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At least 397 records · Page 22Linked to original sources
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↗
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↗
PubMed2001

Dynamics of neural networks: a proposed mechanism to account for changes in clinical symptomatology through time in patients with psychotic diseases.

The classical Kraepelinean dichotomy between manic depressive insanity and the schizophrenias has been recently challenged from clinical and neurobiological quarters. It is not so infrequent to see patients shift from a manic to a schizophrenic symptomatology and vice versa. This paper proposes neurobiological mechanisms as to how these changes may occur, based on recent data on the functioning of neural networks at different modes.

Humans↗
PubMed1992

Evolution of host-parasitoid network through homeochaotic dynamics.

Host-parasitoid systems with evolving mutation rates are studied. By increasing the growth rate of hosts, the diversity of both species is maintained dynamically. For the lower growth rate, diversity is brought about by mere parasitism. The average mutation rate for parasites is elevated to a high value, while that for hosts is suppressed at a low level. For the higher growth rate, the mutation rates for both hosts and parasites are elevated to form a symbiotic cluster connected by on-going mutation. This symbiotic state is sustained through a chaotic oscillation keeping some coherency among species. For a flat landscape for hosts, dynamical clustering of oscillation is observed. Lyapunov spectra of such oscillations show that high dimensional chaos with small positive exponents underlies in the symbiotic state. This weak high dimensional chaos, termed "homeochaos," is essential to the maintenance of symbiosis in ecosystems.

Journal Article↗
PubMed2000

More realistic models of sexually transmitted disease transmission dynamics: sexual partnership networks, pair models, and moment closure.

BACKGROUND: Mathematical models of sexually transmitted disease transmission have proven powerful tools for interpreting observed epidemiologic pattern. However, the most commonly used formulation of such models largely fail to capture the effect of partnership concurrency and contact network structure on transmission. GOAL: The development of a compartmental model of partnership formation and dissolution that includes approximations for the influence of the sexual-partner network. STUDY DESIGN: Theoretical analysis of ordinary differential equation models for sexually transmitted disease transmission within sex-partner networks. RESULTS: The approach developed advances earlier pair models, allows for the influence of concurrent sexual partnerships, and illustrates the importance of concurrency to the persistence of diseases with relatively short durations of infectiousness. The authors also illustrate that heterogeneity in risk is possible even in model populations in which all individuals follow the same behavioral rules. CONCLUSION: Deterministic extended pair models offer a powerful approach to modelling sexually transmitted disease transmission that usefully complement computationally intensive microsimulation models.

Female↗
PubMed2002

Information space dynamics for neural networks.

We propose a coupled map lattice defined on a hypercube in M dimensions, the information space, to model memory retrieval by a neural network. We consider that both neuronal activity and the spiking phase may carry information. In this model the state of the network at a given time t is completely determined by a function y(sigma-->,t) of the bit strings sigma-->=(sigma1,sigma2,...,sigmaM), where sigma(i)=+/-1 with i=1,2, ...,M, that gives the intensity with which the information sigma--> is being expressed by the network. As an example, we consider logistic maps, coupled in the information space, to describe the evolution of the intensity function y(sigma-->,t). We propose an interpretation of the maps in terms of the physiological state of the neurons and the coupling between them, obtain Hebb-like learning rules, show that the model works as an associative memory, numerically investigate the capacity of the network and the size of the basins of attraction, and estimate finite size effects. We finally show that the model, when exposed to sequences of uncorrelated stimuli, shows recency and latency effects that depend on the noise level, delay time of measurement, and stimulus intensity.

Journal Article↗
PubMed2004

Extreme fluctuations in small-world networks with relaxational dynamics.

We study the distribution and scaling of the extreme height fluctuations for Edwards-Wilkinson-type relaxation on small-world substrates. When random links are added to a one-dimensional lattice, the average size of the fluctuations becomes finite (synchronized state) and the extreme height diverges only logarithmically in the large system-size limit. This latter property ensures synchronization in a practical sense in small-world coupled multi-component autonomous systems. The statistics of the extreme heights is governed by the Fisher-Tippett-Gumbel distribution.

Journal Article↗