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At least 163 records · Page 9Linked to original sources
PubMed2003

Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model.

Slow oscillatory activity (<1 Hz) is observed in vivo in the cortex during slow-wave sleep or under anesthesia and in vitro when the bath solution is chosen to more closely mimic cerebrospinal fluid. Here we present a biophysical network model for the slow oscillations observed in vitro that reproduces the single neuron behaviors and collective network firing patterns in control as well as under pharmacological manipulations. The membrane potential of a neuron oscillates slowly (at <1 Hz) between a down state and an up state; the up state is maintained by strong recurrent excitation balanced by inhibition, and the transition to the down state is due to a slow adaptation current (Na(+)-dependent K(+) current). Consistent with in vivo data, the input resistance of a model neuron, on average, is the largest at the end of the down state and the smallest during the initial phase of the up state. An activity wave is initiated by spontaneous spike discharges in a minority of neurons, and propagates across the network at a speed of 3-8 mm/s in control and 20-50 mm/s with inhibition block. Our work suggests that long-range excitatory patchy connections contribute significantly to this wave propagation. Finally, we show with this model that various known physiological effects of neuromodulation can switch the network to tonic firing, thus simulating a transition to the waking state.

Algorithms↗
PubMed2003

Social dilemmas in a telemedicine network: experience with the implementation of the Norwegian Pathology Network.

The implementation process for the Norwegian Pathology Network (PatNet) was explored. Fourteen departments of pathology in Norwegian university and local hospitals that subscribed to PatNet were investigated. Sociological theory was applied to analyse the obstacles to implementation in terms of coordination and cooperation. Successful collective action requires that the dilemma between collective and individual interests is resolved. The establishment of PatNet has not accomplished this to a sufficient degree. The originators of PatNet expected that easy access to the network combined with comprehensive information and discussion within the network would attract users. More information and discussions were meant to be produced by users and thus attract further users. In this way the network was expected to run by itself. However, this strategy did not work. New strategies to handle the dilemma concerning collective and individual interests have not been discussed openly. The present study suggests that an open discussion among pathologists to explore and highlight this dilemma would be useful.

Computer Communication Networks↗
PubMed2000

A national laboratory network for bioterrorism: evolution from a prototype network of laboratories performing routine surveillance.

The need for an enhanced network of laboratories to respond to a bioterrorism attack has been realized. Therefore, the Association of Public Health Laboratories and the Centers for Disease Control are developing a system involving civilian public health and private laboratories that builds on the existing network for routine disease surveillance. It is anticipated that most bioterrorist attacks will not be immediately recognized, so increased laboratory capabilities and communications are necessary. The laboratory network has four categories with different biosafety levels assigned to clearly delineate the correct referral route. Improving communications through World Wide Web-based systems will allow test results, surge capacity, and training and identification algorithms to be shared instantly. There are plans to expand the network to include standard public health surveillance and emerging infectious diseases.

Biological Warfare↗
PubMed1993

Single community research networks. The HARNET experience. Harrisburg Area Research Network.

Clinical research performed in family physicians' offices is critical for building an expanded knowledge base for modern health care. Practitioners do not usually have the time, funds, or research expertise to conduct clinical studies. Organized networks can accomplish this goal. Large-area networks, composed of many separate practice sites from a wide geographic area, are valuable sources of information for describing the natural history of disease. These observational studies usually consist of data collection during clinical practice. Experimental trials include evaluations of new protocols, diagnostic tests, or therapies, often in a randomized and blinded fashion. Because of the difficulties in adhering to a standardized protocol, experimental trials are rarely undertaken in the busy clinician's office. Similarly, it may be difficult to standardize these studies in large-area networks. Smaller networks, often in a single community, can feasibly perform more complex studies. Important strategies are required to avoid loss of interest, lack of continuity, and conflict of interest.

Family Practice↗
PubMed2001

Pruned median networks: a technique for reducing the complexity of median networks.

Observations from molecular marker studies on recently diverged species indicate that substitution patterns in DNA sequences can often be complex and poorly described by tree-like bifurcating evolutionary models. These observations might result from processes of species diversification and/or processes of sequence evolution that are not tree-like. In these cases, bifurcating tree representations provide poor visualization of phylogenetic signals in sequence data. In this paper, we use median networks to study DNA sequence substitution patterns in plant nuclear and chloroplast markers. We describe how to prune median networks to obtain so called pruned median networks. These simpler networks may help to provide a useful framework for investigating the phylogenetic complexity of recently diverged taxa with hybrid origins.

Base Sequence↗
PubMed2005

Local network parameters can affect inter-network phase lags in central pattern generators.

Weakly coupled phase oscillators and strongly coupled relaxation oscillators have different mechanisms for creating stable phase lags. Many oscillations in central pattern generators combine features of each type of coupling: local networks composed of strongly coupled relaxation oscillators are weakly coupled to similar local networks. This paper analyzes the phase lags produced by this combination of mechanisms and shows how the parameters of a local network, such as the decay time of inhibition, can affect the phase lags between the local networks. The analysis is motivated by the crayfish central pattern generator used for swimming, and uses techniques from geometrical singular perturbation theory.

Animals↗
PubMed1995

Mathematic modelling of the enteric nervous network. 5. Excitation propagation in a planar neural network.

A mathematical model of the enteric nervous system (Auerbach's plexus) as a planar neural network has been developed, based on the actual morphological data of its organization. The network is composed of excitatory (cholinergic) and inhibitory (adrenergic) neurones interconnected by polysynaptic channels, formed of the geometrically non-uniform unmyelinated nerve axons. The synaptic zones are modelled as a three-compartment open pharmacokinetics system, i.e., presynaptic terminal, synaptic cleft and postsynaptic membrane where the pharmacokinetic mechanisms of electrochemical coupling are considered. All the chemical reactions of transformation of acetylcholine and adrenaline within them are described by first order Michaelis-Menten kinetics. The propagation of the electrical impulse along the pathways and in the vicinity of the nerve terminal is described by the modified Hodgkin-Huxley equations. The results of numerical simulation of the propagation of excitation within the neuronal chain, inhibitory feedback circuit, and a planar neuronal network under normal physiological conditions and after treatment with cholinergic/adrenergic agonists and antagonists are presented. The model predicts the dose-dependent influence of pharmacological agents on the neural network function.

Action Potentials↗
PubMed2004

Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data.

We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as a continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We conduct Monte Carlo experiments to examine the effectiveness of the proposed method. We also demonstrate the proposed method through the analysis of the Saccharomyces cerevisiae gene expression data.

Bayes Theorem↗
PubMed1997

Methodological limitations of the ESRD Core Indicators Project: an ESRD network's experience with implementing an ESRD quality survey. Medical Review Board of the ESRD Network of New England.

ESRD Network Number 1, composed of Maine, New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island, developed a Network Core Indicator Pilot Project using the dialysis units represented on the Medical Review Board. Network 1's Core Indicator Pilot Project aimed to (1) estimate the proportion of end-stage renal disease (ESRD) patients in Network 1 receiving hemodialysis treatments associated with a urea reduction ratio less than 60% to 65%, (2) elucidate the patient characteristics associated with a hemodialysis dose less than 65%, (3) define the processes in the delivery of hemodialysis that limit the provision of an adequate dialysis dose, and (4) initiate the routine collection of measures of dialysis dose, the analysis of those data, and feedback to the participating dialysis units. In the course of the Core Indicator Pilot Project, we observed little uniformity in the sampling method for the postdialysis blood urea nitrogen sample. Thirty-three percent of the hemodialysis units reported that this critical blood sample was drawn immediately before the dialysis treatment was terminated; 25% were obtained immediately at the end of the dialysis treatment and 42% drew the sample > or = 5 minutes after all blood was reinfused to the patient. Especially in the presence of unappreciated blood recirculation in the angioaccess or postdialysis urea rebound, the lack of standardization in obtaining this critical blood sample to support the urea reduction ratio calculation greatly compromises any comparisons of performance across dialysis facilities and may jeopardize patient care. Future ESRD quality improvement efforts must focus not only on the results of the outcome measure but also on the process by which the measure is achieved. These fundamental principles of quality assessment should be considered by policy specialists, payers, providers, and developers of clinical practice guidelines.

Adolescent↗
PubMed2000

NHLBI family blood pressure program: methodology and recruitment in the HyperGEN network. Hypertension genetic epidemiology network.

PURPOSE: Hypertension is a common precursor of serious disorders including stroke, myocardial infarction, congestive heart failure, and renal failure in whites and to a greater extent in African Americans. Large genetic-epidemiological studies of hypertension are needed to gain information that will improve future methods for diagnosis, treatment, and prevention of hypertension, a major contributor to cardiovascular morbidity and mortality. METHODS: We report successful implementation of a new structure of research collaboration involving four NHLBI "Networks," coordinated under the Family Blood Pressure Program. The Hypertension Genetic Epidemiology Network (HyperGEN) involves scientists from six universities and the NHLBI who seek to identify and characterize genes promoting hypertension. Blood samples and clinical data were projected to be collected from a sample of 2244 hypertensive siblings diagnosed before age 60 from 960 sibships (half African-American) with two or more affected persons. Nonparametric sibship linkage analysis of over one million genotype determinations (20 candidate loci and 387 anonymous marker loci) was projected to have sufficient power for detecting genetic loci promoting hypertension. For loci showing evidence for linkage in this study and for loci reported linked or associated with hypertension by other groups, genotypes are compared in hypertensive cases versus population-based controls to identify or confirm genetic variants associated with hypertension. For some of these genetic variants associated with hypertension, detailed physiological and biochemical characterization of untreated adult offspring carriers versus non-carriers may help elucidate the pathophysiological mechanisms that promote hypertension. RESULTS: The projected sample size of 2244 hypertensive participants was surpassed, as 2407 hypertensive individuals (1262 African-Americans and 1145 whites) from 917 sibships were examined. Detailed consent forms were designed to offer participants several options for DNA testing; 94% of participants gave permission for DNA testing now or in the future for any confidential medical research, with only 6% requesting restrictions for tests performed on their DNA. Since this is a family study, participants also are asked to list all first degree relatives (along with names, addresses, and phone numbers) and to indicate for each relative whether they were willing to allow study staff to make a contact. Seventy percent gave permission to contact some relatives; about 30% gave permission to contact all first degree relatives; and less than 1% asked that no relatives be contacted. Successes after the first four years of this study include: 1) productive collaboration of eight centers from six different locations; 2) early achievement of recruitment goals for study participants including African-Americans; 3) an encouraging rate of consent for DNA testing (including future testing) and relative contacting; 4) completed analyses of genetic linkage and association for several candidate gene markers and polymorphisms; 5) completed genotyping of random markers for over half of the full sample; and 6) early sharing of results among the four Family Blood Pressure Program networks for candidate and genome search analyses. CONCLUSIONS: Experience after four years of this five-year program (1995-2000) suggests that the newly initiated NHLBI Network Program mechanism is fulfilling many of the expectations for which it was designed. It may serve as a paradigm for future genetic research that can benefit from large sample sizes, frequent sharing of ideas among laboratories, and prompt independent confirmation of early findings, which are required in the search for common genes with relatively small effects such as those that predispose to human hypertension.

Adult↗
PubMed2006

Connectivity matrix method for analyses of biological networks and its application to atom-level analysis of a model network of carbohydrate metabolism.

An approach for analysis of biological networks is proposed. In this approach, named the connectivity matrix (CM) method, all the connectivities of interest are expressed in a matrix. Then, a variety of analyses are performed on GNU Octave or Matlab. Each node in the network is expressed as a row vector or numeral that carries information defining or characterising the node itself. Information about connectivity itself is also expressed as a row vector or numeral. Thus, connection of node n1 to node n2 through edge e is expressed as [n1, n2, e], a row vector formed by the combination of three row vectors or numerals, where n1, n2 and e indicate two different nodes and one connectivity, respectively. All the connectivities in any given network are expressed as a matrix, CM, each row of which corresponds to one connectivity. Using this CM method, intermetabolite atom-level connectivity is investigated in a model metabolic network composed of the reactions for glycolysis, oxidative decarboxylation of pyruvate, citric acid cycle, pentose phosphate pathway and gluconeogenesis.

Adaptation, Physiological↗
PubMed1993

Kinetoplast DNA maxicircles: networks within networks.

Kinetoplast DNA (kDNA), the mitochondrial DNA of trypanosomes, is an enormous network of interlocked minicircles and maxicircles. We selectively removed minicircles from Trypanosoma equiperdum kDNA networks by restriction enzyme cleavage. Maxicircles remained in aggregates that were resistant to protease or RNase and contained no residual minicircles, but were resolved into circular monomers by topoisomerase II. Maxicircles thus form independent catenanes within kDNA networks. Heterogeneity in the size, composition, and organization of maxicircle catenanes reflects changes that occur during kDNA replication. The rosette-like arrangement of maxicircle catenanes is distinctly different from that of minicircle catenanes. Trypanosome kDNA networks reveal unique topological complexity: they are composed of entirely dissimilar catenanes that are in turn extensively interlocked with one another.

Animals↗
PubMed2003

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Angiotensin II↗
PubMed2003

Inferring gene networks from time series microarray data using dynamic Bayesian networks.

Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria for learning network structures are introduced from a Bayesian statistical viewpoint. This paper reviews the applications of DBNs over the past years. Real data applications for Saccharomyces cerevisiae time series gene expression data are also shown.

Algorithms↗
PubMed2004

A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data.

MOTIVATION: Signaling pathways are dynamic events that take place over a given period of time. In order to identify these pathways, expression data over time are required. Dynamic Bayesian network (DBN) is an important approach for predicting the gene regulatory networks from time course expression data. However, two fundamental problems greatly reduce the effectiveness of current DBN methods. The first problem is the relatively low accuracy of prediction, and the second is the excessive computational time. RESULTS: In this paper, we present a DBN-based approach with increased accuracy and reduced computational time compared with existing DBN methods. Unlike previous methods, our approach limits potential regulators to those genes with either earlier or simultaneous expression changes (up- or down-regulation) in relation to their target genes. This allows us to limit the number of potential regulators and consequently reduce the search space. Furthermore, we use the time difference between the initial change in the expression of a given regulator gene and its potential target gene to estimate the transcriptional time lag between these two genes. This method of time lag estimation increases the accuracy of predicting gene regulatory networks. Our approach is evaluated using time-series expression data measured during the yeast cell cycle. The results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing DBN approaches.

Algorithms↗
PubMed2006

Packet traffic analysis of scale-free networks for large-scale network-on-chip design.

Recent progress in integrated circuit technologies requires precise evaluation between dynamic characteristics and topological architecture design. In this paper, we have investigated the performance evaluation of network-on-chip (NoC) architectures constructed with diverse scale-free network topologies by dynamic packet traffic simulation and theoretical network analysis. Topological differences of scale-free networks are evaluated by the degree-degree correlations that indicate topological tendency between the degree of a node and that of the nearest neighbors. Our simulation results quantitatively show that the NoC architecture constructed with the topology where hubs mostly connect to lower-degree nodes is found to achieve short latency and low packet loss ratio since it can disperse traffic load and avoid the extreme concentration of load on hubs.

Journal Article↗
PubMed2002

Reduction of high-frequency network oscillations (ripples) and pathological network discharges in hippocampal slices from connexin 36-deficient mice.

Recent evidence suggests that electrotonic coupling is an important mechanism for neuronal synchronisation in the mammalian cortex and hippocampus. Various types of network oscillations have been shown to depend on, or be sharpened by, gap junctions between inhibitory interneurones or excitatory projection cells. Here we made use of a targeted disruption of the gene coding for Cx36, a recently discovered neuronal gap junction subunit, to analyse its role in hippocampal network behaviour. Mice lacking Cx36 are viable and lack obvious morphological or behavioural abnormalities. Stimulation of afferent and efferent fibre pathways in hippocampal slices revealed a largely normal function of the synaptic circuitry, including tetanically evoked network oscillations. Spontaneous sharp waves and ripple (approximately 200 Hz) oscillations, however, occurred less frequently in slices from Cx36 -/- mice, and ripples were slightly slower than in littermate controls. Moreover, epileptiform discharges elicited by 4-aminopyridine were attenuated in slices from Cx36 -/- mice. Our findings indicate that Cx36 plays a role in the generation of certain forms of network synchronisation in the hippocampus, namely sharp wave-ripple complexes and hypersynchronous epileptiform discharges.

4-Aminopyridine↗
PubMed2005

Bayesian network analysis of signaling networks: a primer.

High-throughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We discuss ways to automatically derive a Bayesian network model from proteomic data and to interpret the resulting model.

Algorithms↗