the polarity of the bulk crystallization solvents.

Journal Article↗
PubMed1987

Differential effects of social support and social network on physiological and social outcomes in men and women with type II diabetes mellitus.

Patients with non-insulin-dependent diabetes mellitus (NIDDM) were advised to comply with a complex behavioral regimen of diet and exercise. The relationship between social support satisfaction and social support network size was evaluated using the Social Support Questionnaire for 32 men and 44 women with a confirmed diagnosis of NIDDM. Control of diabetes, as measured by the glycosylated hemoglobin assay, was significantly correlated with social support satisfaction for women but negatively correlated with social support satisfaction for men. Social support network size differentially predicted success in a program for men and women. For women, network size was significantly correlated with failure to attend sessions and with failure to complete a diary. For these women, network size was not significantly correlated with weight loss, which was the goal of the program. For men, network size was correlated with increases in weight, cholesterol, and triglycerides over an 18-month period. We conclude that social support network size and satisfaction have different functions for men and women faced with a serious chronic illness. Network size adversely affects success in a program, whereas social support satisfaction has some benefits for women. The direction of the influence of social network may be determined by the similarity or dissimilarity of network norms to the desired behavior.

Adult↗
PubMed1999

Reciprocal interactions between CA3 network activity and strength of recurrent collateral synapses.

In hippocampal slices, synchronous CA3 network activity induced persistent strengthening of active positive-feedback synapses. This altered network operation by increasing probability of future synchronous network activation. Long-term depression of synaptic strength induced by partial blockade of NMDA receptors during synchronous network activity reversed changes in probability of spontaneous network activation. These results suggest that specific network activity patterns selectively alter strength of active synapses. Stable, reversible alterations in network activity can also be effected by corresponding alterations in synaptic strength. These findings confirm the Hebb memory model at the neural-network level and suggest new therapies for pathological patterns of network activity in epilepsy.

Action Potentials↗
PubMed2005

Self-similarity of complex networks.

Complex networks have been studied extensively owing to their relevance to many real systems such as the world-wide web, the Internet, energy landscapes and biological and social networks. A large number of real networks are referred to as 'scale-free' because they show a power-law distribution of the number of links per node. However, it is widely believed that complex networks are not invariant or self-similar under a length-scale transformation. This conclusion originates from the 'small-world' property of these networks, which implies that the number of nodes increases exponentially with the 'diameter' of the network, rather than the power-law relation expected for a self-similar structure. Here we analyse a variety of real complex networks and find that, on the contrary, they consist of self-repeating patterns on all length scales. This result is achieved by the application of a renormalization procedure that coarse-grains the system into boxes containing nodes within a given 'size'. We identify a power-law relation between the number of boxes needed to cover the network and the size of the box, defining a finite self-similar exponent. These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics.

Journal Article↗
PubMed2006

Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach.

Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene's mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated.

Algorithms↗
PubMed2000

A role of network arrangements of microcirculation vessels in cardiovascular function.

The objective of the work was to explain the mechanisms for the appearance or creation of the so-called preferential channels for blood flow in tissues. We postulated that the addressed preferential blood flow occurs at the level of network structure of vascular system and results directly from its heterogeneity. The studies have been done in regular, two-dimensional network model of microvessels. Due to poorly defined dimensions, shapes and tortuousity of real vessels, the values of network elements were random. We examined how the heterogeneity of network elements influences flow redistribution within the network and the global network resistance to flow. The results indicated that the higher segmental resistance scatters the greater probability of the appearance within the network preferential flow paths passing through the whole network or being its local singularity. These channels significantly reduce the effect the global network resistance increases.

Cardiovascular Physiological Phenomena↗
PubMed2003

An adjustable aperiodic model class of genomic interactions using continuous time Boolean networks (Boolean delay equations).

Following the complete sequencing of several genomes, interest has grown in the construction of genetic regulatory networks, which attempt to describe how different genes work together in both normal and abnormal cells. This interest has led to significant research in the behavior of abstract network models, with Boolean networks emerging as one particularly popular type. An important limitation of these networks is that their time evolution is necessarily periodic, motivating our interest in alternatives that are capable of a wider range of dynamic behavior. In this paper we examine one such class, that of continuous-time Boolean networks, a special case of the class of Boolean delay equations (BDEs) proposed for climatic and seismological modeling. In particular, we incorporate a biologically motivated refractory period into the dynamic behavior of these networks, which exhibit binary values like traditional Boolean networks, but which, unlike Boolean networks, evolve in continuous time. In this way, we are able to overcome both computational and theoretical limitations of the general class of BDEs while still achieving dynamics that are either aperiodic or effectively so, with periods many orders of magnitude longer than those of even large discrete time Boolean networks.

Computer Simulation↗
PubMed2004

Boolean networks with variable number of inputs (K).

We studied a random Boolean network model with a variable number of inputs K per element. An interesting feature of this model, compared to the well-known fixed-K networks, is its higher orderliness. It seems that the distribution of connectivity alone contributes to a certain amount of order. In the present research, we tried to disentangle some of the reasons for this unexpected order. We also studied the influence of different numbers of source elements (elements with no inputs) on the network's dynamics. An analysis carried out on the networks with an average value of K=2 revealed a correlation between the number of source elements and the dynamic diversity of the network. As a diversity measure we used the number of attractors, their lengths and similarity. As a quantitative measure of the attractors' similarity, we developed two methods, one taking into account the size and the overlapping of the frozen areas, and the other in which active elements are also taken into account. As the number of source elements increases, the dynamic diversity of the networks does likewise: the number of attractors increases exponentially, while their similarity diminishes linearly. The length of attractors remains approximately the same, which indicates that the orderliness of the networks remains the same. We also determined the level of order that originates from the canalizing properties of Boolean functions and the propagation of this influence through the network. This source of order can account only for one-half of the frozen elements; the other half presumably freezes due to the complex dynamics of the network. Our work also demonstrates that different ways of assigning and redirecting connections between elements may influence the results significantly. Studying such systems can also help with modeling and understanding a complex organization and self-ordering in biological systems, especially the genetic ones.

Computer Simulation↗
PubMed2005

Topology of biological networks and reliability of information processing.

Survival of living cells and organisms is largely based on highly reliable function of their regulatory networks. However, the elements of biological networks, e.g., regulatory genes in genetic networks or neurons in the nervous system, are far from being reliable dynamical elements. How can networks of unreliable elements perform reliably? We here address this question in networks of autonomous noisy elements with fluctuating timing and study the conditions for an overall system behavior being reproducible in the presence of such noise. We find a clear distinction between reliable and unreliable dynamical attractors. In the reliable case, synchrony is sustained in the network, whereas in the unreliable scenario, fluctuating timing of single elements can gradually desynchronize the system, leading to nonreproducible behavior. The likelihood of reliable dynamical attractors strongly depends on the underlying topology of a network. Comparing with the observed architectures of gene regulation networks, we find that those 3-node subgraphs that allow for reliable dynamics are also those that are more abundant in nature, suggesting that specific topologies of regulatory networks may provide a selective advantage in evolution through their resistance against noise.

Biological Evolution↗
PubMed2003

Protein complexes and functional modules in molecular networks.

Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

Biophysical Phenomena↗
PubMed2003

Network component analysis: reconstruction of regulatory signals in biological systems.

High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.

Biology↗
PubMed2005

An electron-sharing network involved in the catalytic mechanism is functionally conserved in different glutathione transferase classes.

In Anopheles dirus glutathione transferase D3-3, there are electrostatic interactions between the negatively charged glutamyl alpha-carboxylate group of glutathione, the positively charged Arg-66, and the negatively charged Asp-100. This ionic interaction is stabilized by a network of hydrogen bonds from Ser-65, Thr-158, Thr-162, and a conserved water-mediated contact. This alternating ionic bridge interaction between negatively and positively charged residues stabilized by a network of hydrogen bonding we have named an electron-sharing network. We show that the electron-sharing network assists the glutamyl alpha-carboxylate of glutathione to function as a catalytic base accepting the proton from the thiol group forming an anionic glutathione, which is a crucial step in the glutathione transferase (GST) catalysis. Kinetic studies demonstrate that the mutation of electron-sharing network residues results in a decreased ability to lower the pKa of the thiol group of glutathione. Although the residues that contribute to the electron-sharing network are not conserved in the primary sequence, structural characterizations indicate that the presence of the network can be mapped to the same region in all GST classes. A structural diversification but functional conservation suggests a significant role for the electron-sharing network in catalysis as the purpose was maintained during the divergent evolution of GSTs. This network appears to be a functionally conserved motif that contributes to the "base-assisted deprotonation" model suggested to be essential for the glutathione ionization step of the catalytic mechanism.

Animals↗
PubMed1998

Personal social networks and HIV status among women on methadone.

This objective of this study was to examine the association between a women's HIV status and specific (IDUs) characteristics of her social networks with respect to (1) number of injection drug users (2) number of drug partners and (3) number of HIV-positive contacts in her personal networks, after controlling for the respondent's demographic characteristics and drug use. Participants were recruited through posted announcements in three methadone clinics in Harlem, New York City. Individuals were considered eligible if they were enrolled as patients in one of the clinics for at least three months. A social network questionnaire modeled after the General Social Survey network section was developed by the investigators. Face-to-face interviews were conducted by trained interviewers and included demographics, drug use, self-reported HIV status of the woman and her network members, and the social network structures. Univariate analyses found that HIV-positive and HIV-negative women had different network profiles. HIV-positive women were more likely to associate with a higher number of current drug users, injection drug users, injection drug users who were HIV-positive, drug partners, drug partners who used injection drugs, and drug partners who were HIV-positive. Multivariate analyses indicated that HIV-positive respondents were more likely to associate with HIV-positive network members than their HIV-negative counterparts. The findings suggest that to better understand the spread of HIV among female drug users and to design more effective HIV/AIDS prevention programmes, efforts should move beyond focusing on individual attributes to address the contextual dynamics of social networks.

Adult↗
PubMed2005

Does diagnosis matter? Differential effects of 12-step participation and social networks on abstinence.

Previous studies that have examined the effects of specific aspects of 12-step participation and social network composition on abstinence have focused mostly on alcohol-related outcomes and have screened out drug dependent persons. This article explores whether these predictors differentially affect abstinence based on DSM-III-R substance dependence disorder (alcohol dependence, drug dependence, and both alcohol and drug dependence). A heterogeneous community sample of treatment seekers (N=302) randomized to day treatment programs were followed at 6 and 12 months. Bivariate and multivariate regression models were used to test whether engagement in 12-step practices and social network influences to drink or use drugs predicted total abstinence from alcohol and drugs differentially by dependence disorder. Chi-square automatic interaction detector (CHAID) segmentation analyses were then conducted to identify the specific 12-step activities and social network thresholds that best distinguished higher rates of abstinence in each dependence category. Results showed that the number of 12-step meetings attended and number of prescribed 12-step activities engaged in similarly predicted abstinence for alcoholics, drug addicts, and those dependent on both alcohol and drugs. However, specific activities were associated with abstinence differentially by dependence disorder. While many activities differentiated abstinence for drug addicts and those dependent on both alcohol and drugs, for alcoholics only two Alcoholics Anonymous (AA) activities distinguished abstinence (having a sponsor and doing service). Key predictors of abstinence (CHAID) varied by follow-up and dependence disorder, except for doing service in AA and/or Narcotics Anonymous, which was the only specific 12-step activity that was a best predictor of abstinence in all three categories one year following treatment. Thus, "giving back" to one's peer community through service work, an important 12-step belief, seems to be universally valuable later in recovery. As for social network influences, a multivariate regression model showed that having a higher proportion of abstinent individuals in the network was associated with abstinence for alcoholics at 6 months only and for drug dependent persons at 12 months only. CHAID models supported these results and provided specific thresholds for 12-step measures (e.g., >20 meetings for alcoholics, 2 or more nondrinkers in the social network, 3 or more persons supporting reduction for those dependent on both alcohol and drugs, and having 2 or more nondrinkers for those dependent on drugs only). These results support the value of treatment providers prioritizing certain 12-step-related practices and social network changes based on their client dependence profiles. Early on, those with an alcohol diagnosis need to make a commitment to meetings and obtain a sponsor; also, they need to place themselves in a network that encourages sobriety. Early on, those who are drug-dependent-only especially need to become connected with 12-step programs to the extent that they consider themselves a member, and, later, saturate themselves in a highly supportive and predominantly nondrinking environment. Alcohol and drug dependent clients need more intense ongoing 12-step involvement (sponsor and meetings) as well as having nondrinking individuals and people supportive of abstinence in their network. For all clients, doing service is especially important at the longer 12-month posttreatment timeframe.

Adolescent↗
PubMed2005

Self-organization of an acentrosomal microtubule network at the basal cortex of polarized epithelial cells.

Mechanisms underlying the organization of centrosome-derived microtubule arrays are well understood, but less is known about how acentrosomal microtubule networks are formed. The basal cortex of polarized epithelial cells contains a microtubule network of mixed polarity. We examined how this network is organized by imaging microtubule dynamics in acentrosomal basal cytoplasts derived from these cells. We show that the steady-state microtubule network appears to form by a combination of microtubule-microtubule and microtubule-cortex interactions, both of which increase microtubule stability. We used computational modeling to determine whether these microtubule parameters are sufficient to generate a steady-state acentrosomal microtubule network. Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network. However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ. These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

Animals↗
PubMed2002

Gene perturbation and intervention in probabilistic Boolean networks.

MOTIVATION: A major objective of gene regulatory network modeling, in addition to gaining a deeper understanding of genetic regulation and control, is the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. We consider the general question of the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene perturbation as well as intervention in order to elicit desired network behavior. RESULTS: Using a recently introduced class of models, called Probabilistic Boolean Networks (PBNs), this paper develops a model for random gene perturbations and derives an explicit formula for the transition probabilities in the new PBN model. This result provides a building block for performing simulations and deriving other results concerning network dynamics. An example is provided to show how the gene perturbation model can be used to compute long-term influences of genes on other genes. Following this, the problem of intervention is addressed via the development of several computational tools based on first-passage times in Markov chains. The consequence is a methodology for finding the best gene with which to intervene in order to most likely achieve desirable network behavior. The ideas are illustrated with several examples in which the goal is to induce the network to transition into a desired state, or set of states. The corresponding issue of avoiding undesirable states is also addressed. Finally, the paper turns to the important problem of assessing the effect of gene perturbations on long-run network behavior. A bound on the steady-state probabilities is derived in terms of the perturbation probability. The result demonstrates that states of the network that are more 'easily reachable' from other states are more stable in the presence of gene perturbations. Consequently, these are hypothesized to correspond to cellular functional states. AVAILABILITY: A library of functions written in MATLAB for simulating PBNs, constructing state-transition matrices, computing steady-state distributions, computing influences, modeling random gene perturbations, and finding optimal intervention targets, as described in this paper, is available on request from is@ieee.org.

Chromosome Mapping↗
PubMed2003

The small-world dynamics of tree networks and data mining in phyloinformatics.

MOTIVATION: A noble and ultimate objective of phyloinformatic research is to assemble, synthesize, and explore the evolutionary history of life on earth. Data mining methods for performing these tasks are not yet well developed, but one avenue of research suggests that network connectivity dynamics will play an important role in future methods. Analysis of disordered networks, such as small-world networks, has applications as diverse as disease propagation, collaborative networks, and power grids. Here we apply similar analyses to networks of phylogenetic trees in order to understand how synthetic information can emerge from a database of phylogenies. RESULTS: Analyses of tree network connectivity in TreeBASE show that a collection of phylogenetic trees behaves as a small-world network-while on the one hand the trees are clustered, like a non-random lattice, on the other hand they have short characteristic path lengths, like a random graph. Tree connectivities follow a dual-scale power-law distribution (first power-law exponent approximately 1.87; second approximately 4.82). This unusual pattern is due, in part, to the presence of alternative tree topologies that enter the database with each published study. As expected, small collections of trees decrease connectivity as new trees are added, while large collections of trees increase connectivity. However, the inflection point is surprisingly low: after about 600 trees the network suddenly jumps to a higher level of coherence. More stringent definitions of 'neighbour' greatly delay the threshold whence a database achieves sufficient maturity for a coherent network to emerge. However, more stringent definitions of 'neighbour' would also likely show improved focus in data mining. AVAILABILITY: http://treebase.org

Algorithms↗
PubMed2004

Minimal cut sets in biochemical reaction networks.

MOTIVATION: Structural studies of metabolic networks yield deeper insight into topology, functionality and capabilities of the metabolisms of different organisms. Here, we address the analysis of potential failure modes in metabolic networks whose occurrence will render the network structurally incapable of performing certain functions. Such studies will help to identify crucial parts in the network structure and to find suitable targets for repressing undesired metabolic functions. RESULTS: We introduce the concept of minimal cut sets for biochemical networks. A minimal cut set (MCS) is a minimal (irreducible) set of reactions in the network whose inactivation will definitely lead to a failure in certain network functions. We present an algorithm which enables the computation of the MCSs in a given network related to user-defined objective reactions. This algorithm operates on elementary modes. A number of potential applications are outlined, including network verifications, phenotype predictions, assessing structural robustness and fragility, metabolic flux analysis and target identification in drug discovery. Applications are illustrated by the MCSs in the central metabolism of Escherichia coli for growth on different substrates. AVAILABILITY: Computation and analysis of MCSs is an additional feature of the FluxAnalyzer (freely available for academic users upon request, special contracts for industrial companies; see web page below). SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer

Algorithms↗