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At least 343 records · Page 19Linked to original sources
PubMed1998

Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis.

We present a network model and its dynamic analysis for the regulatory relationships among 11 genes that participate in Arabidopsis thaliana flower morphogenesis. The topology of the network and the relative strengths of interactions among these genes were based from published genetic and molecular data, mainly relying on mRNA expression patterns under wild type and mutant backgrounds. The network model is made of binary elements and we used a particular dynamic implementation for the network that we call semi-synchronic. Using this method the network reaches six attractors; four of them correspond to observed patterns of gene expression found in the floral organs of Arabidopsis (sepals, petals, stamens and carpels) as predicted by the ABC model of flower morphogenesis. The fifth state corresponds to cells that are not competent to flowering, and the sixth attractor predicted by the model is never found in wild-type plants, but it could be induced experimentally. We discuss the biological implications and the potential use of this network modeling approach to integrate functional data of regulatory genes of plant development.

Arabidopsis↗
PubMed1999

Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis.

The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.

Anesthesia↗
PubMed2000

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks.

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells.

Animals↗
PubMed1992

Steady state dynamics of intermediate filament networks.

We have conducted experiments to examine the dynamic exchange between subunit and polymer of vimentin intermediate filaments (IF) at steady state through the use of xrhodamine-labeled vimentin in fluorescence recovery after photobleaching (FRAP) analysis. The xrhodamine-vimentin incorporated into the endogenous vimentin IF network after microinjection into fibroblasts and could be visualized with a cooled charge-coupled device (CCD) camera and digital imaging fluorescence microscopy. Bar shaped regions were bleached in the fluorescent IF network using a beam from an argon ion laser and the cells were monitored at various times after bleaching to assess recovery of fluorescence in the bleached zones. We determined that bleached vimentin fibers can recover their fluorescence over relatively short time periods. Vimentin fibers in living cells also can exhibit significant movements, but the recovery of fluorescence was not dependent upon movement of fibers. Fluorescence recovery within individual fibers did not exhibit any marked polarity and was most consistent with a steady state exchange of vimentin subunits along the lengths of IF.

3T3 Cells↗
PubMed2025

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma.

Post-traumatic stress (PTS) encompasses a range of psychological responses following trauma, which may lead to more severe outcomes such as post-traumatic stress disorder (PTSD). Identifying early neuroimaging biomarkers that link brain function to PTS outcomes is critical for understanding PTSD risk. This longitudinal study examines the association between brain dynamic functional network connectivity (dFNC) and current/future PTS symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.197, p corrected = 0.0079) and future (r=-0.176, p corrected = 0.0176) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.205, p corrected = 0.0079). We additionally observed that the association between the network dynamics of the inter-network and intra-network brain state with symptom severity is more pronounced in female group. Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger effect in female group, underscoring the importance of sex differences.

Journal Article↗
PubMed1991

Molecular dynamics simulation on a network of workstations using a machine-independent parallel programming language.

Molecular dynamics simulations investigate local and global motion in molecules. Several parallel computing approaches have been taken to attack the most computationally expensive phase of molecular simulations, the evaluation of long range interactions. This paper develops a straightforward but effective algorithm for molecular dynamics simulations using the machine-independent parallel programming language, Linda. The algorithm was run both on a shared memory parallel computer and on a network of high performance Unix workstations. Performance benchmarks were performed on both systems using two proteins. This algorithm offers a portable cost-effective alternative for molecular dynamics simulations. In view of the increasing numbers of networked workstations, this approach could help make molecular dynamics simulations more easily accessible to the research community.

Algorithms↗
PubMed1993

The dynamics of sparse random networks.

Recurrent neural networks with full symmetric connectivity have been extensively studied as associative memories and pattern recognition devices. However, there is considerable evidence that sparse, asymmetrically connected, mainly excitatory networks with broadly directed inhibition are more consistent with biological reality. In this paper, we use the technique of return maps to study the dynamics of random networks with sparse, asymmetric connectivity and nonspecific inhibition. These networks show three qualitatively different kinds of behavior: fixed points, cycles of low period, and extremely long cycles verging on aperiodicity. Using statistical arguments, we relate these behaviors to network parameters and present empirical evidence for the accuracy of this statistical model. The model, in turn, leads to methods for controlling the level of activity in networks. Studying random, untrained networks provides an understanding of the intrinsic dynamics of these systems. Such dynamics could provide a substrate for the much more complex behavior shown when synaptic modification is allowed.

Animals↗
PubMed1992

Molecular dynamics simulation on a network of workstations using a machine-independent parallel programming language.

Molecular dynamics simulations investigate local and global motion in molecules. Several parallel computing approaches have been taken to attack the most computationally expensive phase of molecular simulations, the evaluation of long range interactions. This paper reviews these approaches and develops a straightforward but effective algorithm using the machine-independent parallel programming language, Linda. The algorithm was run both on a shared memory parallel computer and on a network of high performance Unix workstations. Performance benchmarks were performed on both systems using two proteins. This algorithm offers a portable cost-effective alternative for molecular dynamics simulations. In view of the increasing numbers of networked workstations, this approach could help make molecular dynamics simulations more easily accessible to the research community.

Algorithms↗
PubMed2002

A versatile ODE approximation to a network model for the spread of sexually transmitted diseases.

We develop a moment closure approximation (MCA) to a network model of sexually transmitted disease (STD) spread through a steady/casual partnership network. MCA has been used previously to approximate static, regular lattices, whereas application to dynamic, irregular networks is a new endeavour, and application to sociologically-motivated network models has not been attempted. Our goals are 1). to investigate issues relating to the application of moment closure approximations to dynamic and irregular networks, and 2). to understand the impact of concurrent casual partnerships on STD transmission through a population of predominantly steady monogamous partnerships. We are able to derive a moment closure approximation for a dynamic irregular network representing sexual partnership dynamics, however, we are forced to use a triple approximation due to the large error of the standard pair approximation. This example underscores the importance of doing error analysis for moment closure approximations. We also find that a small number of casual partnerships drastically increases the prevalence and rate of spread of the epidemic. Finally, although the approximation is derived for a specific network model, we can recover approximations to a broad range of network models simply by varying model parameters which control the structure of the dynamic network. Thus our moment closure approximation is very flexible in the kinds of network models it can approximate.

Computer Simulation↗
PubMed1997

Radar image segmentation using self-adapting recurrent networks.

This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.

Doppler Effect↗
PubMed1993

The dynamic clamp: artificial conductances in biological neurons.

The dynamic clamp is a novel method that uses computer simulation to introduce conductances into biological neurons. This method can be used to study the role of various conductances in shaping the activity of single neurons, or neurons within networks. The dynamic clamp can also be used to form circuits from previously unconnected neurons. This approach makes computer simulation an interactive experimental tool, and will be useful in many applications where the role of synaptic strengths and intrinsic properties in neuronal and network dynamics is of interest.

Animals↗
PubMed2001

Water and the cytoskeleton.

The diffusion of intracellular fluid and solutes is mainly limited by the density and the geometry of crossbridges between cytoskeletal polymers mediating the formation of an integrated cytoplasmic scaffold. Evidence for specific relationships between water and cytoskeletal polymers arises from the effect of heavy water on their polymerization process in vitro and on the cytoskeleton of living cells. The hydration of cytoskeletal subunits is modified through polymerization, a mechanism which may be involved in the direct contribution of the cytoskeleton to the osmotic properties of cells together with changes of hydration of polymers within networks. The dynamic properties of the hydration layer of cytoskeletal polymers may reflect the repetitive distribution of the surface charges of subunits within the polymer lattice, thus inducing a local and long range ordering of the diffusion flows of water and solutes inside polymer networks. The interactions between subunits in protofilaments and between protofilaments determine the specific viscoelastic properties of each type of polymer, regulated by associated proteins, and the mechanical properties of the cell through the formation of bundles and gels. Individual polymers are interconnected into dynamic networks through crossbridging by structural associated proteins and molecular motors, the activity of which involves cooperative interactions with the polymer lattice and likely the occurence of coordinated modifications of the hydration layer of the polymer surface. The cytoskeletal polymers are polyelectrolytes which constitute a large intracellular surface of condensed anionic charges and form a buffering structure for the sequestration of cations involved in the regulation of intracellular events. This property allows also the association of cytoplasmic enzymes and multimolecular complexes with the cytoskeleton, facilitating metabolic channelling and the localization of these complexes in specific subdomains of the cytoplasm. The consequences of interactions between membranes and the cytoskeleton in all cellular compartments range from the local immobilization and clustering of lipids and membrane proteins to the regulation of water and ion flows by the association of cytoskeletal subunits or polymers with transmembrane channels. The possibility that the polyelectrolyte properties of the cytoskeletal polymers contribute to the modulation of membrane potentials supports the hypothesis of a direct involvement of the cytoskeleton in intercellular communications.

Actins↗
PubMed2000

Dynamic properties of hydrogen-bonded networks in supercritical water

Dynamic properties of supercritical water at temperatures between 573 and 773 K and densities between 0.49 and 0.83 g/cm(3) have been investigated by molecular dynamics simulation and compared to states located on the vapor-liquid coexistence curve. A flexible simple point charge potential has been assumed for interactions in the subcritical states, whereas a reparameterization of that model has been performed to model the supercritical states. The hydrogen bonding structure and the diffusion coefficients in an ensemble of simulated states were previously analyzed and a good agreement with available experimental data was found. Dynamic properties of hydrogen bonding like the bond lifetimes and the influence of hydrogen bonds in the vibrational spectra are discussed along a range of simulation conditions. A nonlinear behavior of the hydrogen-bond lifetime as a function of temperature is observed in subcritical water whereas a linear dependence is found in supercritical water. Special attention is paid to the intermolecular vibrational spectrum (10-400 cm(-1)). It has been observed that the mode centered at 200 cm(-1), attributed to the intermolecular O-O stretching vibration in the ambient state remains active in the supercritical states.

Journal Article↗
PubMed2002

Pattern storage and processing in attractor networks with short-time synaptic dynamics.

Neurophysiological experiments show that the strength of synaptic connections can undergo substantial changes on a short time scale. These changes depend on the history of the presynaptic input. Using mean-field techniques, we study how short-time dynamics of synaptic connections influence the performance of attractor neural networks in terms of their memory capacity and capability to process external signals. For binary discrete-time as well as for firing rate continuous-time neural networks, the fixed points of the network dynamics are shown to be unaffected by synaptic dynamics. However, the stability of patterns changes considerably. Synaptic depression turns out to reduce the storage capacity. On the other hand, synaptic depression is shown to be advantageous for processing of pattern sequences. The analytical results on stability, size of the basins of attraction and on the switching between patterns are complemented by numerical simulations.

Algorithms↗
PubMed1994

Optimization dynamics for partitioned neural networks.

Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.

Kinetics↗
PubMed1995

Tomographic electroencephalography/magnetoencephalography. Dynamics of human neural network switching.

New developments in multimodal registration of electroencephalography (EEG), magnetic resonance imaging (MRI), and positron emission tomography (PET) are presented as a method to create a tomographic EEG. Three-dimensional information about the x,y,z location of the sources of event-related potentials is corroborated through the use of experimental design and coregistration with MRI and PET. Once the three-dimensional location of event-related potential dipole sources is identified and corroborated, pseudoinverse procedures are used to derive a new EEG voltage sequence from each of the dipoles. Each derived EEG dipole time series is analogous to recording EEG from a deeply implanted electrode and constitutes a four-dimensional tomographic EEG (i.e., three-dimensional space plus time). EEG coherence and phase analyses are then performed on the dipole-derived time series to study the temporal and spatial dynamics of neural network switching during voluntary finger movements. The purpose of this article is to demonstrate a new method to exploit the time domain dynamics of neural network switching in behaving human subjects.

Brain↗
PubMed1995

Computing with dynamic attractors in neural networks.

In this paper we report on some new architectures for neural computation, motivated in part by biological considerations. One of our goals is to demonstrate that it is just as easy for a neural net to compute with arbitrary attractors--oscillatory or chaotic--as with the more usual asymptotically stable fixed points. The advantages (if any) of such architectures are currently being investigated; but it seems reasonable that the much richer dynamics of recurrent networks, so obvious in recordings of brain activity, must be useful for something. On the other hand, the constraints of computing with biological wet-ware may make chaotic dynamics unavoidable in complex nervous systems. We hypothesize also that the as yet unrivaled capabilities of the human brain derive from an ability to integrate both analog intuitive pattern recognition operations, and digital symbolic logical operations at the ground level of its hardware. To investigate these possibilities, we have constructed a parallel distributed processing architecture inspired by the structure and dynamics of cerebral cortex. The construction assumes that cortex is a set of coupled associative memories with dynamic attractors. It is guided also by a particular concept of the physical structure required of macroscopic computational systems in general for reliable computation in the presence of noise. Our challenge is to accomplish real tasks that brains can do, using ordinary differential equations, in networks that are as faithful as possible to the known anatomy and dynamics of cortex.

Algorithms↗