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PubMed2005

Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and a duplication-mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.

Algorithms↗
PubMed2002

Microrheometry of semiflexible actin networks through enforced single-filament reptation: frictional coupling and heterogeneities in entangled networks.

Magnetic tweezers are applied to study the enforced motion of single actin filaments in entangled actin networks to gain insight into friction-mediated entanglement in semiflexible macromolecular networks. Magnetic beads are coupled to one chain end of test filaments, which are pulled by 5 to 20 pN force pulses through entangled solutions of nonlabeled actin, the test filaments thus acting as linear force probes of the network. The transient filament motion is analyzed by microfluorescence, and the deflection-versus-time curves of the beads are evaluated in terms of a mechanical equivalent circuit to determine viscoelastic parameters, which are then interpreted in terms of viscoelastic moduli of the network. We demonstrate that the frictional coefficient characterizing the hydrodynamic coupling of the filaments to the surrounding network is much higher than predicted by the tube model, suggesting that friction-mediated interfilament coupling plays an important role in the entanglement of non-cross-linked actin networks. Furthermore, the local tube width along the filament contour (measured in terms of the root-mean-square displacement characterizing the lateral Brownian motion of the test filament) reveals strong fluctuations that can lead to transient local pinching of filaments.

Actin Cytoskeleton↗
PubMed2000

Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function.

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology. Several studies have been done using the Boolean network as a model of a genetic network. This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mnD+1) time naive algorithm and a simple O (mnD) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum in degree. This paper presents an improved O(momega-2nD + mnD+omega-3) time Monte-Carlo type randomized algorithm, where omega is the exponent of matrix multiplication (currently, omega < 2.376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is nontrivial and the technique can be applied to several related problems.

Algorithms↗
PubMed2003

Genetic Network Analyzer: qualitative simulation of genetic regulatory networks.

MOTIVATION: The study of genetic regulatory networks has received a major impetus from the recent development of experimental techniques allowing the measurement of patterns of gene expression in a massively parallel way. This experimental progress calls for the development of appropriate computer tools for the modeling and simulation of gene regulation processes. RESULTS: We present Genetic Network Analyzer (GNA), a computer tool for the modeling and simulation of genetic regulatory networks. The tool is based on a qualitative simulation method that employs coarse-grained models of regulatory networks. The use of GNA is illustrated by a case study of the network of genes and interactions regulating the initiation of sporulation in Bacillus subtilis. AVAILABILITY: GNA and the model of the sporulation network are available at http://www-helix.inrialpes.fr/gna.

Bacillus subtilis↗
PubMed2004

Advances to Bayesian network inference for generating causal networks from observational biological data.

MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/

Algorithms↗
PubMed2005

Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels).

MOTIVATION: The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this paper, we introduce the method of Belief (Bayesian) networks to the domain of genotype-to-phenotype analyses and provide an example application. RESULTS: A Belief network is a graphical model of a probabilistic nature that represents a joint multivariate probability distribution and reflects conditional independences between variables. Given the data, optimal network topology can be estimated with the assistance of heuristic search algorithms and scoring criteria. Statistical significance of edge strengths can be evaluated using Bayesian methods and bootstrapping. As an example application, the method of Belief networks was applied to 20 SNPs in the apolipoprotein (apo) E gene and plasma apoE levels in a sample of 702 individuals from Jackson, MS. Plasma apoE level was the primary target variable. These analyses indicate that the edge between SNP 4075, coding for the well-known epsilon2 allele, and plasma apoE level was strong. Belief networks can effectively describe complex uncertain processes and can both learn from data and incorporate prior knowledge. AVAILABILITY: Various alternative and supplemental networks (not given in the text) as well as source code extensions, are available from the authors. SUPPLEMENTARY INFORMATION: http://bioinformatics.oxfordjournals.org.

Apolipoproteins E↗
PubMed2003

Patterns in randomly evolving networks: idiotypic networks.

We present a model for the evolution of networks of occupied sites on undirected regular graphs. At every iteration step in a parallel update, I randomly chosen empty sites are occupied and occupied sites having occupied neighbor degree outside of a given interval (t(l),t(u)) are set empty. Depending on the influx I and the values of both lower threshold and upper threshold of the occupied neighbor degree, different kinds of behavior can be observed. In certain regimes stable long-living patterns appear. We distinguish two types of patterns: static patterns arising on graphs with low connectivity and dynamic patterns found on high connectivity graphs. Increasing I patterns become unstable and transitions between almost stable patterns, interrupted by disordered phases, occur. For still larger I the lifetime of occupied sites becomes very small and network structures are dominated by randomness. We develop methods to analyze the nature and dynamics of these network patterns, give a statistical description of defects and fluctuations around them, and elucidate the transitions between different patterns. Results and methods presented can be applied to a variety of problems in different fields and a broad class of graphs. Aiming chiefly at the modeling of functional networks of interacting antibodies and B cells of the immune system (idiotypic networks), we focus on a class of graphs constructed by bit chains. The biological relevance of the patterns and possible operational modes of idiotypic networks are discussed.

Journal Article↗
PubMed1995

The biological reality of the interlacunar network in the embryonic, cartilaginous, skeleton: a thiazine dye/absolute ethanol/LR White resin protocol for visualizing the network with minimal tissue shrinkage.

Third toe phalanges of chicks aged 8-13 days in ovo and 7-day post-natal rat femoral growth plate were examined to determine whether the interlacunar network (IN), a structure with no lipoprotein membrane component or cytoplasmic organelles, is a genuine component of young growth cartilage. In chick phalanges dehydrated by 70% (v/v) ethanol and LR White resin, variable metachromatic staining of the interlacunar network by toluidine blue and red staining by picro-Sirius red indicate the presence of glycosaminoglycans and collagen. The network in phalanges dehydrated by 80% (v/v) ethanol appears little different; however, the network is much less widely detectable in phalanges dehydrated by 90% (v/v) ethanol and, after dehydration by absolute ethanol, is almost completely undetectable. In contrast, when the young cartilage is permeated by a thiazine dye such as toluidine blue, using a solution of dye in the aldehyde fixative, the network is widely detectable, following dehydration by absolute ethanol, both in chick phalanges and in rat growth plate. Comparison of projected areas shows that the extent to which whole chick feet are found to have shrunk, by the time that they are photographed under LR White resin, is determined principally by the extent of dehydration, by 70% (v/v) or absolute ethanol; post-shrinkage areas are 33% or 35% of areas measured in buffer for 70% (v/v) ethanol/LR White resin and 71% or 75% for absolute ethanol/LR White resin (the higher value in each is for the toluidine blue treatment). The network is thus present in radically shrunk tissue, but, significantly, is also fully represented in tissue shrunk by only a conventional margin and is therefore not produced as an artefact by exceptional tissue shrinkage as has been suggested.

Animals↗
PubMed1999

Identification of genetic networks from a small number of gene expression patterns under the Boolean network model.

Liang, Fuhrman and Somogyi (PSB98, 18-29, 1998) have described an algorithm for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Their results of computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. This paper gives a mathematical proof for their observation. Precisely, this paper devises a much simpler algorithm for the same problem and proves that, if the indegree of each node (i.e., the number of input nodes to each node) is bounded by a constant, only O(log n) state transition pairs (from 2n pairs) are necessary and sufficient to identify the original Boolean network of n nodes correctly with high probability. We made computational experiments in order to expose the constant factor involved in O(log n) notation. The computational results show that the Boolean network of size 100,000 can be identified by our algorithm from about 100 INPUT/OUTPUT pairs if the maximum indegree is bounded by 2. It is also a merit of our algorithm that the algorithm is conceptually so simple that it is extensible for more realistic network models.

Algorithms↗
PubMed1992

A computer-based model for realistic simulations of neural networks. II. The segmental network generating locomotor rhythmicity in the lamprey.

1. To analyze the function of the spinal interneuronal network generating locomotion in the lamprey CNS, a vertebrate model system, we performed computer simulations with realistic model neurons possessing the essential properties of their biological counterparts. 2. The segmental network has been simulated by modeling experimentally established types of neurons with their specific membrane properties and synaptic interconnections. Fictive locomotor activity, which can be experimentally induced by elevating the background excitability by bath application of excitatory amino acids, was simulated by opening membrane conductances for kainate/alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) or N-methyl-D-aspartate (NMDA) receptors. Kainate/AMPA receptor activation induced a rhythm in the middle and upper part of the physiological burst frequency range, whereas NMDA receptor activation evoked bursting in the lower part of the range, which corresponds well to earlier experimental findings. 3. Several factors contributing to the termination of the burst were studied and their interaction was assessed in simulations of the network. 1) The summation of spike afterhyperpolarizations (late AHPs), leading to adaptation of the discharge, acts as a primary burst-terminating factor at lower rates of kainate/AMPA-induced bursting, and it also interacts with the NMDA-induced oscillatory membrane properties during slow rhythmicity. 2) The termination of the depolarized NMDA plateau is another important factor during NMDA-evoked rhythmicity. 3) The synaptic inhibition from lateral interneurons to the interneurons mediating reciprocal inhibition is important at higher rates of kainate/AMPA-induced bursting. 4. The mechanism of action of 5-hydroxytryptamine (5-HT) on the lamprey segmental network was further investigated by simulation. 5-HT is known to lower the burst frequency during fictive locomotion and also to decrease the conductance through the Ca(2+)-dependent K+ channels, and thereby the size of the late AHP that follows the action potential. Decreasing this conductance in the network simulations resulted in a lesser amount of AHP summation and thereby less frequency adaptation during the burst, longer bursts, and a lower locomotor frequency. Thus the selective action of 5-HT on the Ca(2+)-dependent K+ channels, and hence on the AHP, can account for the modulatory effect on the fictive locomotor rhythm seen experimentally. 5. The results demonstrate that the present simulation of the segmental network can account for essential features of the motor pattern seen experimentally during lamprey locomotion.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed2000

[The Hepatitis Network in Maine-et-Loire city hospitals: six year follow-up of patients and physicians. Hepatitis Network of Maine-et-Loire].

OBJECTIVES: To evaluate 6 years of a city-hospital hepatitis network. The network was set up in 3 steps: 1988: intrahospital network, 1991: city-hospital network, 1997: compliance with government regulations. METHODS: The whole activity from 1991 to 1997 was evaluated and special attention was paid to patient files and participating physicians. RESULTS: From June 1991 to December 1997 (6.5 years), 759 patient files were registered which corresponds to 531 patients (male 57%) with a mean age of 44 +/- 16 years (+/- standard deviation). Four hundred and twenty one patients (79%) had hepatitis C, 95 (18%) hepatitis B and 15 (3%) co-infection; 83% of patients had had a liver biopsy confirming cirrhosis in 21.5%. The annual number of files registered increased continuously. This was more a result of recruiting known patients than new patients, after the network had been in place for several years, mainly with hepatitis B virus (known patients in 1997: hepatitis B virus: 53% vs 33% for hepatitis C virus, P<0.05). Treatment protocols (73%) were more frequent for hepatitis C virus patients than for hepatitis B (73% vs 59%, P<0.01). Therapeutic trial proposals (37%) increased from 21% in 1991 to 59% in 1997, P<0.01. Participation in monthly meetings by academic hepato-gastroenterologists increased slightly while that of regional hospital hepato-gastroenterologists increased markedly and that of private hepato-gastroenterologists remained stable. The annual proportion of files submitted by academic hepato-gastroenterologists decreased in parallel to the increase in submission of patient files by other hepato-gastroenterologists. CONCLUSIONS: During 6 years of activity, the network grew with an increase in the annual number of patient files, growing participation in therapeutic trials as well as in monthly meetings by practitioners.

Adult↗
PubMed2005

Simulations of simple artificial genetic networks reveal features in the use of Relevance Networks.

Recent research on large scale microarray analysis has explored the use of Relevance Networks to find networks of genes that are associated to each other in gene expression data. In this work, we compare Relevance Networks with other types of clustering methods to test some of the stated advantages of this method. The dataset we used consists of artificial time series of Boolean gene expression values, with the aim of mimicking microarray data, generated from simple artificial genetic networks. By using this dataset, we could not confirm that Relevance Networks based on mutual information perform better than Relevance Networks based on Pearson correlation, partitional clustering or hierarchical clustering, since the results from all methods were very similar. However, all three methods successfully revealed the subsets of co-expressed genes, which is a valuable step in identifying co-regulation.

Cluster Analysis↗
PubMed1993

The Vermont-Oxford Trials Network: very low birth weight outcomes for 1990. Investigators of the Vermont-Oxford Trials Network Database Project.

This report describes the Vermont-Oxford Trials Network, a voluntary collaborative research network, and summarizes the outcomes and medical interventions for very low birth weight infants at participating centers in 1990. The Vermont-Oxford Trials Network included 36 centers in 1990 (11% university hospitals, 44% university affiliates, 44% nonaffiliated) with a total of 2961 infants weighing 501 to 1500 g (median 73 infants, range 5 to 172). Eighty percent of the infants were inborn and 65% were white. The overall network frequencies for selected interventions and outcomes were as follows: prenatal care, 90%; a complete course of antenatal corticosteroids, 12%; cesarean section, 56%; surfactant therapy, 49%; postnatal steroids for chronic lung disease, 16%; high-frequency ventilation, 4%; patent ductus arteriosus, 31%; necrotizing enterocolitis, 6%; bacterial sepsis, 16%; and intraventricular hemorrhage, 26%. By 28 days, 15% of the infants had died and 8% had been transferred, whereas by discharge 18% had died and 18% had been transferred. There were marked variations among the centers in the frequencies of different medical interventions and in the frequencies of various clinical outcomes. The Vermont-Oxford Trials Network is a unique collaborative research group composed of a broad range of neonatal intensive care units. During 1990 there were considerable differences among the centers in the interventions used and patient outcomes observed. The investigators plan to devote the resources of the Network to a research program of randomized trials and outcome studies so that effective interventions can be identified and the quality of neonatal intensive care can be continuously improved.

Clinical Medicine↗
PubMed2001

Computer networks as social networks.

Computer networks are inherently social networks, linking people, organizations, and knowledge. They are social institutions that should not be studied in isolation but as integrated into everyday lives. The proliferation of computer networks has facilitated a deemphasis on group solidarities at work and in the community and afforded a turn to networked societies that are loosely bounded and sparsely knit. The Internet increases people's social capital, increasing contact with friends and relatives who live nearby and far away. New tools must be developed to help people navigate and find knowledge in complex, fragmented, networked societies.

Community Networks↗
PubMed2000

The RETAIN project: DICOM teleradiology over an ATM-based network. Radiological Examinations Transfer on an ATM Integrated Network.

The RETAIN project (Radiological Examinations Transfer on an ATM Integrated Network) has aimed at testing videoconferencing and DICOM image transfers to get advice about difficult radiological cases over an asynchronous transfer mode (ATM)-based network, which affords a more comfortable interface than narrow-band networks and allows exchange of complete image series using the DICOM format of studies. For this purpose, an experimental ATM network was applied between six university hospitals in four different countries. An assessment of the functionalities of the system was performed by means of log-file analysis, video recording of the sessions and forms filled out by the participants at the end of each session. Questionnaires were answered by the users at the end of the project to bring out perspectives of utilisation and added value. We discussed 43 cases during 20 sessions. For technical or organisational problems, only 20 of the 36 planned sessions took place. The throughput over ATM (10.5 Mbit/s, 20 times faster than six ISDN B-channels) was adequate. Despite the experimental configuration of the network, the system was considered as satisfactory by all the physicians. In 72 % of the sessions, the expected result (answer to the question) was gained. By common consent, videoconferencing was unanimously regarded as a prominent tool in improving the interaction quality. Asynchronous transfer mode is an efficient method for fast transferring of radiologic examinations in DICOM format and for discussing them through high-quality videoconferencing.

Computer Communication Networks↗
PubMedDate not supplied

Broadcast scheduling in wireless multihop networks using a neural-network-based hybrid algorithm.

In wireless multihop networks, the objective of the broadcast scheduling problem is to find a conflict free transmission schedule for each node at different time slots in a fixed length time cycle, called TDMA cycle. The optimization criterion is to find an optimal TDMA schedule with minimal TDMA cycle length and maximal node transmissions. In this paper we propose a two-stage hybrid method to solve this broadcast scheduling problem in wireless multihop networks. In the first stage, we use a sequential vertex-coloring algorithm to obtain a minimal TDMA frame length. In the second stage, we apply the noisy chaotic neural network to find the maximum node transmission based on the results obtained in the previous stage. Simulation results show that this hybrid method outperforms previous approaches, such as mean field annealing, a hybrid of the Hopfield neural network and genetic algorithms, the sequential vertex coloring algorithm, and the gradual neural network.

Algorithms↗
PubMed2006

What sort of networks are public health networks?

BACKGROUND: Re-organization of the English National Health Service (NHS) has fragmented the public health workforce, relocating teams from about 100 health authorities into over 300 primary care trusts (PCTs). The UK Government announced the setting up of public health networks (PHNs) as a solution to the problems created by fragmentation. METHODS: Fifty-seven semi-structured telephone interviews were held with key players in PHNs in all strategic health authority areas in England in early 2003. RESULTS: PHNs appeared to be primarily networks of public health professionals rather than of organizations. Informants were unsure about PCTs' commitment to public health. Predominantly, members were those NHS personnel with a clear and explicit public health role. Most PHNs intended to include others later (e.g. health visitors, environmental health officers), although a few thought that inclusivity was essential from the start. Continuing professional development for public health personnel dominated the work being undertaken, with some collaborative work across PCTs. PHNs were seen as a compulsory reconfiguration of existing networks, and informants doubted that they were appropriate for the many levels of networking that public health work requires. CONCLUSION: The formation of PHNs does not appear to have been either necessary or sufficient. However, the public health community has a well-established tradition of networking, and therefore has the skills to use PHNs advantageously.

Community Networks↗
PubMedDate not supplied

QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks.

The Probabilistic Neural Network (PNN) and its close relative, the Generalized Regression Neural Network (GRNN), are presented as simple yet powerful neural network techniques for use in Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) studies. The PNN methodology is applicable to classification problems, and the GRNN is applicable to continuous function mapping problems. The basic underlying theory behind these probability-based methods is presented along with two applications of the PNN/GRNN methodology. The PNN model presented identifies molecules as potential soluble epoxide hydrolase inhibitors using a binary classification scheme. The GRNN model presented predicts the aqueous solubility of nitrogen- and oxygen-containing small organic molecules. For each application, the network inputs consist of a small set of descriptors that encode structural features at the molecular level. Each of these studies has also been previously addressed in this research group using more traditional techniques such as k-nearest neighbor classification, multiple linear regression, and multilayer feed-forward neural networks. In each case, the predictive power of the PNN and GRNN models was found to be comparable to that of the more traditional techniques but requiring significantly fewer input descriptors.

Bayes Theorem↗