Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Networks”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 847 records · Page 47Linked to original sources
PubMed2006

Prevention of HIV and sexually transmitted diseases in high risk social networks of young Roma (Gypsy) men in Bulgaria: randomised controlled trial.

OBJECTIVE: To determine the effects of a behavioural intervention for prevention of HIV and sexually transmitted diseases that identified, trained, and engaged leaders of Roma (Gypsy) men's social networks to counsel their own network members. DESIGN: A two arm randomised controlled trial. SETTING: A disadvantaged, impoverished Roma settlement in Bulgaria. PARTICIPANTS: 286 Roma men from 52 social networks recruited in the community. INTERVENTION: At baseline all participants were assessed for HIV risk behaviour, tested and treated for sexually transmitted diseases, counselled in risk reduction, and randomised to intervention or control groups. Network leaders learnt how to counsel their social network members on risk prevention. Networks were followed up three and 12 months after the intervention to determine evidence of risk reduction. MAIN OUTCOME MEASURE: Occurrence of unprotected intercourse during the three months before each assessment. RESULTS: Reported prevalence of unprotected intercourse in the intervention group fell more than in control group (from 81% and 80%, respectively, at baseline to 65% and 75% at three months and 71% and 86% at 12 months). Changes were more pronounced among men with casual partners. Effects remained strong at long term follow-up, consistent with changes in risk reduction norms in the social network. Other measures of risk reduction corroborated the intervention's effects. CONCLUSIONS: Endorsement and advice on HIV prevention from the leader of a social network produces well maintained change in the reported sexual practices in members of that network. This model has particular relevance for health interventions in populations such as Roma who may be distrustful of outsiders. TRIAL REGISTRATION: Clinical Trials NCT00310973.

Adult↗
PubMed2025

Glaucoma and brain functional networks: a bidirectional Mendelian randomisation study.

OBJECTIVE: Glaucoma is a complex neurodegenerative ocular disorder accompanied by brain functional abnormalities that extend beyond the visual system. However, the causal association between the two remains unclear at present. This study aimed to investigate the potential causal relationships between glaucoma and brain functional networks in order to provide novel insights into the neuropathic mechanism of glaucoma. METHODS AND ANALYSIS: Based on the genome-wide association studies data of glaucoma and resting-state functional MRI (Rs-fMRI), a bidirectional Mendelian randomisation (MR) analysis was conducted between glaucoma and brain functional networks. Inverse variance weighting was applied as the primary method to estimate causality with false discovery rate correction. Additional sensitivity analyses were conducted to evaluate the robustness of the results. RESULTS: Forward MR analysis suggested that glaucoma was causally associated with two brain networks between the subcortical cerebellum and the attention or visual network (p=0.022), as well as the default mode and central executive network (p=0.008), but without significance after false discovery rate correction (q>0.1). Reverse MR analysis revealed 19 Rs-fMRI traits related to glaucoma risk, including the salience or central executive network in the frontal region (p=0.0005, q=0.08) and the motor network (p=0.0009, q=0.08) with significant causality. CONCLUSIONS: This MR study revealed potentially causal relationships between glaucoma and brain functional networks. Especially, the functional connectivity of the motor network between the postcentral or precentral areas may potentially lead to increased risk of glaucoma.

Humans↗
PubMed2006

Tagging and tracking individual networks within a complex mitochondrial web with photoactivatable GFP.

Assembly of mitochondria into networks supports fuel metabolism and calcium transport and is involved in the cellular response to apoptotic stimuli. A mitochondrial network is defined as a continuous matrix lumen whose boundaries limit molecular diffusion. Observation of individual networks has proven challenging in live cells that possess dense populations of mitochondria. Investigation into the electrical and morphological properties of mitochondrial networks has therefore not yielded consistent conclusions. In this study we used matrix-targeted, photoactivatable green fluorescent protein to tag single mitochondrial networks. This approach, coupled with real-time monitoring of mitochondrial membrane potential, permitted the examination of matrix lumen continuity and fusion and fission events over time. We found that adjacent and intertwined mitochondrial structures often represent a collection of distinct networks. We additionally found that all areas of a single network are invariably equipotential, suggesting that a heterogeneous pattern of membrane potential within a cell's mitochondria represents differences between discrete networks. Interestingly, fission events frequently occurred without any gross morphological changes and particularly without fragmentation. These events, which are invisible under standard confocal microscopy, redefine the mitochondrial network boundaries and result in electrically disconnected daughter units.

Animals↗
PubMed1987

Quantitative analysis of arteriolar network architecture in cat sartorius muscle.

The geometry of the arteriolar network is one of the major determinants of blood flow distribution within a tissue. The purpose of this study was to describe the distribution of geometrical variables (lengths, diameters) as well as the pattern of branching in the nonarcading portion of the arteriolar network in skeletal muscle. The exteriorized cat sartorius muscle was used as the experimental model. The intravascular fluorescence of fluorescein isothiocyanate (FITC)-labeled Dextran 150 was observed with a low-light-level video camera, and the vascular networks were mapped. Arteriolar lengths and diameters were measured, and vessel position in the network was characterized by Strahler's method of ordering, in which the first-order arterioles give rise to most of the capillaries. Typically, the nonarcading, terminal networks contain three or four arteriolar orders. The sequences of the number of vessels, mean diameter, and mean length for each order are accurately described by geometric progressions (Horton's law). The distribution of diameters within each order was rather narrow: typically two-thirds of the vessels fell within 20% of the mean value. The spread was reduced by half when vessels within a single network were considered. During vasodilation to a standard stimulus the relative dispersion of diameters increased modestly. The distribution of vessel lengths was broader than for diameters. Two-thirds of vessels of a single order fell within 50-75% of the mean. The spread was less within individual networks. The variability of vessel geometry and branching patterns was substantially less within a single network than for a population drawn from a group of networks.

Anatomy↗
PubMed2002

Period differences between segmental oscillators produce intersegmental phase differences in the leech heartbeat timing network.

Considerable experimental and theoretical effort has been exerted to understand how constant intersegmental phase relationships are produced between oscillators in segmentally organized pattern generating networks. The phase relationship between the segmental oscillators in the isolated timing network of the leech heartbeat central pattern generator is quite regular within individual preparations. However, it varies considerably among different preparations. Our goal is to determine how the phase relationships in this network are established. Here we assess whether inherent period differences, as suggested by the excitability-gradient hypothesis, play a role in establishing the phase relationships between the two coupled segmental oscillators of the leech heartbeat timing network. To do this we developed methods for reversibly uncoupling the segmental oscillators (sucrose knife) and pharmacological manipulation of the individual oscillators (split bath). Differences in inherent cycle periods between the third and fourth segmental oscillators (G3 and G4) were present in most (20 of 26) preparations. These period differences correlated with the phase differences observed between the segmental oscillators in the recoupled timing network, such that the oscillator with the faster cycle period, regardless of the segment in which it was located, led in phase in proportion to its period difference with the other oscillator. The phase differences between the original (coupled) and recoupled states of individual preparations were similar. Thus application and removal of the sucrose knife did not alter the period difference between the segmental oscillators in the timing network. Pharmacological manipulation of the uncoupled oscillators to alter the period difference between the oscillators led to similar correlated phase differences in the recoupled timing network. Across all experiments the uncoupled segmental oscillator with the faster cycle period established the cycle period of the timing network when recoupled. In conclusion, our findings indicate that an excitability-gradient plays a role in establishing the phase relationship between the segmental oscillators of the leech heartbeat central pattern generator since inherent period differences present between the oscillators are correlated to the phase relationships of the coupled/recoupled timing network.

Animals↗
PubMed2004

Using heterogeneity to predict inhibitory network model characteristics.

From modeling studies it has been known for >10 years that purely inhibitory networks can produce synchronous output given appropriate balances of intrinsic and synaptic parameters. Several experimental studies indicate that synchronous activity produced by inhibitory networks is critical to the production of population rhythms associated with various behavioral states. Heterogeneity of inputs to inhibitory networks strongly affect their ability to synchronize. In this paper, we explore how the amount of input heterogeneity to two-cell inhibitory networks affects their dynamics. Using numerical simulations and bifurcation analyses, we find that the ability of inhibitory networks to synchronize in the face of heterogeneity depends nonmonotonically on each of the synaptic time constant, synaptic conductance and external drive parameters. Because of this, an optimal set of parameters for a given cellular model with various biophysical characteristics can be determined. We suggest that this could be a helpful approach to use in determining the importance of different, underlying biophysical details. We further find that two-cell coherence properties are maintained in larger 10-cell networks. As such, we think that a strategy of "embedding" small network dynamics in larger networks is a useful way to understand the contribution of biophysically derived parameters to population dynamics in large networks.

Biological Clocks↗
PubMed1990

Sensory input induces long-lasting changes in the output of the lobster pyloric network.

1. A long-lasting restructuring of the pyloric neural network of the lobster stomatogastric nervous system (STS) by a multisynaptic sensory afferent is described. This restructuring can be obtained either by mechanical stimulation of the pyloric region of the stomach or by brief high-frequency electrical stimulation of a nerve that innervates this region, the lateral posterolateral nerve (lpln). Electron microscopy shows that this nerve contains several thousand very small fibers (approximately 0.3 microns diam), the activation of some subset of which is responsible for the effects of lpln stimulation. 2. These stimulation paradigms result in both short-duration changes in pyloric activity and modulatory effects long outlasting the stimulus end. The long-lasting changes include the cessation of rhythmic ventricular dilator (VD) and lateral pyloric (LP) neuron activity, and thus result in a reduced pyloric pattern in which only the pyloric dilator (PD), inferior cardiac (IC), anterior burster (AB), and pyloric (PY) neurons are active. 3. Tonic low-frequency lpln stimulation, alternatively, results in the VD neuron rhythmically firing long spike bursts with a cycle frequency much slower than that of the pyloric network while an otherwise complete pyloric pattern continues. In this new bursting pattern the VD neuron fires exclusively with another STS neural network, the cardiac sac (CS) network, and thus functionally "switches" from the pyloric to the CS network. This switch of the VD neuron from the pyloric to the CS network also occurs when the CS network is spontaneously active. 4. Our results thus demonstrate that sensory input can provoke a long-lasting modification of the functional configuration of a rhythmic neural network. They further extend the concept of flexibility in nervous systems by showing that individual neurons can belong to more than one neural network, "switching" from one to another in response to sensory input or spontaneous central nervous activity.

Animals↗
PubMed2002

Differential effects of serotonin enhance activity of an electrically coupled neural network.

Networks of electrically coupled neurons play an important role in coordinating activity among widely distributed neurons in the CNS. Such networks are sensitive to neuromodulation; but how modulation of individual cells affects activity of the entire network is not well understood. In the CNS of the medicinal leech, the S interneuron (S-cell) forms a network of electrically coupled neurons where each S-cell is linked to its two neighboring S-cells by electrical synapses. An action potential initiated in one cell is carried the length of the animal along this S-cell chain. The S-cell network is of interest because it is crucial for sensitization and dishabituation of the whole-body shortening reflex, although it is not necessary for reflexive shortening itself. Mechanosensory stimuli that produce shortening will directly elicit a train of action potentials by the S-cell network. This activity reflects the sum of action potential initiations in several S interneurons within the chain. The activity was enhanced by serotonin (5HT) in terms of both the total number of action potentials initiated and the average frequency of these initiations. Increases in evoked activity were accompanied by differential changes in the rates of action potential initiation in individual S-cells. 5HT only weakly enhanced initiations in S-cells that made a large contribution to the network-level response, while initiations in other, less active, S-cells were strongly enhanced by 5HT. This neurotransmitter also modulated the pattern of how activity was distributed throughout the network. 5HT-induced changes in activity patterns of the S-cell network may represent an important component of learning-related neuroplasticity in the leech shortening reflex.

Action Potentials↗
PubMedDate not supplied

The fractal nature of myocardial blood flow emerges from a whole-organ model of arterial network.

Mammalian hearts exhibit a heterogeneous spatial distribution of blood flows, but flows in near-neighbor regions correlate strongly. Also, tracer (15)O-water washout after injection into the inflow shows a straight log-log relationship between outflow concentration and time. To uncover the role of the arterial network in governing these phenomena, morphometric data were used to construct a mathematical model of the coronary arterial network of the pig heart. The model arterial network, built in a simplified three-dimensional representation of tissue geometry, satisfies the statistical morphometric data on segment lengths, diameters and connectivities reported for real arterial networks. The model uses an avoidance algorithm to position successive vascular segments in the network. Assuming flows through the network to be steady, the calculated regional flow distributions showed (1) the degree of heterogeneity observed in normal hearts; (2) spatial self-similarity in local flows; (3) fractal spatial correlations, all with the same fractal dimension found in animal studies; (4) pressure distributions along the model arterial network comparable to those observed in nature, with maximal resistances in small vessels. In addition, the washout of intravascular tracer showed tails with power law slopes that fitted h(t) = at(-alpha-1) with the exponents alpha = 2 for the reconstructed networks compared with those from experimental outflow concentration-time curves with alpha = 2.1+/-0.3. Thus, we concluded that the fractal nature of spatial flow distribution in the heart, and of temporal intravascular washout, are explicable in terms of the morphometry of the coronary network.

Animals↗
PubMed2001

Application of classification tree and neural network algorithms to the identification of serological liver marker profiles for the diagnosis of hepatocellular carcinoma.

OBJECTIVE: Although many attempts have been made to identify tumour-specific alpha-fetoprotein (AFP) glycoforms or other serological markers for the diagnosis of hepatocellular carcinoma (HCC), none of the available markers has, so far, shown satisfactory sensitivity and specificity. Here we aimed to apply classification tree and neural network algorithms to interpret the levels of multiple serological liver markers to improve overall specificity and sensitivity, particularly with a view to discriminating between liver cirrhosis with and without HCC. METHODS: We developed classification trees and neural networks that identified serological liver marker profiles comprising AFP, alpha1-antitrypsin (A1AT), alpha2-macroglobulin (A2MG), thyroxine-binding globulin (TBG), transferrin and albumin as well as sex and age, which might permit the diagnosis of HCC. Data were collected from 65 HCC patients, 51 patients with liver cirrhosis alone (LC) and 51 normal healthy subjects. RESULTS: The generated classification trees and neural networks showed similar diagnostic values in differentiating HCC from LC. The classification trees identified AFP, A1AT and albumin as the most important classification parameters, whereas the neural networks identified A2MG, AFP, A1AT and albumin as the predominant factors. The classification logic of the classification trees indicated that more HCC cases could be identified among cases with slightly elevated AFP levels by using the serum levels of A1AT and albumin. The neural networks were also useful for the identification of the HCC cases when the AFP levels were below 500 ng/ml (p < 0.005). The neural networks could identify HCC cases with AFP levels within the normal range, but the classification trees could not. By combining the conventional AFP test and the neural networks, the overall diagnostic sensitivity for HCC was significantly increased from 60.0 to 73.8% (p < 0.05) while maintaining a high specificity (88.2%). The sensitivities for tumors of different sizes were similar. CONCLUSION: The neural network algorithm appeared to be more powerful than the classification tree algorithm in the identification of the distinctive serological liver marker profiles for the diagnosis of the HCC subgroup without significant elevation in serum AFP levels. By incorporating serological levels of other liver markers and including data from a large number of patients and control subjects, it should prove possible to develop a versatile neural network for early diagnosis of HCC.

Adult↗
PubMed1994

Heterogeneity of red blood cell perfusion in capillary networks supplied by a single arteriole in resting skeletal muscle.

Flow heterogeneity within capillary beds may have two sources: (1) unequal distribution of red blood cell (RBC) supply among arterioles and (2) unique properties of RBC flow in branching networks of capillaries. Our aim was to investigate the capillary network as a source of both spatial and temporal heterogeneity of RBC flow. Five networks, each supplied by a single arteriole, were studied in frog sartorius muscle (one network per frog) by intravital video microscopy. Simultaneous data on RBC velocity (millimeters per second), lineal density (RBCs per millimeter), and supply rate (RBCs per second) were measured continuously (10 samples per second) from video recordings in 5 to 10 capillary segments per network for 10 minutes by use of automated computer analysis. To quantify heterogeneity, mean values from successive 10-second intervals were tabulated for each flow parameter in each capillary segment (ie, portion of capillary between successive bifurcations), and percent coefficient of variation (SD/mean.100%) was calculated for (1) spatial heterogeneity among vessels (CVs) every 10 seconds and for the entire 10-minute sample and (2) temporal heterogeneity within vessels for every capillary segment and for the mean flow parameter. Analysis of these data indicates that (1) capillary networks are a significant source of both spatial and temporal flow heterogeneity, and (2) continuous redistributions of flow occur within networks, resulting in substantial temporal changes in CVs, although a persistent spatial heterogeneity of perfusion still exists on a 10-minute basis. In most networks, CVs decreased as supply rate within the network increased, thus indicating that rheology plays a significant role in determining the perfusion heterogeneity.

Animals↗
PubMed2003

Permitted and forbidden sets in symmetric threshold-linear networks.

The richness and complexity of recurrent cortical circuits is an inexhaustible source of inspiration for thinking about high-level biological computation. In past theoretical studies, constraints on the synaptic connection patterns of threshold-linear networks were found that guaranteed bounded network dynamics, convergence to attractive fixed points, and multistability, all fundamental aspects of cortical information processing. However, these conditions were only sufficient, and it remained unclear which were the minimal (necessary) conditions for convergence and multistability. We show that symmetric threshold-linear networks converge to a set of attractive fixed points if and only if the network matrix is copositive. Furthermore, the set of attractive fixed points is nonconnected (the network is multiattractive) if and only if the network matrix is not positive semidefinite. There are permitted sets of neurons that can be coactive at a stable steady state and forbidden sets that cannot. Permitted sets are clustered in the sense that subsets of permitted sets are permitted and supersets of forbidden sets are forbidden. By viewing permitted sets as memories stored in the synaptic connections, we provide a formulation of long-term memory that is more general than the traditional perspective of fixed-point attractor networks. There is a close correspondence between threshold-linear networks and networks defined by the generalized Lotka-Volterra equations.

Animals↗
PubMed2006

When response variability increases neural network robustness to synaptic noise.

Cortical sensory neurons are known to be highly variable, in the sense that responses evoked by identical stimuli often change dramatically from trial to trial. The origin of this variability is uncertain, but it is usually interpreted as detrimental noise that reduces the computational accuracy of neural circuits. Here we investigate the possibility that such response variability might in fact be beneficial, because it may partially compensate for a decrease in accuracy due to stochastic changes in the synaptic strengths of a network. We study the interplay between two kinds of noise, response (or neuronal) noise and synaptic noise, by analyzing their joint influence on the accuracy of neural networks trained to perform various tasks. We find an interesting, generic interaction: when fluctuations in the synaptic connections are proportional to their strengths (multiplicative noise), a certain amount of response noise in the input neurons can significantly improve network performance, compared to the same network without response noise. Performance is enhanced because response noise and multiplicative synaptic noise are in some ways equivalent. So if the algorithm used to find the optimal synaptic weights can take into account the variability of the model neurons, it can also take into account the variability of the synapses. Thus, the connection patterns generated with response noise are typically more resistant to synaptic degradation than those obtained without response noise. As a consequence of this interplay, if multiplicative synaptic noise is present, it is better to have response noise in the network than not to have it. These results are demonstrated analytically for the most basic network consisting of two input neurons and one output neuron performing a simple classification task, but computer simulations show that the phenomenon persists in a wide range of architectures, including recurrent (attractor) networks and sensorimotor networks that perform coordinate transformations. The results suggest that response variability could play an important dynamic role in networks that continuously learn.

Algorithms↗
PubMed2001

Neural network models of schizophrenia.

There is considerable neurobiological evidence suggesting that schizophrenia is associated with reduced corticocortical connectivity. The authors describe two neural network computer simulations that explore functional consequences of these abnormalities. The first utilized an "attractor" neural network capable of content-addressable memory. Application of a pruning rule that eliminated weaker connections over longer distances produced functional fragmentation and the emergence of localized, "parasitic" attractors that intruded into network dynamics. These pathologies generally were expressed only when input information was ambiguous and provide models for delusions and cognitive disorganization. A second neural network simulation examined effects of corticocortical pruning in a speech perception network. Excessive pruning caused the network to produce percepts spontaneously, that is, in the absence of inputs, thereby simulating hallucinations. The "hallucinating" network also demonstrated subtle impairments in narrative speech perception. A parallel study of human patients found similar impairments when comparing hallucinating patients with nonhallucinating patients. In addition, the authors have used transcranial magnetic stimulation (TMS) to directly probe speech perception neurocircuitry in patients with these hallucinations. As predicted by the neural network model, the authors confirmed that "suppressive" low-frequency TMS reduces auditory hallucinations. Neural network simulations provide empirically testable concepts linking phenomenological, cognitive, and neurobiological findings in schizophrenia.

Brain↗
PubMed2004

Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network.

BACKGROUND: Transcriptional regulation of cellular functions is carried out through a complex network of interactions among transcription factors and the promoter regions of genes and operons regulated by them. To better understand the system-level function of such networks simplification of their architecture was previously achieved by identifying the motifs present in the network, which are small, overrepresented, topologically distinct regulatory interaction patterns (subgraphs). However, the interaction of such motifs with each other, and their form of integration into the full network has not been previously examined. RESULTS: By studying the transcriptional regulatory network of the bacterium, Escherichia coli, we demonstrate that the two previously identified motif types in the network (i.e., feed-forward loops and bi-fan motifs) do not exist in isolation, but rather aggregate into homologous motif clusters that largely overlap with known biological functions. Moreover, these clusters further coalesce into a supercluster, thus establishing distinct topological hierarchies that show global statistical properties similar to the whole network. Targeted removal of motif links disintegrates the network into small, isolated clusters, while random disruptions of equal number of links do not cause such an effect. CONCLUSION: Individual motifs aggregate into homologous motif clusters and a supercluster forming the backbone of the E. coli transcriptional regulatory network and play a central role in defining its global topological organization.

Base Composition↗
PubMed2005

A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks.

BACKGROUND: Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation. RESULTS: We present a microarray data-based semi-kinetic (MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model. CONCLUSION: We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.

Amino Acid Motifs↗
PubMed2006

Metabolite coupling in genome-scale metabolic networks.

BACKGROUND: Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŝŝT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where ŝ is the binary form of S. RESULTS: Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual metabolites. CONCLUSION: The coupling of metabolites is an important topological property of metabolic networks. By computing coupling quantitatively for the first time in genome-scale metabolic networks, we provide insight into the basic structure of these networks.

Chromosome Mapping↗
PubMed2006

The comprehensive updated regulatory network of Escherichia coli K-12.

BACKGROUND: Escherichia coli is the model organism for which our knowledge of its regulatory network is the most extensive. Over the last few years, our project has been collecting and curating the literature concerning E. coli transcription initiation and operons, providing in both the RegulonDB and EcoCyc databases the largest electronically encoded network available. A paper published recently by Ma et al. (2004) showed several differences in the versions of the network present in these two databases. Discrepancies have been corrected, annotations from this and other groups (Shen-Orr et al., 2002) have been added, making the RegulonDB and EcoCyc databases the largest comprehensive and constantly curated regulatory network of E. coli K-12. RESULTS: Several groups have been using these curated data as part of their bioinformatics and systems biology projects, in combination with external data obtained from other sources, thus enlarging the dataset initially obtained from either RegulonDB or EcoCyc of the E. coli K12 regulatory network. We kindly obtained from the groups of Uri Alon and Hong-Wu Ma the interactions they have added to enrich their public versions of the E. coli regulatory network. These were used to search for original references and curate them with the same standards we use regularly, adding in several cases the original references (instead of reviews or missing references), as well as adding the corresponding experimental evidence codes. We also corrected all discrepancies in the two databases available as explained below. CONCLUSION: One hundred and fifty new interactions have been added to our databases as a result of this specific curation effort, in addition to those added as a result of our continuous curation work. RegulonDB gene names are now based on those of EcoCyc to avoid confusion due to gene names and synonyms, and the public releases of RegulonDB and EcoCyc are henceforth synchronized to avoid confusion due to different versions. Public flat files are available providing direct access to the regulatory network interactions thus avoiding errors due to differences in database modelling and representation. The regulatory network available in RegulonDB and EcoCyc is the most comprehensive and regularly updated electronically-encoded regulatory network of E. coli K-12.

Computer Simulation↗