Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Dynamic network”

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 1,603 records · Page 89Linked to original sources
PubMed2004

The yeast cell-cycle network is robustly designed.

The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the cell-cycle regulatory network of the budding yeast. With the use of a simple dynamical model, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state, the G1 state, is a global attractor of the dynamics. The biological pathway, the cell-cycle sequence of protein states, is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.

Cell Cycle↗
PubMed2004

An annealed chaotic maximum neural network for bipartite subgraph problem.

In this paper, based on maximum neural network, we propose a new parallel algorithm that can help the maximum neural network escape from local minima by including a transient chaotic neurodynamics for bipartite subgraph problem. The goal of the bipartite subgraph problem, which is an NP- complete problem, is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. Lee et al. presented a parallel algorithm using the maximum neural model (winner-take-all neuron model) for this NP- complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to a local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm. The simulation results show that our algorithm finds the optimum or near-optimum solution for the bipartite subgraph problem superior to that of the best existing parallel algorithms.

Algorithms↗
PubMed1997

Ca2+-mediated GTP-dependent dynamic assembly of bacterial cell division protein FtsZ into asters and polymer networks in vitro.

FtsZ, a tubulin-like GTPase that forms a dynamic ring marking the division plane of prokaryotic cells, is essential for cytokinesis. It is not known what triggers FtsZ ring assembly. In this work, we use a FtsZ-green fluorescent protein (Gfp) chimera to assay FtsZ assembly over time by using fluorescence microscopy. We show that FtsZ polymers can assemble dynamically in solution in a GTP-dependent manner. Initially, FtsZ nucleation centers grow into aster-like structures that dramatically resemble microtubule organizing centers. As assembly proceeds further, protofilament bundles emanating from different asters interconnect, mimicking the closure of the FtsZ ring in vivo. Surprisingly, millimolar levels of Ca2+ promote FtsZ dynamic assembly. FtsZ can undergo repeated GTP-dependent assembly and disassembly in solution by sequential addition and removal of Ca2+. In addition, GTP binding and hydrolysis by FtsZ are regulated by Ca2+ concentration. Although the concentration of Ca2+ required for FtsZ assembly in vitro is high, its clear and specific effect on FtsZ dynamics suggests the possibility that Ca2+ may have a role in regulating FtsZ ring assembly in the cell.

Bacterial Proteins↗
PubMed2007

Distinct synaptic dynamics of heterogeneous pacemaker neurons in an oscillatory network.

Many rhythmically active networks involve heterogeneous populations of pacemaker neurons with potentially distinct synaptic outputs that can be differentially targeted by extrinsic inputs or neuromodulators, thereby increasing possible network output patterns. To understand the roles of heterogeneous pacemaker neurons, we characterized differences in synaptic output from the anterior burster (AB) and pyloric dilator (PD) neurons in the lobster pyloric network. These intrinsically distinct neurons are strongly electrically coupled, coactive, and constitute the pyloric pacemaker ensemble. During pyloric oscillations, the pacemaker neurons produce compound inhibitory synaptic connections to the follower lateral pyloric (LP) and pyloric constrictor (PY) neurons, which fire out of phase with AB/PD and with different delay times. Using pharmacological blockers, we separated the synapses originating from the AB and PD neurons and investigated their temporal dynamics. These synapses exhibited distinct short-term dynamics, depending on the presynaptic neuron type, and had different relative contributions to the total synaptic output depending on waveform shape and cycle frequency. However, paired comparisons revealed that the amplitude or dynamics of synapses from either the AB or PD neuron did not depend on the postsynaptic neuron type, LP or PY. To address the functional implications of these findings, we examined the correlation between synaptic inputs from the pacemakers and the burst onset phase of the LP and PY neurons in the ongoing pyloric rhythm. These comparisons showed that the activity of the LP and PY neurons is influenced by the peak phase and amplitude of the synaptic inputs from the pacemaker neurons.

Animals↗
PubMed2006

Modelling in molecular biology: describing transcription regulatory networks at different scales.

Approaches to describe gene regulation networks can be categorized by increasing detail, as network parts lists, network topology models, network control logic models or dynamic models. We discuss the current state of the art for each of these approaches. We study the relationship between different topology models, and give examples how they can be used to infer functional annotations for genes of unknown function. We introduce a new simple way of describing dynamic models called finite state linear model (FSLM). We discuss the gap between the parts list and topology models on one hand, and network logic and dynamic models, on the other hand. The first two classes of models have reached a genome-wide scale, while for the other model classes high-throughput technologies are yet to make a major impact.

Computational Biology↗
PubMed2002

Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics.

A basic challenge in systems biology is to understand the dynamical behavior of gene regulation networks. Current approaches aim at determining the network structure based on genomic-scale data. However, the network connectivity alone is not sufficient to define its dynamics; one needs to also specify the kinetic parameters for the regulation reactions. Here, we ask whether effective kinetic parameters can be assigned to a transcriptional network based on expression data. We present a combined experimental and theoretical approach based on accurate high temporal-resolution measurement of promoter activities from living cells by using green fluorescent protein (GFP) reporter plasmids. We present algorithms that use these data to assign effective kinetic parameters within a mathematical model of the network. To demonstrate this, we employ a well defined network, the SOS DNA repair system of Escherichia coli. We find a strikingly detailed temporal program of expression that correlates with the functional role of the SOS genes and is driven by a hierarchy of effective kinetic parameter strengths for the various promoters. The calculated parameters can be used to determine the kinetics of all SOS genes given the expression profile of just one representative, allowing a significant reduction in complexity. The concentration profile of the master SOS transcriptional repressor can be calculated, demonstrating that relative protein levels may be determined from purely transcriptional data. This finding opens the possibility of assigning kinetic parameters to transcriptional networks on a genomic scale.

Algorithms↗
PubMed1996

Synchronized chaos in local cortical circuits.

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This is in marked contrast to the behavior of cortical neurons in-vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a "hypercolumn" in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model with several cellular and synaptic conductances. The pattern of connectivity is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and analyzed. The relation between the results of the model and experiments in visual cortex is discussed.

Animals↗
PubMed2004

Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network.

It is currently believed that the mechanisms underlying spindle oscillations are related to those that generate spike and wave (SW) discharges. The mechanisms of transition between these two types of activity, however, are not well understood. In order to provide more insight into the dynamics of the neuronal networks leading to seizure generation in a rat experimental model of absence epilepsy we developed a computational model of thalamo-cortical circuits based on relevant (patho)physiological data. The model is constructed at the macroscopic level since this approach allows to investigate dynamical properties of the system and the role played by different mechanisms in the process of seizure generation, both at short and long time scales. The main results are the following: (i) SW discharges represent dynamical bifurcations that occur in a bistable neuronal network; (ii) the durations of paroxysmal and normal epochs have exponential distributions, indicating that transitions between these two stable states occur randomly over time with constant probabilities; (iii) the probabilistic nature of the onset of paroxysmal activity implies that it is not possible to predict its occurrence; (iv) the bistable nature of the dynamical system allows that an ictal state may be aborted by a single counter-stimulus.

Animals↗
PubMed2001

Convergence of the Integration Dynamics of the Construction-Integration Model.

The connectionist network corresponding to the nonlinear integration dynamical system associated with Kintsch's construction-integration (CI) model is analysed with linear algebra tools. This addresses some theoretical questions raised and left unanswered by Rodenhausen (1992, Psychological Review 99, 547-549). A mathematical characterization for equilibrium points, which allows an a priori enumeration of all possible asymptotic states for the integration dynamical system, given a connectivity matrix, is given. The dynamics of convergence of the integration dynamical system is characterized in some detail as well. This provides a tool for understanding CI simulations and helps in particular to let us know to what extent the outcome will depend on the initial conditions. The criteria also provide a new mathematical analysis which allows for the explicit calculation of asymptotic states of the integration process without requiring computer simulation experiments. The new mathematical analysis should facilitate comparisons of the model's predictions with behavioural data. Copyright 2001 Academic Press.

Journal Article↗
PubMed2001

Investigation of the nonlinearity in the heart rate dynamics.

This paper presents the application of a neural network to predict human heart rate. Electrocardiograms were measured from 5 healthy adult human subjects and 5 data sets were constructed calculating instantaneous heart rate from the measured signal. The nonlinear radial basis function neural network was applied to have a one step ahead prediction of the 1000 point heart rate. The results of the prediction are compared to that obtained by a linear autoregressive model. The results show that the neural network performs better than the autoregressive model in predicting heart rate for 2 data sets while for the other 3 data sets the performance of the two models is statistically similar. This indicates that the heart rate may be controlled nonlinearly by the autonomic nervous system.

Adult↗
PubMed2003

Diffusion-limited friendship network: a model for six degrees of separation.

A dynamic model of a society is studied where each person is an uncorrelated and noninteracting random walker. A dynamical random graph represents the acquaintance network of the society whose nodes are the individuals and links are the pairs of mutual friendships. This network exhibits a different percolationlike phase transition in all dimensions. On introducing simultaneous death and birth rates in the population, we show that the friendship network shows the six degrees of separation for ever after where the precise value of the network diameter depends on the death/birth rate. A susceptible-infected-susceptible-type model of disease spreading shows that this society always remains healthy if the population density is less than certain threshold value.

Journal Article↗
PubMed2002

Stability of dead zone bidirectional associative memory neural networks involving time delays.

A mathematical model describing the dynamical interactions of bidirectional associative memory networks involving transmission delays is considered. The influence of a dead zone or a zone of noactivation on the global stability is investigated and various easily verifiable sets of sufficient conditions are established. The asymptotic nature of solutions when the given system of equations does not possess an equilibrium pattern is discussed.

Association Learning↗
PubMed2006

Integrated analysis of multiple data sources reveals modular structure of biological networks.

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.

Algorithms↗
PubMedDate not supplied

Universality in visual cortical pattern formation.

During ontogenetic development, the visual cortical circuitry is remodeled by activity-dependent mechanisms of synaptic plasticity. From a dynamical systems perspective this is a process of dynamic pattern formation. The emerging cortical network supports functional activity patterns that are used to guide the further improvement of the network's structure. In this picture, spontaneous symmetry breaking in the developmental dynamics of the cortical network underlies the emergence of cortical selectivities such as orientation preference. Here universal properties of this process depending only on basic biological symmetries of the cortical network are analyzed. In particular, we discuss the description of the development of orientation preference columns in terms of a dynamics of abstract order parameter fields, connect this description to the theory of Gaussian random fields, and show how the theory of Gaussian random fields can be used to obtain quantitative information on the generation and motion of pinwheels, in the two dimensional pattern of visual cortical orientation columns.

Animals↗
PubMed2005

Synchronous neural activity in scale-free network models versus random network models.

Synchronous firing peaks at levels greatly exceeding background activity have recently been reported in neocortical tissue. A small subset of neurons is dominant in a large fraction of the peaks. To investigate whether this striking behavior can emerge from a simple model, we constructed and studied a model neural network that uses a modified Hopfield-type dynamical rule. We find that networks having a power-law ("scale-free") node degree distribution readily generate extremely large synchronous firing peaks dominated by a small subset of nodes, whereas random (Erdös-Rényi) networks do not. This finding suggests that network topology may play an important role in determining the nature and magnitude of synchronous neural activity.

Computer Simulation↗
PubMed2006

Extrinsic and intrinsic systems in the posterior cortex of the human brain revealed during natural sensory stimulation.

When exposing subjects to a continuous segment of an audiovisual movie, a large expanse of human cortex, especially in the posterior half of the cerebral cortex, shows stimulus-driven activity. However, embedded within this widespread activity, there are cortical regions whose activity is dissociated from the external stimulation. These regions are intercorrelated among themselves, forming a functional network, which largely overlaps with cortical areas previously shown to be deactivated by task-oriented paradigms. Moreover, the network of areas whose neuronal dynamics are associated with external inputs and the network of areas that appears to be intrinsically driven complement each other, providing coverage of most of the posterior cortex. Thus, we propose that naturalistic stimuli reveal a fundamental neuroanatomical partition of the human posterior cortex into 2 global networks: an "extrinsic" system, comprising areas associated with the processing of external inputs, and an "intrinsic" system, largely overlapping with the task-negative, default-mode network, comprising areas associated with--as yet not fully understood--intrinsically oriented functions.

Acoustic Stimulation↗
PubMed2001

Generalization in interactive networks: the benefits of inhibitory competition and Hebbian learning.

Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Error-driven backpropagation is computationally powerful and has proven useful for modeling a range of psychological data but is not biologically plausible. Several approaches to implementing backpropagation in a biologically plausible fashion converge on the idea of using bidirectional activation propagation in interactive networks to convey error signals. This article demonstrates two main points about these error-driven interactive networks: (1) they generalize poorly due to attractor dynamics that interfere with the network's ability to produce novel combinatorial representations systematically in response to novel inputs, and (2) this generalization problem can be remedied by adding two widely used mechanistic principles, inhibitory competition and Hebbian learning, that can be independently motivated for a variety of biological, psychological, and computational reasons. Simulations using the Leabra algorithm, which combines the generalized recirculation (GeneRec), biologically plausible, error-driven learning algorithm with inhibitory competition and Hebbian learning, show that these mechanisms can result in good generalization in interactive networks. These results support the general conclusion that cognitive neuroscience models that incorporate the core mechanistic principles of interactivity, inhibitory competition, and error-driven and Hebbian learning satisfy a wider range of biological, psychological, and computational constraints than models employing a subset of these principles.

Algorithms↗
PubMed2003

Integrative modelling of gene expression and cell metabolism.

The goal of modelling biochemical networks is to understand the system's behaviour (dynamics and control) of these networks in terms of the properties of the individual molecules. Most modelling approaches have dealt with either gene expression or cell metabolism. In light of the widespread use of robotic technologies in laboratories and improved computational power, it is now time to incorporate information from all biochemical levels--gene expression, protein interactions and metabolism--into integrated models. Here, we review the literature on modelling gene expression with cell metabolism. At the end we describe some analytic methods to deal with these systems.

Animals↗