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PubMed2004

Regulog analysis: detection of conserved regulatory networks across bacteria: application to Staphylococcus aureus.

A transcriptional regulatory network encompasses sets of genes (regulons) whose expression states are directly altered in response to an activating signal, mediated by trans-acting regulatory proteins and cis-acting regulatory sequences. Enumeration of these network components is an essential step toward the creation of a framework for systems-based analysis of biological processes. Profile-based methods for the detection of cis-regulatory elements are often applied to predict regulon members, but they suffer from poor specificity. In this report we describe Regulogger, a novel computational method that uses comparative genomics to eliminate spurious members of predicted gene regulons. Regulogger produces regulogs, sets of coregulated genes for which the regulatory sequence has been conserved across multiple organisms. The quantitative method assigns a confidence score to each predicted regulog member on the basis of the degree of conservation of protein sequence and regulatory mechanisms. When applied to a reference collection of regulons from Escherichia coli, Regulogger increased the specificity of predictions up to 25-fold over methods that use cis-element detection in isolation. The enhanced specificity was observed across a wide range of biologically meaningful parameter combinations, indicating a robust and broad utility for the method. The power of computational pattern discovery methods coupled with Regulogger to unravel transcriptional networks was demonstrated in an analysis of the genome of Staphylococcus aureus. A total of 125 regulogs were found in this organism, including both well-defined functional groups and a subset with unknown functions.

Bacillus subtilis↗

PubMed2006

An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality.

MOTIVATION: Modularity analysis is a powerful tool for studying the design of biological networks, offering potential clues for relating the biochemical function(s) of a network with the 'wiring' of its components. Relatively little work has been done to examine whether the modularity of a network depends on the physiological perturbations that influence its biochemical state. Here, we present a novel modularity analysis algorithm based on edge-betweenness centrality, which facilitates the use of directional information and measurable biochemical data.

Algorithms↗

PubMed2003

Artificial gene networks for objective comparison of analysis algorithms.

MOTIVATION: Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know. RESULTS: We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated. AVAILABILITY: The data sets and kinetic models described here are available from http://www.vbi.vt.edu/~mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats.

Algorithms↗

PubMed1996

Sample size and statistical power in [15O]H2O studies of human cognition.

Determining the appropriate sample size is a crucial component of positron emission tomography (PET) studies. Power calculations, the traditional method for determining sample size, were developed for hypothesis-testing approaches to data analysis. This method for determining sample size is challenged by the complexities of PET data analysis: use of exploratory analysis strategies, search for multiple correlated nodes on interlinked networks, and analysis of large numbers of pixels that may have correlated values due to both anatomical and functional dependence. We examine the effects of variable sample size in a study of human memory, comparing large (n = 33), medium (n = 16,17), small (n = 11, 11, 11), and very small (n = 6,6,7,7,7) samples. Results from the large sample are assumed to be the "gold standard." The primary criterion for assessing sample size is replicability. This is evaluated using a hierarchically ordered group of parameters: pattern of peaks, location of peaks, number of peaks, size (volume) of peaks, and intensity of the associated t (or z) statistic. As sample size decreases, false negatives begin to appear, with some loss of pattern and peak detection; there is no corresponding increase in false positives. The results suggest that good replicability occurs with a sample size of 10-20 subjects in studies of human cognition that use paired subtraction comparisons of single experimental/baseline conditions with blood flow differences ranging from 4 to 13%.

Adult↗

PubMed1979

Single cell isolation: a way to examine network interactions.

A procedure for isolating identified, small neurons from snail ganglia is described. The technique allows a particular neuron, previously identified by morphological and electrophysiological characteristics, to be marked and then isolated from the ganglia. This procedure was developed to permit the detailed comparison of the electrical characteristics of a neuron before and after isolation from an intact system. An earlier description has appeared. The cell somata is marked intracellularly by the iontophoretic injection of Procion navy blue H3RS which visually differentiates the cell from other cells in the ganglion. The ganglion is then treated with a trypsin-haluronidase solution to soften the ganglion sheath, which is then removed. The cells are gently shaken to isolate them from the ganglion and then examined electrophysiologically. A comparison of membrane properties, such as action potential height, duration and rate of rise and decay was made before and after all treatments were applied to assess deleterious effect. An analysis of network properties, such as burst duration, number of spikes per burst and presynaptic activity was also performed after each phase of the procedure. No significant differences were noted after dye injection, enzyme treatment, and where appropriate, after isolation. An increase in input resistance and corresponding decrease in the slope of the steady state current--voltage plot (I--V plot) were observed after isolation of a cell. These were expected results of removing the 'load' (i.e. axon or electrical coupling) from the cell soma. This method may be applied to many other systems to study the effects of network interactions on the properties of a single cell and should therefore facilitate the analysis of neuronal networks as well as single cell properties.

Animals↗

PubMedDate not supplied

A modification of the staining technique of reticular fibres for image analysis of the cardiac collagen network.

INTRODUCTION: Silver stain of reticular fibres demonstrates the fine structure of the cardiac collagen network. However, nuclei are also stained with current techniques, a drawback that makes computer image analysis difficult. To solve this problem our study was designed to modify Gomori's method. Reactive concentrations and action times represent the core of that modification. Only stromal tissue is stained. The technique was tested for repeatability and reproducibility to assess the precision of the measurement procedure in the assessment of myocardial collagen in a consecutive series of myocardial samples from patients with and without heart muscle disease. In addition, we checked the reliability of the method by comparing our results with the point-counting method (PCM). METHODS: The right ventricular myocardium was fixed in 10% buffered formaldehyde and routinely processed. Paraffin sections (4 microm) were stained with several modifications of Gomori's method. Variable concentrations of potassium permanganate, silver solutions and different oxidation times were tried. A field from each sample was digitized. Additionally, the technique was tested for repeatability and reproducibility. RESULTS: We obtained absence of background and nuclear staining together with a highly contrasted image of the collagen network with 3 min oxidation with 2% potassium permanganate and a silver concentration of 1% during 5 min. In this way, it was very easy to perform acquisition, thresholding and area measurement without any further manual processing of the image. CONCLUSIONS: This technique appears very helpful for the quantitative study of the cardiac collagen network by means of computerized image analysis systems.

Adolescent↗

PubMed2007

Functional network topology learning and sensitivity analysis based on ANOVA decomposition.

A new methodology for learning the topology of a functional network from data, based on the ANOVA decomposition technique, is presented. The method determines sensitivity (importance) indices that allow a decision to be made as to which set of interactions among variables is relevant and which is irrelevant to the problem under study. This immediately suggests the network topology to be used in a given problem. Moreover, local sensitivities to small changes in the data can be easily calculated. In this way, the dual optimization problem gives the local sensitivities. The methods are illustrated by their application to artificial and real examples.

Analysis of Variance↗

PubMed1986

Channel catfish virus: use of nucleic acids in studying viral relationships.

Restriction digestion patterns were used to determine differences in the DNA of various isolates of channel catfish virus (CCV). All viruses were different from each other and from the type strain of CCV. The differences in the digestion patterns were used to relate the viruses quantitatively as to sequence divergence between all pairs of viruses. A range of values from 104 nucleotide changes to 1,690 nucleotide changes/total DNA of CCV was found for the various pairs. A cladistic analysis produced a phylogenetic network relating the viruses by possible ancestory. This network indicated that some viruses were relatively more separated from other viruses that were clustered in the network. A phenetic analysis indicated that the viruses that were clustered in the network were also reasonably similar to one another.

Animals↗

PubMed2005

Analysis of cyclic dynamics for networks of linear threshold neurons.

The network of neurons with linear threshold (LT) transfer functions is a prominent model to emulate the behavior of cortical neurons. The analysis of dynamic properties for LT networks has attracted growing interest, such as multistability and boundedness. However, not much is known about how the connection strength and external inputs are related to oscillatory behaviors. Periodic oscillation is an important characteristic that relates to nondivergence, which shows that the network is still bounded although unstable modes exist. By concentrating on a general parameterized two-cell network, theoretical results for geometrical properties and existence of periodic orbits are presented. Although it is restricted to two-dimensional systems, the analysis can provide a useful contribution to analyze cyclic dynamics of some specific LT networks of high dimension. As an application, it is extended to an important class of biologically motivated networks of large scale: the winner-take-all model using local excitation and global inhibition.

Action Potentials↗

PubMed1989

Stability of symmetric idiotypic networks--a critique of Hoffmann's analysis.

Hoffmann (1982) analysed a very simple model of suppressive idiotypic immune networks and showed that idiotypic interactions are stabilizing. He concluded that immune networks provide a counterexample to the general analysis of large dynamic systems (Gardner and Ashby, 1970; May, 1972). The latter is often verbalized as: an increase in size and/or connectivity decreases the system stability. We here analyse this apparent contradiction by extending the Hoffmann model (with a decay term), and comparing it to an ecological model that was used as a paradigm in the general analysis. Our analysis confirms that the neighbourhood stability of such idiotypic networks increases with connectivity and/or size. However, the contradiction is one of interpretation, and is not due to exceptional properties of immune networks. The contradiction is caused by the awkward normalization used in the general analysis.

Animals↗

PubMed1998

Links between social network and quality of life: an epidemiologically representative study of psychotic patients in south London.

Quality of life has been found to be associated with social networks in patients with psychiatric disorders. We aimed to determine whether quality of life was related to social network size in group of severely mentally ill subjects living in the community. In a population-based, prospective controlled study of two sector mental health teams in South London, a random sample of representative 1-year prevalent cases of non-organic psychosis was identified. Patients were interviewed at baseline, and associations between quality of life and social network size were analyzed cross-sectionally. For average quality of life there was increase up to a certain level of social network size (about 20 social contacts). For the quality of life subscore on social relations there appeared to be an optimal middle level of network size (10-12), with lower subscores for smaller and larger networks. Multivariate analysis confirmed the associations between quality of life and social network size. In analyses of network subgroups the importance of confiding contacts was underlined.

Adolescent↗

PubMedDate not supplied

Towards more biological mutation operators in gene regulation studies.

Genetic regulation is often viewed as a complex system whose properties emerge from the interaction of regulatory genes. One major paradigm for studying the complex dynamics of gene regulation uses directed graphs to explore structure, behaviour and evolvability. Mutation operators used in such studies typically involve the insertion and deletion of nodes, and the insertion, deletion and rewiring of links at the network level. These network-level mutational operators are sufficient to allow the statistical analysis of network structure, but impose limitations on the way networks are evolved. There are a wide variety of mutations in DNA sequences that have yet to be analysed for their network-level effects. By modelling an artificial genome at the level of nucleotide sequences and mapping it to a regulatory network, biologically grounded mutation operators can be mapped to network-level mutations. This paper analyses five such sequence level mutations (single-point mutation, transposition, inversion, deletion and gene duplication) for their effects at the network level. Using analytic and simulation techniques, we show that it is rarely the case that nodes and links are cleanly added or deleted, with even the simplest point mutation causing a wide variety of network-level modifications. As expected, the vast majority of simple (single-point) mutations are neutral, resulting in a neutral plateau from which a range of functional behaviours can be reached. By analysing the effects of sequence-level mutations at the network level of gene regulation, we aim to stimulate more careful consideration of mutation operators in gene regulation models than has previously been given.

Algorithms↗

PubMed2001

Recognition of environmental and genetic effects on barley phenolic fingerprints by neural networks.

Through computational analysis of high-performance liquid chromatography (HPLC) traces we find correlations between secondary metabolites and growth conditions of six varieties of barley. Using artificial neural networks, it was possible to classify chromatograms for which the varieties were fertilized by nitrogen and treated by fungicide. For each variety of barley we could also differentiate it from the others. Surprisingly, all these classification tasks could be solved successfully by a simple network with no hidden units. When adding to the methodology pruning of the network weights, we were able to reduce the set of peaks in the chromatograms and obtain a necessary subset from which the growth conditions and differentiation may be decided. In some instances, more complex networks with hidden units could lead to a further reduction of the number of peaks used. In most cases, far more than half of the peaks are redundant. We find that it requires fewer information-rich peaks to perform the variety differentiation tasks than to recognize any of the growth conditions. Analysis of the network weights reveals correlations between weighted combinations of peaks.

Chromatography↗

PubMed1994

Differentiation of beats of ventricular and sinus origin using a self-training neural network.

Despite advances in the computerized detection of arrhythmias, arrhythmia recognition by morphological waveform analysis still poses a difficult problem. Artificial neural networks, computer algorithms that are self-trained by an analog of biological synaptic modification to perform pattern recognition, hold great promise for the differentiation of various cardiac rhythms. The goal of this study was to differentiate beats of sinus and ventricular origin on a global basis and on a patient-specific basis by the use of artificial neural network analysis. Neural networks were trained to recognize digitized intracardiac electrograms (9 patients) and surface electrocardiograms (11 patients) obtained during sinus rhythm and ventricular tachycardia. After training, sinus rhythm or ventricular tachycardia beats were input into the neural network, and classified as to their origin. By the use of modified receiver operating characteristic curve plots, it was possible to differentiate with high sensitivity and specificity between beats of sinus origin and ventricular origin in all patients. The addition of high amounts of noise to the beats did not markedly degrade the performance of the surface ECG neural networks, and still allowed high sensitivity in differentiating beats of sinus origin from beats of ventricular origin, especially when noise was added to the training set. Neural networks provided sensitive and specific detection of cardiac electrical activity during sinus rhythm and ventricular tachycardia, and may play an important role in allowing development of improved arrhythmia recognition and management systems.

Aged↗

PubMed1998

A novel organotypic long-term culture of the rat hippocampus on substrate-integrated multielectrode arrays.

Spatiotemporally coordinated activity of neural networks is crucial for brain functioning. To understand the basis of physiological information processing and pathological states, simultaneous multisite long-term recording is a prerequisite. In a multidisciplinary approach we developed a novel system of organotypically cultured rat hippocampal slices on a planar 60-microelectrode array (MEA). This biohybrid system allowed cultivation for 4 weeks. Methods known from semiconductor production were employed to fabricate and characterize the MEA. Simultaneous extracellular recording of local field potentials (LFPs) and spike activity at 60 sites under sterile conditions allowed the analysis of network activity with high spatiotemporal resolution. To our knowledge this is the first realization of hippocampus cultured organotypically on multi-microelectrode arrays for simultaneous recording and electrical stimulation. This biohybrid system promises to become a powerful tool for drug discovery and for the analysis of neural networks, of synaptic plasticity, and of pathophysiological conditions such as ischemia and epilepsy.

Action Potentials↗

PubMed2001

Classification of rapeseed and soybean oils by use of unsupervised pattern-recognition methods and neural networks.

Unsupervised pattern-recognition methods and Kohonen neural networks have been applied to the classification of rapeseed and soybean oil samples according to their type and quality by use of chemical and physical properties (density, refractive index, saponification value, and iodine and acid numbers) and thermal properties (thermal decomposition temperatures) as variables. A multilayer feed-forward (MLF) neural network (NN) has been used to select the most important variables for accurate classification of edible oils. To accomplish this task different neural networks architectures trained by back propagation of error method, using chemical, physical, and thermal properties as inputs, were employed. The network with the best performance and the smallest root mean squared (RMS) error was chosen. The results of MLF network sensitivity analysis enabled the identification of key properties, which were again used as variables in principal components analysis (PCA), cluster analysis (CA), and in Kohonen self-organizing feature maps (SOFM) to prove their reliability.

Cluster Analysis↗

PubMed2006

Cerebral metabolite differences in adolescents with low birth weight: assessment with in vivo proton MR spectroscopy.

BACKGROUND: Children with very low birth weight (VLBW) have a significantly increased risk of later neurodevelopmental problems, while infants born small for gestational age (SGA) at term are also at some risk of developing neurological impairment. OBJECTIVE: To investigate possible brain metabolite differences in adolescents with VLBW, SGA at term and controls by proton in vivo magnetic resonance spectroscopy (MRS) at 1.5 T. MATERIALS AND METHODS: MR spectra were acquired from volumes localized in the left frontal lobe, containing mainly white matter (54 subjects). Peak areas of N-acetyl aspartate (NAA), choline (Cho) and creatine (Cr) were determined, and the peak area ratio of NAA to Cr, total Cho to Cr, or NAA to Cho calculated. Probabilistic neural network (PNN) analysis was performed utilizing the chemical shift region containing resonances from NAA, Cho and Cr as inputs. RESULTS: No significant difference in the peak area ratios could be found using the Kruskal-Wallis test. By application of PNN, a correct classification of 52 of the 54 adolescents with a sensitivity and specificity exceeding 93% for all groups was achieved. CONCLUSION: Small, yet systematic, differences in brain metabolite distribution among the groups were confirmed by PNN analysis.

Adolescent↗

PubMed2004

Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system.

The use of volatile production patterns produced by Mycobacterium tuberculosis and associated bacterial infections from sputum samples were examined in vitro and in situ using an electronic nose based on a 14 sensor conducting polymer array. In vitro, it was possible to successfully discriminate between M. tuberculosis (TB) and control media, and between M. tuberculosis and M. avium, M. scrofulaceum and Pseudomonas aeruginosa cultures in the stationary phase after 5-6h incubation at 37 degrees C based on 35 samples. Using neural network (NN) analysis and cross-validation it was possible to successfully identify 100% of the TB cultures from others. A second in vitro study with 61 samples all four groups were successfully discriminated with 14 of 15 unknowns within each of the four groups successfully identified using cross-validation and discriminant function analysis. Subsequently, lipase enzymes were added to 46 sputum samples directly obtained from patients and the head space analysed. Parallel measurements of bacterial contamination were also carried out for confirmation using agar media. NN analysis was carried out using some of the samples as a training set. Based on the NN and genetic algorithms of up to 10 generations it was possible to successfully cross-validate 9 of 10 unknown samples. PCA was able to discriminate between TB infection alone, the controls, M. avium, P. aeruginosa and a mixed infection. These findings will have significant implications for the development of rapid qualitative systems for screening of patient samples and clinical diagnosis of tuberculosis.

Algorithms↗