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At least 1,333 records · Page 74Linked to original sources
PubMed2002

Dynamic organization of the actin system in the motile cells of Dictyostelium.

The actin system forms a supramolecular, membrane-associated network that serves multiple functions in Dictyostelium cells, including cell motility controlled by chemoattractant, phagocytosis, macropinocytosis, and cytokinesis. In executing these functions the monomeric G-actin polymerizes reversibly, and the actin filaments are assembled into membrane-anchored networks together with other proteins involved in shaping the networks and controlling their dynamics. Most impressive is the speed at which actin-based structures are built, reorganized, or disassembled. We used GFP-tagged coronin and Arp3, an intrinsic constituent of the Arp2/3 complex, as examples of proteins that are recruited to highly dynamic actin-filament networks. By fluorescence recovery after photobleaching (FRAP), average exchange rates of cell-cortex bound coronin were estimated. A nominal value of 5 s for half-maximal incorporation of coronin into the cortex, and a value of 7 s for half-maximal dissociation from cortical binding sites has been obtained. Actin dynamics implies also flow of F-actin from sites of polymerization to sites of depolymerization, i.e. to the tail of a migrating cell, the base of a phagocytic cup, and the cleavage furrow in a mitotic cell. To monitor this flow, we expressed in Dictyostelium cells a GFP-tagged actin-binding fragment of talin. This fragment (GFP-TalC63) translocates from the front to the tail during cell migration and from the polar regions to the cleavage furrow during mitotic cell division. The intrinsic dynamics of the actin system can be manipulated in vivo by drugs or other probes that act either as inhibitors of actin polymerization or as stabilizers of filamentous actin. In order to investigate structure-function relationships in the actin system, a technique of reliably arresting transient network structures is in demand. We discuss the potential of electron tomography of vitrified cells to visualize actin networks in their native association with membranes.

Actins↗
PubMed1993

An n-level field theory of biological neural networks.

An n-level field theory, based on the concept of "functional interaction", is proposed for a description of the continuous dynamics of biological neural networks. A "functional interaction" describes the action from one substructure of a network to another at several levels of organization, molecular, synaptic, and neural. Because of the continuous representation of neurons and synapses, which constitute a hierarchical system, it is shown that the property of non-locality leads to a non-local field operator in the field equations. In a hierarchical continuous system, the finite velocity of the functional interaction at the lower level implies non-locality at the higher level. Two other properties of the functional interaction are introduced in the formulation: the non-symmetry between sources and sinks, and the non-uniformity of the medium. Thus, it is shown that: (i) The coupling between topology and geometry can be introduced via two functions, the density of neurons at the neuronal level of organization, and the density-connectivity of synapses between two points of the neural space at the synaptic level of organization. With densities chosen as Dirac functions at regularly spaced points, the dynamics of a discrete network becomes a particular case of the n-level field theory. (ii) The dynamics at each of the molecular and synaptic lower level are introduced, at the next upper level, both in the source and in the non-local interaction of the field to integrate the dynamics at the neural level. (iii) New learning rules are deduced from the structure of the field equations: Hebbian rules result from strictly local activation; non-Hebbian rules result from homosynaptic activation with strict heterosynaptic effects, i.e., when an activated synaptic pathway affects the efficacy of a non-activated one; non-Hebbian rules and/or non-linearities result from the structure of the interaction operator and/or the internal biochemical kinetics.

Animals↗
PubMed2006

Bringing metabolic networks to life: convenience rate law and thermodynamic constraints.

BACKGROUND: Translating a known metabolic network into a dynamic model requires rate laws for all chemical reactions. The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes. RESULTS: We introduce a simple and general rate law called "convenience kinetics". It can be derived from a simple random-order enzyme mechanism. Thermodynamic laws can impose dependencies on the kinetic parameters. Hence, to facilitate model fitting and parameter optimisation for large networks, we introduce thermodynamically independent system parameters: their values can be varied independently, without violating thermodynamical constraints. We achieve this by expressing the equilibrium constants either by Gibbs free energies of formation or by a set of independent equilibrium constants. The remaining system parameters are mean turnover rates, generalised Michaelis-Menten constants, and constants for inhibition and activation. All parameters correspond to molecular energies, for instance, binding energies between reactants and enzyme. CONCLUSION: Convenience kinetics can be used to translate a biochemical network--manually or automatically--into a dynamical model with plausible biological properties. It implements enzyme saturation and regulation by activators and inhibitors, covers all possible reaction stoichiometries, and can be specified by a small number of parameters. Its mathematical form makes it especially suitable for parameter estimation and optimisation. Parameter estimates can be easily computed from a least-squares fit to Michaelis-Menten values, turnover rates, equilibrium constants, and other quantities that are routinely measured in enzyme assays and stored in kinetic databases.

Energy Metabolism↗
PubMed2005

Investigating neuronal activity with planar microelectrode arrays: achievements and new perspectives.

Neuronal networks underlie memory storage and information processing in the human brain, and ultimately participate in what Eccles referred to as "the creation of consciousness". Moreover, as physiological dysfunctions of neurons almost always translate into serious health issues, the study of the dynamics of neuronal networks has become a major avenue of research, as well as their response to pharmacological tampering. Planar microelectrode arrays represent a unique tool to investigate such dynamics and interferences, as they allow one to observe the activity of neuronal networks spread in both space and time. We will here review the major results obtained with microelectrode arrays and give an overview of the latest technological developments in the field, including our own efforts to develop the potential of this already powerful technology.

Microelectrodes↗
PubMed2004

Internal motility in stiffening actin-myosin networks.

We present a study on filamentous actin solutions containing heavy meromyosin subfragments of myosin II motor molecules. We focus on the viscoelastic phase behavior and internal dynamics of such networks during adenosine-triphosphate depletion. By combining microrheology and fluorescence microscopy, we observed a sol-gel transition accompanied by a sudden onset of directed filament motion. We interpret the sol-gel transition in terms of myosin II enzymology, and suggest a "zipping" mechanism to explain the filament motion in the vicinity of the sol-gel transition.

Actins↗
PubMed1997

Prediction of dynamic tendon forces from electromyographic signals: an artificial neural network approach.

Artificial neural networks (ANN) with a backpropagation algorithm were used to predict dynamic tendon forces from electromyographic (EMG) signals. To achieve this goal, tendon forces and EMG-signals were recorded simultaneously in the gastrocnemius muscle of three cats while walking and trotting at different speeds on a motor-driven treadmill. The quality of the tendon force predictions were evaluated for three levels of generalization. First, at the intrasession level, tendon force predictions were made for step cycles from the same experimental session as the step cycles which were used to train the ANN. At this level of generalization very good results were obtained. Second, at the intrasubject level, tendon force predictions were made for one cat walking at a given speed while the ANN was trained with data from the same animal walking at different speeds. For the intrasubject predictions, the quality of the results depended on the walking speed for which the predictions were made: for the speeds at the low and high extremes, the predictions were worse than for the intermediate speeds. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.78 to 0.91. Third, at the intersubject level, tendon forces were predicted for one animal walking at a given speed while the ANN was trained with data from the remaining two animals walking at the corresponding speed. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.72 to 0.98. It was concluded that the ANN-approach is a powerful technique to predict dynamic tendon forces from EMG-signals.

Animals↗
PubMed2006

The fundamental organization of cardiac mitochondria as a network of coupled oscillators.

Mitochondria can behave as individual oscillators whose dynamics may obey collective, network properties. We have shown that cardiomyocytes exhibit high-amplitude, self-sustained, and synchronous oscillations of bioenergetic parameters when the mitochondrial network is stressed to a critical state. Computational studies suggested that additional low-amplitude, high-frequency oscillations were also possible. Herein, employing power spectral analysis, we show that the temporal behavior of mitochondrial membrane potential (DeltaPsi(m)) in cardiomyocytes under physiological conditions is oscillatory and characterized by a broad frequency distribution that obeys a homogeneous power law (1/f(beta)) with a spectral exponent, beta = 1.74. Additionally, relative dispersional analysis shows that mitochondrial oscillatory dynamics exhibits long-term memory, characterized by an inverse power law that scales with a fractal dimension (D(f)) of 1.008, distinct from random behavior (D(f) = 1.5), over at least three orders of magnitude. Analysis of a computational model of the mitochondrial oscillator suggests that the mechanistic origin of the power law behavior is based on the inverse dependence of amplitude versus frequency of oscillation related to the balance between reactive oxygen species production and scavenging. The results demonstrate that cardiac mitochondria behave as a network of coupled oscillators under both physiological and pathophysiological conditions.

Animals↗
PubMed1996

A biologically inspired neural net for trajectory formation and obstacle avoidance.

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

Avoidance Learning↗
PubMed2005

Green's-function reaction dynamics: a particle-based approach for simulating biochemical networks in time and space.

We have developed a new numerical technique, called Green's-function reaction dynamics (GFRD), that makes it possible to simulate biochemical networks at the particle level and in both time and space. In this scheme, a maximum time step is chosen such that only single particles or pairs of particles have to be considered. For these particles, the Smoluchowski equation can be solved analytically using Green's functions. The main idea of GFRD is to exploit the exact solution of the Smoluchoswki equation to set up an event-driven algorithm, which combines in one step the propagation of the particles in space with the reactions between them. The event-driven nature allows GFRD to make large jumps in time and space when the particles are far apart from each other. Here, we apply the technique to a simple model of gene expression. The simulations reveal that spatial fluctuations can be a major source of noise in biochemical networks. The calculations also show that GFRD is highly efficient. Under biologically relevant conditions, GFRD is up to five orders of magnitude faster than conventional particle-based techniques for simulating biochemical networks in time and space. GFRD is not limited to biochemical networks. It can also be applied to a large number of other reaction-diffusion problems.

Algorithms↗
PubMed2006

Measuring asymmetric temporal interdependencies in simulated and biological networks.

We use a newly developed metric to characterize asymmetric temporal interdependencies in networks of coupled dynamical elements. We studied the formation of temporal ordering in a system of coupled Rossler oscillators for different connectivity ratios and network topologies and also applied the metric to investigate the functional structure of a biological network (cerebral ganglia of Helix snail). In the former example we show how the local ordering evolves to the global one as a function of structural parameters of the network, while in the latter we show spontaneous emergence of functional interdependence between two groups of electrodes.

Action Potentials↗
PubMedDate not supplied

Oscillatory activity in the neural networks of spiking elements.

We study the dynamics of activity in the neural networks of enhanced integrate-and-fire elements (with random noise, refractory periods, signal propagation delay, decay of postsynaptic potential, etc.). We consider the networks composed of two interactive populations of excitatory and inhibitory neurons with all-to-all or random sparse connections. It is shown by computer simulations that the regime of regular oscillations is very stable in a broad range of parameter values. In particular, oscillations are possible even in the case of very sparse and randomly distributed inhibitory connections and high background activity. We describe two scenarios of how oscillations may appear which are similar to Andronov-Hopf and saddle-node-on-limit-cycle bifurcations in dynamical systems. The role of oscillatory dynamics for information encoding and processing is discussed.

Action Potentials↗
PubMed2001

Methods and measures for the description of epidemiologic contact networks.

This article describes new methods to characterize epidemiologic contact networks that involve links that are being dynamically formed and dissolved. The new social network measures are designed with an epidemiologic interpretation in mind. These methods are intended to capture dynamic aspects of networks related to their potential to spread infection. This differs from many social network measures that are based on static networks. The networks are formulated as transmission graphs (TGs), in which nodes represent relationships between two individuals and directed edges (links) represent the potential of an individual in one relationship to carry infection to an individual in another relationship. Network measures derived from transmission graphs include "source counts," which are defined as the number of prior relationships that could potentially transmit infection to a particular node or individual.

Contact Tracing↗
PubMed2003

Detection of heterogeneities in dry and swollen polymer networks by proton low-field NMR spectroscopy.

We report on the implementation of modern proton multiple-quantum NMR methods for the characterization of molecular order and dynamics in polymer networks and melts on cost-efficient low-resolution low-field NMR instrumentation. The method permits the extraction of chain order parameter distributions, and is therefore sensitive to a heterogeneity of cross-links and other topological constraints. Data from samples with bimodal network chain length distributions acquired at 20 MHz (0.47 T) are in quantitative agreement with results obtained at high field (500 MHz). It is shown for the first time that the chain order distribution is broadened upon swelling, providing evidence for the nontrivial nature of the swelling process.

Journal Article↗
PubMed1998

Effect of delay on the boundary of the basin of attraction in a system of two neurons.

The behavior of neural networks may be influenced by transmission delays and many studies have derived constraints on parameters such as connection weights and output functions which ensure that the asymptotic dynamics of a network with delay remains similar to that of the corresponding system without delay. However, even when the delay does not affect the asymptotic behavior of the system, it may influence other important features in the system's dynamics such as the boundary of the basin of attraction of the stable equilibria. In order to better understand such effects, we study the dynamics of a system constituted by two neurons interconnected through delayed excitatory connections. We show that the system with delay has exactly the same stable equilibrium points as the associated system without delay, and that, in both the network with delay and the corresponding one without delay, most trajectories converge to these stable equilibria. Thus, the asymptotic behavior of the network with delay and that of the corresponding system without delay are similar. We obtain a theoretical characterization of the boundary separating the basins of attraction of two stable equilibria, which enables us to estimate the boundary. Our numerical investigations show that, even in this simple system, the boundary separting the basins of attraction of two stable equilibrium points depends on the value of the delays. The extension of these results to networks with an arbritrary number of units is discussed.

Journal Article↗
PubMed1999

Analog circuits for modeling biological neural networks: design and applications.

Computational neuroscience is emerging as a new approach in biological neural networks studies. In an attempt to contribute to this field, we present here a modeling work based on the implementation of biological neurons using specific analog integrated circuits. We first describe the mathematical basis of such models, then present analog emulations of different neurons. Each model is compared to its biological real counterpart as well as its numerical computation. Finally, we demonstrate the possible use of these analog models to interact dynamically with real cells through artificial synapses within hybrid networks. This method is currently used to explore neural networks dynamics.

Animals↗
PubMed1998

Activity in sparsely connected excitatory neural networks: effect of connectivity.

The Nucleus Tractus Solitarius (NTS) of the brainstem contains a neural circuit with only excitatory connections displaying a spontaneous activity involved in the control of respiration. A model of a network with random connections is presented and is used to investigate a possible mechanism of spontaneous activity generation consisting of the amplification of a low-background activity by the excitatory connections. First, the steady states of the network model and its ability to amplify the activity are studied. Then, a low-background activity is introduced, and dynamics of simulated networks are examined. Low-tonic, slow-phasic and fast-tonic activities are successively observed when the mean number K of connections per neuron increases. The transition between the two first types of activity is progressive whereas the transition from slow-phasic to fast-tonic activity is sharp. Simulation results show that activities of low frequency can be obtained with the proposed mechanism of spontaneous activity generation only if the network connectivity is low.

Journal Article↗
PubMed1997

Frustrated chaos in biological networks.

The behaviour of three biologically inspired networks is investigated: Immune Idiotypic Network (IIN), Hopfield Network (HN) and Coupled Map Lattice (CML), mainly the effect on the dynamics induced by connecting the network in a frustrated way. Frustration occurs when the global structure is such that local connectivity patterns responsible for stable behaviour are intertwined, leading to mutually competing attractors and chaotic itinerancy among brief appearance of these attractors. Frustration destabilizes the network and provokes an unpredictable "wavering" among the stable dynamic regimes which characterize the same network when it is interconnected in a non-frustrated way. As the main contribution of this paper, an immune idiotypic network in which the prevailing behaviour is oscillatory is studied in detail. It is shown how connecting an elementary three-clone network in a frustrating way transforms the oscillatory regime into a chaotic one. This chaotic regime is further analysed and several interesting aspects are discussed such as the variable homogeneity, the intrinsic chaotic itinerancy among brief oscillatory regimes and the strong unpredictability. In addition, dynamical regimes obtained by frustrating the connectivity of HN and CML are presented and the similarities as well as the differences with the IIN dynamics are emphasized. Common to all these networks is the description of the frustrated chaos as a succession of attempts to relax the network into one of the oscillatory regimes given by a weaker and non-frustrated connectivity, an impossible achievement making the dynamics rambling over brief but repelling orbits.

Animals↗
PubMed2002

Induction of rapid and reversible cytokeratin filament network remodeling by inhibition of tyrosine phosphatases.

The cytokeratin filament network is intrinsically dynamic, continuously exchanging subunits over its entire surface, while conferring structural stability on epithelial cells. However, it is not known how cytokeratin filaments are remodeled in situations where the network is temporarily and spatially restricted. Using the tyrosine phosphatase inhibitor orthovanadate we observed rapid and reversible restructuring in living cells, which may provide the basis for such dynamics. By examining cells stably expressing fluorescent cytokeratin chimeras, we found that cytokeratin filaments were broken down and then formed into granular aggregates within a few minutes of orthovanadate addition. After drug removal, gradual reincorporation of granules into the filament network was observed for aggregates that were either part of residual filaments or stayed in close apposition to remaining filaments. Even when cytokeratin filaments were no longer detectable, granules with low mobility were still able to reestablish a cytokeratin filament network. This process took less than 30 minutes and occurred at multiple foci throughout the cytoplasm without apparent correlation to alterations in the actin- and tubulin-based systems. Interestingly, the short-lived and rather small orthovanadate-induced cytokeratin granules contained the cytoskeletal crosslinker plectin but lacked the cytokeratin-solubilising 14-3-3 proteins. By contrast, the long-lived and larger cytokeratin aggregates generated after treatment with the serine/threonine phosphatase inhibitor okadaic acid were negative for plectin but positive for 14-3-3 proteins. Taken together, our observations in living orthovanadate-treated interphase cells revealed modes of cytokeratin remodeling that qualify as basic mechanisms capable of rapidly adapting the cytokeratin filament cytoskeleton to specific requirements.

14-3-3 Proteins↗