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At least 487 records · Page 27Linked to original sources
PubMed2003

The evolutionary potential of the Drosophila sex determination gene network.

The evolution of sex determination mechanisms is known to be relatively rapid, though recent evidence indicates that certain parts of the mechanism may be more highly conserved. These characteristics establish the sex determination mechanism as a good candidate for the theoretical study of gene network evolution, particularly of networks involved in development. We investigate the short-term evolutionary potential of the sex determination mechanism in Drosophila melanogaster with the aid of a synchronous logical model. We introduce general theoretical concepts such as a network-specific form of mutation, and a notion of functional equivalence between networks. We apply this theoretical framework to the sex determination mechanism and compare it to a population of random networks, enabling us to find features both general to sex determination networks, and particular to the Drosophila network. In general, sex determination networks exist within large sets of functionally equivalent networks all of which satisfy the sex determination task. These large sets are in turn composed of subsets which are mutationally related, suggesting a high degree of flexibility is available without compromising the core functionality. Two particular characteristics of the Drosophila network are found: (a) a parsimonious use of gene interactions, and (b) the network structure can produce a relatively large number of dynamical pattern variations through single network mutations.

Animals↗
PubMed1999

An information theoretic approach for combining neural network process models.

Typically neural network modelers in chemical engineering focus on identifying and using a single, hopefully optimal, neural network model. Using a single optimal model implicitly assumes that one neural network model can extract all the information available in a given data set and that the other candidate models are redundant. In general, there is no assurance that any individual model has extracted all relevant information from the data set. Recently, Wolpert (Neural Networks, 5(2), 241 (1992)) proposed the idea of stacked generalization to combine multiple models. Sridhar, Seagrave and Barlett (AIChE J., 42, 2529 (1996)) implemented the stacked generalization for neural network models by integrating multiple neural networks into an architecture known as stacked neural networks (SNNs). SNNs consist of a combination of the candidate neural networks and were shown to provide improved modeling of chemical processes. However, in Sridhar's work SNNs were limited to using a linear combination of artificial neural networks. While a linear combination is simple and easy to use, it can utilize only those model outputs that have a high linear correlation to the output. Models that are useful in a nonlinear sense are wasted if a linear combination is used. In this work we propose an information theoretic stacking (ITS) algorithm for combining neural network models. The ITS algorithm identifies and combines useful models regardless of the nature of their relationship to the actual output. The power of the ITS algorithm is demonstrated through three examples including application to a dynamic process modeling problem. The results obtained demonstrate that the SNNs developed using the ITS algorithm can achieve highly improved performance as compared to selecting and using a single hopefully optimal network or using SNNs based on a linear combination of neural networks.

Journal Article↗
PubMed2000

Evaluation of neural network models with generalized sensitivity analysis

A sensitivity analysis method for discovering characteristic features of the input data using neural network classification models has been devised. The sensitivity is the gradient of the neural network model response function, and because neural network models are nonlinear, the gradient depends on the point where it is evaluated. Two criteria are used for measuring the sensitivity. The first criterion calculates the sensitivity or gradient of the neural network output with respect to the average of the objects that comprise each class. The second criterion measures the average sensitivity of the class objects. The sensitivity analysis was applied to temperature-constrained cascade correlation network models and evaluated with sets of synthetic data and experimental mobility spectra. The neural network models were built using temperature-constrained cascade correlation networks (TCCCNs). A weight constraint was devised for the output units of the network models. This method implements weight decay with conjugate gradient training and yields more sensitive neural network models. Temperature-constrained hidden units furnish more sensitive network models than networks without constraints. By comparing the sensitivities of the class mean input and the mean sensitivity for all the inputs of a class, the individual input variables may be assessed for linearity. If these two sensitivities for an input variable differ by a constant factor, then that variable is modeled by a simple linear relationship. If the two sensitivities vary by a nonconstant scale factor, then the variable is modeled by higher order functions in the network. The sensitivity method was used to diagnose errors in the training data, and the test for linearity indicated a TCCCN architecture that had better predictability.

Journal Article↗
PubMed1987

Social networks: we get by with (and in spite of) a little help from our friends.

Studies of social support networks have almost exclusively measured only their positive aspects. In this research, we investigated both the helpful or positive and the upsetting or negative aspects of social networks in a longitudinal study of spouses caring for a husband or wife with Alzheimer's disease, a progressive senile dementia. Measures of helpful and upsetting aspects of the care givers' networks, derived from interviews and daily interaction ratings, were studied for their relations with overall network satisfaction and depression at an initial interview period (n = 68) and at a follow-up period about 10 months later (n = 38). Results from hierarchical multiple regression analyses, in which care givers' age and sex and a measure of the spouses' health status were controlled, showed that the care givers' degree of upset with their networks was strongly associated with lower network satisfaction and increased depression at both time periods. Helpful aspects bore little or no direct relation to either depression or network satisfaction. Helpful aspects of the network did, however, interact with network upset in predicting network satisfaction, and depression (combined probabilities test, p less than .05). Longitudinal predictions of follow-up depression, after age, sex, care givers' health status, and initial depression levels were controlled, showed that changes in upsetting aspects of one's network were predictive of changes in depression over time. We interpreted these results within an attributional framework that emphasizes the salience of upsetting events within a social network.

Adaptation, Psychological↗
PubMed2006

Functional holography analysis: simplifying the complexity of dynamical networks.

We present a novel functional holography (FH) analysis devised to study the dynamics of task-performing dynamical networks. The latter term refers to networks composed of dynamical systems or elements, like gene networks or neural networks. The new approach is based on the realization that task-performing networks follow some underlying principles that are reflected in their activity. Therefore, the analysis is designed to decipher the existence of simple causal motives that are expected to be embedded in the observed complex activity of the networks under study. First we evaluate the matrix of similarities (correlations) between the activities of the network's components. We then perform collective normalization of the similarities (or affinity transformation) to construct a matrix of functional correlations. Using dimension reduction algorithms on the affinity matrix, the matrix is projected onto a principal three-dimensional space of the leading eigenvectors computed by the algorithm. To retrieve back information that is lost in the dimension reduction, we connect the nodes by colored lines that represent the level of the similarities to construct a holographic network in the principal space. Next we calculate the activity propagation in the network (temporal ordering) using different methods like temporal center of mass and cross correlations. The causal information is superimposed on the holographic network by coloring the nodes locations according to the temporal ordering of their activities. First, we illustrate the analysis for simple, artificially constructed examples. Then we demonstrate that by applying the FH analysis to modeled and real neural networks as well as recorded brain activity, hidden causal manifolds with simple yet characteristic geometrical and topological features are deciphered in the complex activity. The term "functional holography" is used to indicate that the goal of the analysis is to extract the maximum amount of functional information about the dynamical network as a whole unit.

Algorithms↗
PubMed2005

Identifying, recruiting, and assessing social networks at high risk for HIV/AIDS: methodology, practice, and a case study in St Petersburg, Russia.

Population segments at highest risk for HIV are often hidden, marginalized, and hard to reach by conventional prevention programmes. This pattern is especially true in Central and Eastern Europe, where major HIV epidemics have recently appeared, where population members do not perceive themselves as belonging to a community, and where there is little precedence for strong community-based organization service programmes. In these circumstances, naturally existing intact social networks still can be targeted by prevention programmes. HIV prevention interventions undertaken with at-risk social networks can establish new group norms, reduce the risk behaviour of network members, and can reach 'hidden' members of a population known personally to leaders of the social networks. This article illustrates a methodology and a practical description for: (1) accessing high-risk social networks in a community population; (2) identifying and enumerating the membership of the social networks; (3) identifying the social leadership of the networks; and (4) establishing the HIV risk behaviour levels of the recruited networks. To illustrate how social network methods can be applied in the field, the article provides case study reports of HIV prevention fieldwork practice targeting high-risk networks of young men who have sex with men and young heterosexual adults in St Petersburg, Russia. Although there is an extensive conceptual literature on the influence of social networks on risk behaviour, this article describes specific and practical techniques that can be in the development of approaches for social network-based interventions.

Adult↗
PubMed2000

The TH1 and TH2 cytokine network in healthy subjects: suggestions for experimental studies to create prognostic and diagnostic indices for biotherapeutic treatments.

In vivo and in vitro studies have demonstrated the selective regulatory effect that TH1 and TH2 cytokines reciprocally exert in the regulation of the polarization of precursor cells into TH1 or TH2 types. The study of the network relationships between TH1 and TH2 (TH1/TH2) cytokines in healthy subjects could lead to a better understanding of how the physiological network of cytokines regulates the immune response. Such study could lead to gain suggestions for follow-up experiments to create prognostic and diagnostic indices for biotherapeutic treatments of patients. Hence we determined serum levels (environment network) and PBMC production (cellular network) of IL2, IFN gamma, IL4, IL6 and IL10 in the peripheral blood of healthy subjects; these cytokines made up our networks under basic conditions. Both men and women were studied as hormones can influence the polarization of TH1 and TH2 cells. Cytokines within the physiological network function simultaneously so multivariate statistical methods were used to study TH1/TH2 relationships. The use of mathematical modelling is the only effective way of studying the immune system as a whole. The physiological TH1/TH2 network under activation conditions was evaluated by incorporating: sIL2R and sIL6R into the basic environment network model and the production levels of cytokines by PBMC after PHA stimulus, into the basic cellular network model. The influence of APC was evaluated by adding: serum levels of TNF alpha and IL1 beta to the environment network model, and production levels of IFN gamma, IL10 and IL6, after stimulus with LPS, to the cellular network model. Our results led us to hypothesize that the physiological network of TH1/TH2 cytokines regulates TH polarization by means of specific relationships between TH1 and TH2 cytokines, which may be different in men and women. These relationships could be studied experimentally to create prognostic and diagnostic indices for more efficient prevention programs and biotherapeutic treatments of patients.

Adult↗
PubMed2004

Back to the biology in systems biology: what can we learn from biomolecular networks?

Genome-scale molecular networks, including protein interaction and gene regulatory networks, have taken centre stage in the investigation of the burgeoning disciplines of systems biology and biocomplexity. What do networks tell us? Some see in networks simply the comprehensive, detailed description of all cellular pathways, others seek in networks simple, higher-order qualities that emerge from the collective action of the individual pathways. This paper discusses networks from an encompassing category of thinking that will hopefully help readers to bridge the gap between these polarised viewpoints. Systems biology so far has emphasised the characterisation of large pathway maps. Now one has to ask: where is the actual biology in 'systems biology'? As structures midway between genome and phenome, and by serving as an 'extended genotype' or an 'elementary phenotype', molecular networks open a new window to the study of evolution and gene function in complex living systems. For the study of evolution, features in network topology offer a novel starting point for addressing the old debate on the relative contributions of natural selection versus intrinsic constraints to a particular trait. To study the function of genes, it is necessary not only to see them in the context of gene networks, but also to reach beyond describing network topology and to embrace the global dynamics of networks that will reveal higher-order, collective behaviour of the interacting genes. This will pave the way to understanding how the complexity of genome-wide molecular networks collapses to produce a robust whole-cell behaviour that manifests as tightly-regulated switching between distinct cell fates - the basis for multicellular life.

Animals↗
PubMed2002

Effects of provider networks on health care costs for workers with short-term injuries.

OBJECTIVES: This study examines the effects of preferred provider networks on health care costs and service utilization in the treatment of work-related injuries. RESEARCH DESIGN: A retrospective comparison of workers' compensation claims treated by network and non-network providers was conducted. Pairwise matches of individual cases are used to control for differences in case mix and severity of injury between network patients and a non-network comparison group. Cost differentials are separated into a price effect, the difference in costs attributed to network price discounts, holding services constant; and a utilization effect, the difference in costs attributed to differences in service utilization, holding prices constant. SUBJECTS: Data include approximately 87,000 workers' compensation claims, from California, Connecticut, and Texas, with injury dates between 1995 and 1997. The samples are restricted to five common injury types and work absences of less than 7 days. Workers treated solely by network providers are compared with a matched group of workers treated solely by non-network providers. RESULTS: Average health care costs are lower for network claims than for matched non-network claims. Price discounts explain a large part of the cost differentials for all injury groups studied, but differences in service utilization are also important for back cases and cumulative stress injuries. CONCLUSIONS: Networks bring the traditionally high costs of health care for work-related injuries closer to the costs of health care for off-the-job injuries. The network savings primarily reflect price discounts for the same services, thereby representing an increase in the cost-effectiveness of care.

California↗
PubMed2000

Comparative epidemiology of heterosexual gonococcal and chlamydial networks: implications for transmission patterns.

OBJECTIVE: Networks of sex-partner interaction affect differential risk of acquiring sexually transmitted infections. The authors evaluated sociodemographic and behavioral factors that correlated with membership in networks of gonococcal and chlamydial transmission. METHODS: Face-to-face interviews were conducted with 127 patients with gonorrhea and 184 patients with chlamydia (index cases) and their named sex partners, as well as the partners of infected partners. Detailed information was obtained regarding demographic, behavioral, and sexual-history characteristics of all respondents. RESULTS: Gonococcal-network members differed significantly from chlamydial-network members in a number of demographic variables, including race or ethnicity, education, and unemployment status. Gonococcal-network members were more likely to report past history of crack-cocaine use, sexual assault, and having been in jail. Gonococcal-network members also reported having more sex partners during the past 1 year and 3 months than did chlamydial-network members. Gonococcal and chlamydial mixing matrices demonstrated assortativeness for sex partner selection by race or ethnicity but not by sexual activity level, and no systematic differences between networks were noted. Gonococcal networks were larger than chlamydial networks. CONCLUSIONS: Network analyses of gonococcal and chlamydial infections demonstrated significant differences in sociodemographic and behavioral variables. Further research is required to delineate specific predictors of network membership among persons at risk for sexually transmitted infections.

Adolescent↗
PubMed2006

Complexities and uncertainties of neuronal network function.

The nervous system generates behaviours through the activity in groups of neurons assembled into networks. Understanding these networks is thus essential to our understanding of nervous system function. Understanding a network requires information on its component cells, their interactions and their functional properties. Few networks come close to providing complete information on these aspects. However, even if complete information were available it would still only provide limited insight into network function. This is because the functional and structural properties of a network are not fixed but are plastic and can change over time. The number of interacting network components, their (variable) functional properties, and various plasticity mechanisms endows networks with considerable flexibility, but these features inevitably complicate network analyses. This review will initially discuss the general approaches and problems of network analyses. It will then examine the success of these analyses in a model spinal cord locomotor network in the lamprey, to determine to what extent in this relatively simple vertebrate system it is possible to claim detailed understanding of network function and plasticity.

Animals↗
PubMed2003

Reconciling gene expression data with known genome-scale regulatory network structures.

The availability of genome-scale gene expression data sets has initiated the development of methods that use this data to infer transcriptional regulatory networks. Alternatively, such regulatory network structures can be reconstructed based on annotated genome information, well-curated databases, and primary research literature. As a first step toward reconciling the two approaches, we examine the consistency between known genome-wide regulatory network structures and extensive gene expression data collections in Escherichia coli and Saccharomyces cerevisiae. By decomposing the regulatory network into a set of basic network elements, we can compute the local consistency of each instance of a particular type of network element. We find that the consistency of network elements is influenced by both structural features of the network such as the number of regulators acting on a target gene and by the functional classes of the genes involved in a particular element. Taken together, the approach presented allows us to define regulatory network subcomponents with a high degree of consistency between the network structure and gene expression data. The results suggest that targeted gene expression profiling data can be used to refine and expand particular subcomponents of known regulatory networks that are sufficiently decoupled from the rest of the network.

Computational Biology↗
PubMed2005

Maximal planar networks with large clustering coefficient and power-law degree distribution.

In this article, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called random Apollonian networks (RANs) as they can be considered as a variation of Apollonian networks. We obtain the analytic results of power-law exponent gamma=3 and clustering coefficient C= (46/3)-36 ln 3/2 approximately 0.74, which agree with the simulation results very well. We prove that the increasing tendency of average distance of RANs is a little slower than the logarithm of the number of nodes in RANs. Since most real-life networks are both scale-free and small-world networks, RANs may perform well in mimicking the reality. The RANs possess hierarchical structure as C(k) approximately k(-1) that are in accord with the observations of many real-life networks. In addition, we prove that RANs are maximal planar networks, which are of particular practicability for layout of printed circuits and so on. The percolation and epidemic spreading process are also studied and the comparisons between RANs and Barabási-Albert (BA) as well as Newman-Watts (NW) networks are shown. We find that, when the network order N (the total number of nodes) is relatively small (as N approximately 10(4)), the performance of RANs under intentional attack is not sensitive to N , while that of BA networks is much affected by N. And the diseases spread slower in RANs than BA networks in the early stage of the susceptible-infected process, indicating that the large clustering coefficient may slow the spreading velocity, especially in the outbreaks.

Journal Article↗
PubMed2005

Accelerating, hyperaccelerating, and decelerating networks.

Many growing networks possess accelerating statistics where the number of links added with each new node is an increasing function of network size so the total number of links increases faster than linearly with network size. In particular, biological networks can display a quadratic growth in regulator number with genome size even while remaining sparsely connected. These features are mutually incompatible in standard treatments of network theory which typically require that every new network node possesses at least one connection. To model sparsely connected networks, we generalize existing approaches and add each new node with a probabilistic number of links to generate either accelerating, hyperaccelerating, or even decelerating network statistics in different regimes. Under preferential attachment for example, slowly accelerating networks display stationary scale-free statistics relatively independent of network size while more rapidly accelerating networks display a transition from scale-free to exponential statistics with network growth. Such transitions explain, for instance, the evolutionary record of single-celled organisms which display strict size and complexity limits.

Journal Article↗
PubMed2004

Superfamilies of evolved and designed networks.

Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.

Animals↗
PubMed2004

Associations between the sexual behaviour of men who have sex with men and the structure and composition of their social networks.

OBJECTIVE: This exploratory study identified associations between the number of sexual partners reported by men who have sex with men (MSM) and the structure and composition of their social networks. METHODS: A cross sectional survey was conducted of men recruited as key informants, through advertising and chain referral. A face to face interview was conducted with 206 MSM. The interview included information on the number of sexual partners in the previous year and sociodemographic and behavioural characteristics of the participant. Social networks were enumerated and network size and density were calculated. Ordered logistic regression was used to assess the associations between number of sexual partners and personal and network characteristics. RESULTS: The number of anal sex partners reported was higher if the participant had injected drugs in the past year rather than never having injected (odds ratio, 95% confidence interval: 3.23, 1.28 to 8.15), decreased with network density (0.014, 0.002 to 0.008) and increased if the network did not comprise only HIV negative people (1.77, 1.05 to 2.99). The number of additional oral sex partners increased with network size (1.06, 1.02 to 1.10) and decreased with increased network density (0.034, 0.006 to 0.205). In addition to similar effects of network size (1.05, 1.01 to 1.09) and network density (0.086, 0.013 to 0.563) the model for the number of additional manual sex partners also included age (1.03, 1.01 to 1.05). CONCLUSION: The density of the social networks of MSM appears strongly and consistently associated with patterns of sexual behaviour. This underlines the importance of using network approaches to understanding the sexual behaviour of MSM and their potential value in identifying novel strategies for intervention.

Adult↗
PubMed2002

Follower neurons in lobster (Panulirus interruptus) pyloric network regulate pacemaker period in complementary ways.

Distributed neural networks (ones characterized by high levels of interconnectivity among network neurons) are not well understood. Increased insight into these systems can be obtained by perturbing network activity so as to study the functions of specific neurons not only in the network's "baseline" activity but across a range of network activities. We applied this technique to study cycle period control in the rhythmic pyloric network of the lobster, Panulirus interruptus. Pyloric rhythmicity is driven by an endogenous oscillator, the Anterior Burster (AB) neuron. Two network neurons feed back onto the pacemaker, the Lateral Pyloric (LP) neuron by inhibition and the Ventricular Dilator (VD) neuron by electrical coupling. LP and VD neuron effects on pyloric cycle period can be studied across a range of periods by altering period by injecting current into the AB neuron and functionally removing (by hyperpolarization) the LP and VD neurons from the network at each period. Within a range of pacemaker periods, the LP and VD neurons regulate period in complementary ways. LP neuron removal speeds the network and VD neuron removal slows it. Outside this range, network activity is disrupted because the LP neuron cannot follow slow periods, and the VD neuron cannot follow fast periods. These neurons thus also limit, in complementary ways, normal pyloric activity to a certain period range. These data show that follower neurons in pacemaker networks can play central roles in controlling pacemaker period and suggest that in some cases specific functions can be assigned to individual network neurons.

Animals↗
PubMed2006

Accelerating the reconstruction of genome-scale metabolic networks.

BACKGROUND: The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks. RESULTS: We have evaluated the method using Lactococcus lactis IL1403, for which a genome-scale metabolic network was published recently. We recovered most of the gene-reaction associations (i.e. 74 - 85%) which are incorporated in the published network. Moreover, we predicted over 200 additional genes to be associated to reactions, including genes with unknown function, genes for transporters and genes with specific metabolic reactions, which are good candidates for an extension to the previously published network. In a comparison of our developed method with the well-established approach Pathologic, we predicted 186 additional genes to be associated to reactions. We also predicted a relatively high number of complete conserved protein complexes, which are derived from curated metabolic networks, illustrating the potential predictive power of our method for protein complexes. CONCLUSION: We show that our methodology can be applied to accelerate the reconstruction of genome-scale metabolic networks by taking optimal advantage of existing, manually curated networks. As orthology detection is the first step in the method, only the translated open reading frames (ORFs) of a newly sequenced genome are necessary to reconstruct a metabolic network. When more manually curated metabolic networks will become available in the near future, the usefulness of our method in network prediction is likely to increase.

Bacillus subtilis↗