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At least 775 records · Page 43Linked to original sources
PubMed2026

Proteome-wide Ubiquitinome Profiling Reveals Substrate-specific Dynamics Within the USP7 Network.

USP7 is a pleiotropic deubiquitylating enzyme that is involved in tumor suppression, (neuro) development, chromatin regulation and the DNA damage response. How USP7 regulates these diverse pathways is still unclear. Here, we report data-independent acquisition and label free quantitation mass spectrometry to profile the proteome-wide impact of USP7 on substrate de-ubiquitylation and overall protein abundance. First, we identified proteins associated with endogenous USP7 by immunopurification followed by data-independent acquisition and label free quantitation mass spectrometry. Integration of our new results with earlier interactomes of epitope-tagged USP7 yielded a consensus set of high-confidence protein targets. Domain mapping analysis revealed that, in addition to the TRAF domain, the ubiquitin-like domains of USP7 play a key role in substrate selection. Using specific enrichment of tryptic K-ε-GG peptides, we mapped proteome-wide changes in ubiquitinome dynamics following inhibition of USP7. Combining unbiased proteome-wide and targeted quantitative mass spectrometry revealed that deubiquitylation by USP7 can have different effects on the stability of distinct substrates, and suggests that USP7's activity profile is substrate-dependent rather than an intrinsic enzymatic property. Thus, in addition to providing a proteome-wide map of USP7 target sites, our multi-angle proteomics approach reveals that the effects of USP7-mediated deubiquitylation on its targets are remarkably variable and substrate-specific. Finally, based on these detailed molecular insights we show how USP7 connects various neurodevelopmental syndromes and tumor suppression pathways.

Ubiquitin-Specific Peptidase 7↗
PubMed2006

EEG dynamics of the frontoparietal network during reaching preparation in humans.

Visuomotor transformation processes are essential when accurate reaching movements towards a visual target have to be performed. In contrast, those transformations are not needed for similar, but non-visually guided, arm movements. According to previous studies, these transformations are carried out by neuronal populations located in the parietal and frontal cortical areas (the so-called "dorsal visual stream"). However, it is still debated whether these processes are mediated by the sequential and/or parallel activation of the frontoparietal areas. To investigate this issue, we designed a task where the same visual cue could represent either the target of a reaching/pointing movement or the go-signal for a similar but non-targeting arm movement. By subtracting the event-related potentials (ERPs) recorded from healthy subjects performing the two conditions, we identified the brain processes underlying the visuomotor transformations needed for accurate reaching/pointing movements. We then localized the generators by means of cortical current density (CCD) reconstruction and studied their dynamics from visual cue presentation to movement onset. The results showed simultaneous activation of the parietal and frontal areas from 140 to 260 ms. The results are interpreted as neural correlates of two critical phases of visuomotor integration, namely target selection and movement selection. Our findings suggest that the visuomotor transformation processes required for correct reaching/pointing movements do not rely on a purely sequential activation of the frontoparietal areas, but mainly on a parallel information processing system, where feedback circuits play an important role before movement onset.

Adult↗
PubMedDate not supplied

Phase dynamics of complex-valued neural networks and its application to traffic signal control.

Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.

Brain↗
PubMed1997

Assessment of quantitative artificial neural network analysis in a metabolically dynamic ex vivo 31P NMR pig liver study.

Quantitative artificial neural network analysis for 1550 ex vivo 31P nuclear magnetic resonance spectra from hypothermically reperfused pig livers was assessed. These spectra show wide ranges of metabolite concentrations and have been analyzed using metabolite prior knowledge based lineshape fitting analysis which had proved robust in its biochemical interpretation. This finding provided a good opportunity to assess the performance of artificial neural network analysis in a biochemically complex situation. The results showed high correlations (0.865 < or = R < or = 0.992) between the lineshape fitting and artificial neural network analysis for the metabolite values, and the artificial neural network analysis was able to fully represent the trends in the metabolic fluctuations during the experiments.

Animals↗
PubMed1994

Immune networks modeled by replicator equations.

In order to evaluate the role of idiotypic networks in the operation of the immune system a number of mathematical models have been formulated. Here we examine a class of B-cell models in which cell proliferation is governed by a non-negative, unimodal, symmetric response function f (h), where the field h summarizes the effect of the network on a single clone. We show that by transforming into relative concentrations, the B-cell network equations can be brought into a form that closely resembles the replicator equation. We then show that when the total number of clones in a network is conserved, the dynamics of the network can be represented by the dynamics of a replicator equation. The number of equilibria and their stability are then characterized using methods developed for the study of second-order replicator equations. Analogies with standard Lotka-Volterra equations are also indicated. A particularly interesting result of our analysis is the fact that even though the immune network equations are not second-order, the number and stability of their equilibria can be obtained by a superposition of second-order replicator systems. As a consequence, the problem of finding all of the equilibrium points of the nonlinear network equations can be reduced to solving linear equations.

Animals↗
PubMed2001

Symbolic dynamics and computation in model gene networks.

We analyze a class of ordinary differential equations representing a simplified model of a genetic network. In this network, the model genes control the production rates of other genes by a logical function. The dynamics in these equations are represented by a directed graph on an n-dimensional hypercube (n-cube) in which each edge is directed in a unique orientation. The vertices of the n-cube correspond to orthants of state space, and the edges correspond to boundaries between adjacent orthants. The dynamics in these equations can be represented symbolically. Starting from a point on the boundary between neighboring orthants, the equation is integrated until the boundary is crossed for a second time. Each different cycle, corresponding to a different sequence of orthants that are traversed during the integration of the equation always starting on a boundary and ending the first time that same boundary is reached, generates a different letter of the alphabet. A word consists of a sequence of letters corresponding to a possible sequence of orthants that arise from integration of the equation starting and ending on the same boundary. The union of the words defines the language. Letters and words correspond to analytically computable Poincare maps of the equation. This formalism allows us to define bifurcations of chaotic dynamics of the differential equation that correspond to changes in the associated language. Qualitative knowledge about the dynamics found by integrating the equation can be used to help solve the inverse problem of determining the underlying network generating the dynamics. This work places the study of dynamics in genetic networks in a context comprising both nonlinear dynamics and the theory of computation. (c) 2001 American Institute of Physics.

Journal Article↗
PubMed2000

Local Structure Evolution in Particle Network Formation Studied by Brownian Dynamics Simulation.

The effect of solid content and colloidal interactions on the structure of forming networks of colloidal particles is studied by Brownian dynamics simulation. The different situations are compared in terms of the pair distribution function and the distribution of nearest neighbors around each particle. The results indicate that, in fast coagulation, the higher solid contents lead to a freezing-in of the liquid structure. Nevertheless, this effect can be reduced substantially by the introduction of a shallow secondary minimum and an energy barrier in the interaction potential. However, the structures resulting from such slow coagulation show a substantial degree of porosity, larger than those produced at the same solid content but by fast coagulation. It is also shown how the porosity (defined on a few particle diameters) is reflected in the distribution of nearest neighbors around the center particle, i.e., the very local conformation in the particle network. Fractal analysis shows that, at the relatively high volume fractions considered in this study, no intermediate fractal regime exists. Copyright 2000 Academic Press.

Journal Article↗
PubMed1991

Diversity in motile responses of human neutrophil granulocytes: functional meaning and cytoskeletal basis.

Different agonists induce motility and shape changes, but only a specific polarized shape is correlated with directed migration. An intact and dynamic actin network appears to be important for motility and migration. Motility is usually associated with an increased level of F-actin, and a specific location of F-actin into surface protrusions. For locomotion, a specific location of F-actin, rather than a large net increase in F-actin appears to be of importance. Three major groups of responses can be distinguished on the basis of the type of shape changes, functional activity and organization of F-actin. 1. Agents capable of polarizing cells, such as chemotactic peptides, and microtubule-disassembling agents elicit, at appropriate concentrations, a marked chemokinetic response, but little if any fluid pinocytosis. F-actin shows a polar location, being concentrated mainly in the protrusions at the leading front. Chemotactic peptide also induces an increase in the level of F-actin and cytoskeleton-associated actin. It is, however, not clear if front-tail polarity and locomotion, induced by chemotactic peptide after longer time of stimulation, correlate with an actual increase in the level of cytoskeleton-associated actin. 2. Activators of protein kinase C such as PMA and diacylglycerols, induce nonpolar cells with surface projections. PMA and diacylglycerols stimulate pinocytosis substantially. All three agents tend to inhibit locomotion or chemotaxis as an immediate response. They also increase the percentage of cytoskeletal actin, and induce an enrichment of F-actin in surface projections. 3. Circus movement may occur in response to D20. These cells show little or no stimulation of locomotion or pinocytosis. Thus the functional significance of this motor response remains to be elucidated. We conclude that different agonists can induce motility and shape changes, but not necessarily chemotaxis. Only a polarized shape is correlated with directed locomotion. An intact and dynamic actin network appears to be important for motility including locomotion. Motility is usually associated with an increased level of F-actin, and a specific location of F-actin into surface protrusions. The actin-associated proteins alpha-Actinin, myosin and actin-binding protein appear also to be important for pseudopod formation. For locomotion, a specific location of F-actin, rather than a large net increase in F-actin may be of importance.

Actins↗
PubMed2004

Robust redesign of a neural network controller in the presence of unmodeled dynamics.

This paper presents a neural network control redesign, which achieves robust stabilization in the presence of unmodeled dynamics restricted to be input to output practically stable (IOpS), without requiring any prior knowledge on any bounding function. Moreover, the state of the unmodeled dynamics is permitted to go unbounded provided that the nominal system state and/or the control input also go unbounded. The neural network controller is equipped with a resetting strategy to deal with the problem of possible division by zero, which may appear since we consider unknown input vector fields with unknown signs. The uniform ultimate boundedness of the system output to an arbitrarily small set, plus the boundedness of all other signals in the closed-loop is guaranteed.

Algorithms↗
PubMed1999

Radial basis function neural networks for the characterization of heart rate variability dynamics.

This study introduces new neural network based methods for the assessment of the dynamics of the heart rate variability (HRV) signal. The heart rate regulation is assessed as a dynamical system operating in chaotic regimes. Radial-basis function (RBF) networks are applied as a tool for learning and predicting the HRV dynamics. HRV signals are analyzed from normal subjects before and after pharmacological autonomic nervous system (ANS) blockade and from diabetic patients with dysfunctional ANS. The heart rate of normal subjects presents notable predictability. The prediction error is minimized, in fewer degrees of freedom, in the case of diabetic patients. However, for the case of pharmacological ANS blockade, although correlation dimension approaches indicate significant reduction in complexity, the RBF networks fail to reconstruct adequately the underlying dynamics. The transient attributes of the HRV dynamics under the pharmacological disturbance is elucidated as the explanation for the prediction inability.

Atropine↗
PubMed2003

Dynamic synchronization and chaos in an associative neural network with multiple active memories.

Associative memory dynamics in neural networks are generally based on attractors. Retrieval based on fixed-point attractors works if only one memory pattern is retrieved at the time, but cannot enable the simultaneous retrieval of more than one pattern. Stable phase-locking of periodic oscillations or limit cycle attractors leads to incorrect feature bindings if the simultaneously retrieved patterns share some of their features. We investigate retrieval dynamics of multiple active patterns in a network of chaotic model neurons. Several memory patterns are kept simultaneously active and separated from each other by a dynamic itinerant synchronization between neurons. Neurons representing shared features alternate their synchronization between patterns, thus multiplexing their binding relationships. Our model includes a mechanism for self-organized readout or decoding of memory pattern coherence in terms of short-term potentiation and short-term depression of synaptic weights.

Animals↗
PubMedDate not supplied

The influence of morphological regularities on the dynamics of a connectionist network.

The effects of morphological regularities on the behavior of connectionist networks were studied by training identical networks on orthographic-semantic mappings that either contained such regularities or did not. Morphological regularities had a substantial impact on both the amount of training needed to learn a mapping and the number of words that could be included in the training set. A variety of analyses demonstrated how morphological regularities structure the organization and componentiality of a network's internal representations.

Cognition↗
PubMed2002

Effect of capillary element aspect ratio on the dynamic imbibition within porous networks.

The Washburn equation is widely accepted for describing capillary imbibition. It has, however, been shown to be insufficient at very short times due partly to the lack of inertial terms. Bosanquet (C. H. Bosanquet, Philos. Mag. ser. 645, 525 (1923)) applied an inertial term via momentum, Szekely et al. (J. Szekely, A. W. Neumann, and Y. K. Chang, J. Colloid Interface Sci.35, 273 (1971)) examined single capillaries based on a revised boundary-condition model, and Sorbie et al. (K. S. Sorbie, Y. Z. Wu, and S. R. McDougall, J. Colloid Interface Sci. 289 (1995)) reviewed and applied Szekely's work to examine the effects of comparative imbibition into a parallel pore doublet. The study here extends the work of Sorbie et al. by applying the equation of Bosanquet to a three-dimensional network model, Pore-Cor. All authors agree that, with the inclusion of inertial terms at short times, smaller radius capillaries will initially fill faster than larger radius capillaries which disagrees with the Washburn equation. It is shown that the aspect ratio of a capillary, defined as its length divided by its radius, plays an important role, in combination with the capillary radii themselves, in determining the filling rate of individual elements. The distribution of this ratio associated with the capillary throat elements within a network structure is investigated. The result is that a preferred pathway of permeation is observed under supersource imbibition conditions in the case where a broad size distribution of capillary elements occurs within a network structure.

Journal Article↗
PubMed1982

Dynamic connections in neural networks.

Massively parallel (neural-like) networks are receiving increasing attention as a mechanism for expressing information processing models. By exploiting powerful primitive units and stability-preserving construction rules, various workers have been able to construct and test quite complex models, particularly in vision research. But all of the detailed technical work was concerned with the structure and behavior of fixed networks. The purpose of this paper is to extend the methodology to cover several aspects of change and memory.

Memory↗
PubMed1987

On the dynamics of controlled metabolic network and cellular behaviour.

The existence of elaborate control mechanisms for the various biochemical processes inside and within living cells is responsible for the coherent behaviour observed in its spatio-temporal organisation. Stability and sensitivity are both necessary properties of living systems and these are achieved through negative and positive feedback loops as in other control systems. We have studied a three-step reaction scheme involving a negative and a positive feedback loop in the form of end-product inhibition and allosteric activation. The variety of behaviour exhibited by this system, under different conditions, includes steady state, simple limit cycle oscillations, complex oscillations and period bifurcations leading to random oscillations or chaos. The system also shows the existence of two distinct chaotic regimes under the variation of a single parameter. These results, in comparison with single biochemical control loops, show that new behaviours can be exhibited in a more complex network which are not seen in the single control loops. The results are discussed in the light of a diverse variety of cellular functions in normal and altered cells indicating the role of controlled metabolic network as the underlying basis for cellular behaviour.

Biological Evolution↗