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At least 595 records · Page 33Linked to original sources
PubMed2002

Dynamical properties of model communication networks.

We study the dynamical properties of a collection of models for communication processes, characterized by a single parameter xi representing the relation between information load of the nodes and its ability to deliver this information. The critical transition to congestion reported so far occurs only for xi=1. This case is well analyzed for different network topologies. We focus on the properties of the order parameter, the susceptibility, and the time correlations when approaching the critical point. For xi<1, no transition to congestion is observed but it remains a crossover from a low-density to a high-density state. For xi>1, the transition to congestion is discontinuous and congestion nuclei arise.

Journal Article↗
PubMed1997

Experimental analysis of neuronal dynamics in cultured cortical networks and transitions between different patterns of activity.

Experimental investigation of the dynamics of biological networks is a fundamental step towards understanding how the nervous system works. Spontaneous activity in cultured networks of cortical neurons has been investigated by using a multisite recording technique with planar electrode arrays. In these networks, the spatiotemporal firing patterns were studied in the presence of different extracellular solutions. Transitions from asynchronous firing dynamics to synchronous firing dynamics were observed when the extracellular Ca2+ concentration was increased from 0.1 mM to 1 mM. Addition of extracellular Mg2+ reduced the spontaneous activity at any Ca2+ concentration, and an increase in the extracellular K+ concentration enhanced the frequency of periodical synchronous bursts. N-methyl-D-aspartate (NMDA) and non-NMDA glutamate receptor antagonists inhibited synchronous activity. A spatiotemporal analysis of the data has been performed, and the properties of the network such as the synchronization and the periodicity have been quantified in order to clarify how variations of intrinsic parameters of the network can induce structural transitions in the neural dynamics. This experimental study is a possible approach to investigate the computational properties of a neuronal network.

Animals↗
PubMed2006

Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.

Cell signaling mechanisms often transmit information via posttranslational protein modifications, most importantly reversible protein phosphorylation. Here we develop and apply a general mass spectrometric technology for identification and quantitation of phosphorylation sites as a function of stimulus, time, and subcellular location. We have detected 6,600 phosphorylation sites on 2,244 proteins and have determined their temporal dynamics after stimulating HeLa cells with epidermal growth factor (EGF) and recorded them in the Phosida database. Fourteen percent of phosphorylation sites are modulated at least 2-fold by EGF, and these were classified by their temporal profiles. Surprisingly, a majority of proteins contain multiple phosphorylation sites showing different kinetics, suggesting that they serve as platforms for integrating signals. In addition to protein kinase cascades, the targets of reversible phosphorylation include ubiquitin ligases, guanine nucleotide exchange factors, and at least 46 different transcriptional regulators. The dynamic phosphoproteome provides a missing link in a global, integrative view of cellular regulation.

Binding Sites↗
PubMed2002

Neural network-based segmentation of dynamic MR mammographic images.

The usefulness of neural networks for the classification of signal-time curves from dynamic MR mammography was recently demonstrated by our group. The multi-layer perceptron under study consists of 28 input, 4 hidden, and 3 output nodes, and was trained to classify signal-time curves into three tissue classes: "carcinoma," "benign lesion," and "parenchyma." Extending this approach, it was the aim of the present study to evaluate the performance of the developed network in the segmentation of dynamic MR mammographic images in comparison to a pixel-by-pixel two-compartment pharmacokinetic analysis. The population investigated in this pilot study comprised 15 women with suspicious lesions in the breast, which were confirmed histologically after the MR examination. The neural network classified the same areas as malignant as those which were marked as being highly suspicious by the pharmacokinetic mapping approach but with the advantage that no a priori knowledge on tissue microcirculation was needed, that computation proved to be much faster, and that it yielded a unique classification into just three tissue classes.

Breast↗
PubMed2001

Dynamic localization of rop GTPases to the tonoplast during vacuole development.

Vacuoles are essential pleomorphic organelles that undergo dynamic changes during cell growth and differentiation in plants. How developmental signals are linked to vacuole biogenesis and development is poorly understood. In this report, we show that a Rop GTPase is localized to developing vacuoles in pea (Pisum sativum cv Extra Early Alaska). Rop belongs to the RHO family of Ras-related small GTP-binding proteins that are key molecular switches in a wide variety of eukaryotic signal transduction pathways. Using indirect immunofluorescence and an anti-Rop antibody, we showed that Rop proteins accumulate to high levels in rapidly growing tapetal cells of pea anthers. In these cells, Rop is localized to an endomembrane system that exists as dynamic pleomorphic networks: a perinuclear fine network decorated with punctate dots, a network composed of small spheres and tubules, and interconnected chambers. Colocalization with a tonoplast annexin VCaB42 shows that these dynamic networks represent the tonoplast. Our results suggest that the dynamic Rop-containing tonoplast networks represent a unique stage of vacuole development. The specific localization of Rop to developing vacuoles supports a role for Rop in signal transduction that mediates vacuole development in plants.

Endoplasmic Reticulum↗
PubMed1998

Laws for the dynamics of regulatory networks.

We start our analysis from historical but too seldom quoted papers by Delbrück, Noveck & Weiner, Cohn & Horibata and Monod & Jacob. We try to show how it became possible to draw a line coupling cell differentiation to the physical concept of multistationarity, and the latter to the concept of positive feedback circuits. Two laws give the minimal logical ingredients required for differentiative and homeostatic regulations. It is briefly shown how they can be used to treat such complex dynamics as deterministaic chaos, which, admittedly, does not yet belong to the corpus of developmental biology. It was taken as a challenge to express our ideas here in purely verbal terms, avoiding any formal treatment.

Animals↗
PubMed2004

Genetic networks with canalyzing Boolean rules are always stable.

We determine stability and attractor properties of random Boolean genetic network models with canalyzing rules for a variety of architectures. For all power law, exponential, and flat in-degree distributions, we find that the networks are dynamically stable. Furthermore, for architectures with few inputs per node, the dynamics of the networks is close to critical. In addition, the fraction of genes that are active decreases with the number of inputs per node. These results are based upon investigating ensembles of networks using analytical methods. Also, for different in-degree distributions, the numbers of fixed points and cycles are calculated, with results intuitively consistent with stability analysis; fewer inputs per node implies more cycles, and vice versa. There are hints that genetic networks acquire broader degree distributions with evolution, and hence our results indicate that for single cells, the dynamics should become more stable with evolution. However, such an effect is very likely compensated for by multicellular dynamics, because one expects less stability when interactions among cells are included. We verify this by simulations of a simple model for interactions among cells.

Animals↗
PubMed2006

Desynchronization in diluted neural networks.

The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of "stable chaos," i.e., by observing that the stochasticlike behavior is "limited" to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary.

Journal Article↗
PubMed2005

Dynamics of Drosophila embryonic patterning network perturbed in space and time using microfluidics.

Biochemical networks are perturbed both by fluctuations in environmental conditions and genetic variation. These perturbations must be compensated for, especially when they occur during embryonic pattern formation. Complex chemical reaction networks displaying spatiotemporal dynamics have been controlled and understood by perturbing their environment in space and time. Here, we apply this approach using microfluidics to investigate the robust network in Drosophila melanogaster that compensates for variation in the Bicoid morphogen gradient. We show that the compensation system can counteract the effects of extremely unnatural environmental conditions--a temperature step--in which the anterior and posterior halves of the embryo are developing at different temperatures and thus at different rates. Embryonic patterning was normal under this condition, suggesting that a simple reciprocal gradient system is not the mechanism of compensation. Time-specific reversals of the temperature step narrowed down the critical period for compensation to between 65 and 100 min after onset of embryonic development. The microfluidic technology used here may prove useful to future studies, as it allows spatial and temporal regulation of embryonic development.

Animals↗
PubMed2001

Fluorescently-labeled fimbrin decorates a dynamic actin filament network in live plant cells.

Recently it has been established, through a detailed biochemical analysis, that recombinant Arabidopsis thaliana fimbrin 1 (AtFim1) is a member of the fimbrin/plastin family of actin filament bundling or cross-linking proteins [D.R. Kovar et al. (2000) Plant J 24:625-636]. To determine whether AtFim1 can function as an F-actin-binding protein in the complex environment of the plant cell cytoplasm, we created a fluorescent protein analog and introduced it by microinjection into live Tradescantia virginiana L. stamen hair cells. AtFim1 derivatized with Oregon Green 488 had biochemical properties similar to unlabeled fimbrin, including the Kd value for binding to plant F-actin and the ability to cross-link filaments into higher-order structures. Fluorescent-fimbrin decorated an array of fine actin filaments in the cortical cytoplasm of stamen hair cells, which were shown with time-course studies to be highly dynamic. These data establish AtFim1 as a bona fide member of the fimbrin/plastin family, and represent the first use of a plant actin-binding protein as a powerful cytological tool for tracking the spatial and temporal redistribution of actin filaments in individual cells.

Actin Cytoskeleton↗
PubMed2005

Finite size effects in tightly meshed polymer networks.

Molecular dynamics computer simulations on regular, tightly meshed model networks exhibit variations of the network density with system size. We show that these variations are due mainly to network elasticity. A theoretical expression derived on the basis of the self-consistent-field approach yields finite size scaling behavior in good accord with the simulation for a wide range of thermodynamic conditions.

Journal Article↗
PubMed2006

Designing the dynamics of spiking neural networks.

Precise timing of spikes and temporal locking are key elements of neural computation. Here we demonstrate how even strongly heterogeneous, deterministic neural networks with delayed interactions and complex topology can exhibit periodic patterns of spikes that are precisely timed. We develop an analytical method to find the set of all networks exhibiting a predefined pattern dynamics. Such patterns may be arbitrarily long and of complicated temporal structure. We point out that the same pattern can exist in very different networks and have different stability properties.

Action Potentials↗
PubMed2006

oGNM: online computation of structural dynamics using the Gaussian Network Model.

An assessment of the equilibrium dynamics of biomolecular systems, and in particular their most cooperative fluctuations accessible under native state conditions, is a first step towards understanding molecular mechanisms relevant to biological function. We present a web-based system, oGNM that enables users to calculate online the shape and dispersion of normal modes of motion for proteins, oligonucleotides and their complexes, or associated biological units, using the Gaussian Network Model (GNM). Computations with the new engine are 5-6 orders of magnitude faster than those using conventional normal mode analyses. Two cases studies illustrate the utility of oGNM. The first shows that the thermal fluctuations predicted for 1250 non-homologous proteins correlate well with X-ray crystallographic data over a broad range [7.3-15 A] of inter-residue interaction cutoff distances and the correlations improve with increasing observation temperatures. The second study, focused on 64 oligonucleotides and oligonucleotide-protein complexes, shows that good agreement with experiments is achieved by representing each nucleotide by three GNM nodes (as opposed to one-node-per-residue in proteins) along with uniform interaction ranges for all components of the complexes. These results open the way to a rapid assessment of the dynamics of DNA/RNA-containing complexes. The server can be accessed at http://ignm.ccbb.pitt.edu/GNM_Online_Calculation.htm.

Algorithms↗
PubMed2004

Data-based model and parameter evaluation in dynamic transcriptional regulatory networks.

Finding the causality and strength of connectivity in transcriptional regulatory networks from time-series data will provide a powerful tool for the analysis of cellular states. Presented here is the design of tools for the evaluation of the network's model structure and parameters. The most effective tools are found to be based on evolution strategies. We evaluate models of increasing complexity, from lumped, algebraic phenomenological models to Hill functions and thermodynamically derived functions. These last functions provide the free energies of binding of transcription factors to their operators, as well as cooperativity energies. Optimization results based on published experimental data from a synthetic network in Escherichia coli are presented. The free energies of binding and cooperativity found by our tools are in the same physiological ranges as those experimentally derived in the bacteriophage lambda system. We also use time-series data from high-density oligonucleotide microarrays of yeast meiotic expression patterns. The algorithm appropriately finds the parameters of pairs of regulated regulatory yeast genes, showing that for related genes an overall reasonable computation effort is sufficient to find the strength and causality of the connectivity of large numbers of them.

Algorithms↗
PubMed1997

Emergence of clusters in the hidden layer of a dynamic recurrent neural network.

The neural integrator of the oculomotor system is a privileged field for artificial neural network simulation. In this paper, we were interested in an improvement of the biologically plausible features of the Arnold-Robinson network. This improvement was done by fixing the sign of the connection weights in the network (in order to respect the biological Dale's Law). We also introduced a notion of distance in the network in the form of transmission delays between its units. These modifications necessitated the introduction of a general supervisor in order to train the network to act as a leaky integrator. When examining the lateral connection weights of the hidden layer, the distribution of the weights values was found to exhibit a conspicuous structure: the high-value weights were grouped in what we call clusters. Other zones are quite flat and characterized by low-value weights. Clusters are defined as particular groups of adjoining neurons which have strong and privileged connections with another neighborhood of neurons. The clusters of the trained network are reminiscent of the small clusters or patches that have been found experimentally in the nucleus prepositus hypoglossi, where the neural integrator is located. A study was conducted to determine the conditions of emergence of these clusters in our network: they include the fixation of the weight sign, the introduction of a distance, and a convergence of the information from the hidden layer to the motoneurons. We conclude that this spontaneous emergence of clusters in artificial neural networks; performing a temporal integration, is due to computational constraints, with a restricted space of solutions. Thus, information processing could induce the emergence of iterated patterns in biological neural networks.

Animals↗
PubMed1990

Toward modeling a dynamic biological neural network.

Mammalian macular endorgans are linear bioaccelerometers located in the vestibular membranous labyrinth of the inner ear. In this paper, the organization of the endorgan is interpreted on physical and engineering principles. This is a necessary prerequisite to mathematical and symbolic modeling of information processing by the macular neural network. Mathematical notations that describe the functioning system were used to produce a novel, symbolic model. The model is six-tiered and is constructed to mimic the neural system. Initial simulations show that the network functions best when some of the detecting elements (type I hair cells) are excitatory and others (type II hair cells) are weakly inhibitory. The simulations also illustrate the importance of disinhibition of receptors located in the third tier in shaping nerve discharge patterns at the sixth tier in the model system.

Acceleration↗
PubMed1994

Non-linear dynamics in neural networks.

A general framework for the analysis of neurons as stochastic, three-dimensionally complex and non-linear units with a range of temporal properties is outlined, and a class of problems delineated. Some general mathematical properties of the resulting network are deduced, together with information-theoretic questions to be pursued. In particular examples of the relevance of the nonlinear, temporal and stochastic properties of neurons in effective information processing are briefly outlined.

Animals↗