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At least 955 records · Page 53Linked to original sources
PubMed1993

Effects of collateral inhibition in a model of the immature rat cerebellar cortex: multineuron correlations.

A model of the immature rat cerebellar cortex is used to simulate the effect of the inhibitory recurrent collateral axons of the Purkinje cells on the spike trains in the network. Inhibition induces an important overall change in the statistical characteristics of individual spike trains. It is also instrumental in producing a strong cooperativity between the different neurons. Moreover, a functional spatial anisotropy appears. A specific entropy index is used to analyze levels of information transfer between clustered and faraway neurons in the network. The formatting effect of recurrent collateral inhibition on spike trains and on network functional dynamics is studied by means of a model of the newborn rat cerebellar cortex. This immature structure has simpler morphological characteristics and fewer physiological parameters than the adult one. It is thus a good candidate for the comparison between experimental and theoretical data. The model network is made of 256 formal neurons (FN), arranged in a square lattice. Each neuron is coupled to its eight nearest neighbors by inhibitory links. All the parameters of the different elements of the model--in particular integration of inhibitory and excitatory inputs--are given anatomical and physiological values derived from biological data. Activities of single FNs and correlations between spatially distant ones are analyzed with classical statistical techniques as well as with a specific informational entropy method we introduce. Simulation results indicate that inhibition is instrumental in: (1) the transformation of the spike train characteristics. This includes a lengthening of the mean interspike interval as well as an overall change in the statistical distribution of intervals, with an emergence of long-lasting ones; (2) the functional structuration of the network. Inhibitory connections between nearest neighbors induce a strong cooperativity between FNs. Furthermore a clear spatial anisotropy occurs in the functioning of the network, with inhibitory effects extending beyond local connectivity in preferential directions. We propose an interpretation of this functional structuration in terms of the various routes followed by the inhibition, including relay effects. The parameters of the model (levels of activities, inhibition rules and connectivities) were varied in order to test the robustness of the above results. Finally, the results are compared with those obtained in an experimental situation.

Animals↗
PubMed2006

Mitochondrial membrane dynamics, cristae remodelling and apoptosis.

Mitochondria form a highly dynamic reticular network in living cells, and undergo continuous fusion/fission events and changes in ultrastructural architecture. Although significant progress has been made in elucidating the molecular events underlying these processes, their relevance to normal cell function remains largely unexplored. Emerging evidence, however, suggests an important role for mitochondrial dynamics in cellular apoptosis. The mitochondria is at the core of the intrinsic apoptosis pathway, and provides a reservoir for protein factors that induce caspase activation and chromosome fragmentation. Additionally, mitochondria modulate Ca2+ homeostasis and are a source of various metabolites, including reactive oxygen species, that have the potential to function as second messengers in response to apoptotic stimuli. One of the mitochondrial factors required for activation of caspases in most intrinsic apoptotic pathways, cytochrome c, is largely sequestered within the intracristae compartment, and must migrate into the boundary intermembrane space in order to allow passage across the outer membrane to the cytosol. Recent evidence argues that inner mitochondrial membrane dynamics regulate this process. Here, we review the contribution of mitochondrial dynamics to the intrinsic apoptosis pathway, with emphasis on the inner membrane.

Animals↗
PubMed2005

Active and dynamic information fusion for facial expression understanding from image sequences.

This paper explores the use of multisensory information fusion technique with Dynamic Bayesian networks (DBNs) for modeling and understanding the temporal behaviors of facial expressions in image sequences. Our facial feature detection and tracking based on active IR illumination provides reliable visual information under variable lighting and head motion. Our approach to facial expression recognition lies in the proposed dynamic and probabilistic framework based on combining DBNs with Ekman's Facial Action Coding System (FACS) for systematically modeling the dynamic and stochastic behaviors of spontaneous facial expressions. The framework not only provides a coherent and unified hierarchical probabilistic framework to represent spatial and temporal information related to facial expressions, but also allows us to actively select the most informative visual cues from the available information sources to minimize the ambiguity in recognition. The recognition of facial expressions is accomplished by fusing not only from the current visual observations, but also from the previous visual evidences. Consequently, the recognition becomes more robust and accurate through explicitly modeling temporal behavior of facial expression. In this paper, we present the theoretical foundation underlying the proposed probabilistic and dynamic framework for facial expression modeling and understanding. Experimental results demonstrate that our approach can accurately and robustly recognize spontaneous facial expressions from an image sequence under different conditions.

Algorithms↗
PubMed2001

Dynamic in vivo interactions among Myc network members.

Members of the Myc oncoprotein network (c-Myc, Max, and Mad) play important roles in proliferation, differentiation, and apoptosis. We expressed chimeric green fluorescent protein (GFP) fusions of c-Myc, Max, and three Mad proteins in fibroblasts. Individually, c-Myc and Mad proteins localized in subnuclear speckles, whereas Max assumed a homogeneous nuclear pattern. These distributions were co-dominant and dynamic, however, as each protein assumed the pattern of its heterodimeric partner when the latter was co-expressed at a higher level. Deletion mapping of two Mad members, Mad1 and Mxi1, demonstrated that the domains responsible for nuclear localization and speckling are separable. A non-speckling Mxi1 mutant was also less effective as a transcriptional repressor than wild-type Mxi1. c-Myc nuclear speckles were distinct from SC-35 domains involved in mRNA processing. However, in the presence of co-expressed Max, c-Myc, but not Mad, co-localized to a subset of SC-35 loci. These results show that Myc network proteins comprise dynamic subnuclear structures and behave co-dominantly when co-expressed with their normal heterodimerization partners. In addition, c-Myc-Max heterodimers, but not Max-Mad heterodimers, localize to foci actively engaged in pre-mRNA transcription/processing. These findings suggest novel means by which Myc network members promote transcriptional activation or repression.

3T3 Cells↗
PubMed2004

Cytoskeletal network in colon cancer: from genes to clinical application.

Colorectal cancer arises from well-defined sequential steps characterised by distinct genetic events. Abnormalities in the expression and functional activity of cell adhesion molecules are implicated in the development and progression of the majority of colorectal cancers. Intercellular (e.g. E-cadherin/catenin complex) and cell-matrix (e.g. integrins) adhesion molecules are more than just cementing substances but regulate cell polarity, differentiation, proliferation, migration and invasion. Many of these cellular events are mediated through their intimate association with the actin cytoskeletal network. A dynamic actin cytoskeleton characterises normal epithelial cells and polymerisation and depolymerisation of actin filaments enables cell shape to change during migration and mitosis. In colorectal cancer, cells lose actin cytoskeletal organisation and normal cell adhesion when they become invasive. Future investigations should allow the unravelling of new cytoskeletal network functions in tumour biology and may lead to the development of novel therapeutic strategies based on the manipulation of its associated molecules.

Actins↗
PubMed2006

Synchronization from disordered driving forces in arrays of coupled oscillators.

The effects of disorder in external forces on the dynamical behavior of coupled nonlinear oscillator networks are studied. When driven synchronously, i.e., all driving forces have the same phase, the networks display chaotic dynamics. We show that random phases in the driving forces result in regular, periodic network behavior. Intermediate phase disorder can produce network synchrony. Specifically, there is an optimal amount of phase disorder, which can induce the highest level of synchrony. These results demonstrate that the spatiotemporal structure of external influences can control chaos and lead to synchronization in nonlinear systems.

Action Potentials↗
PubMed2006

Chondrocyte deformation induces mitochondrial distortion and heterogeneous intracellular strain fields.

Chondrocyte mechanotransduction is poorly understood but may involve cell deformation and associated distortion of intracellular structures and organelles. This study quantifies the intracellular displacement and strain fields associated with chondrocyte deformation and in particular the distortion of the mitochondria network, which may have a role in mechanotransduction. Isolated articular chondrocytes were compressed in agarose constructs and simultaneously visualised using confocal microscopy. An optimised digital image correlation technique was developed to calculate the local intracellular displacement and strain fields using confocal images of fluorescently labelled mitochondria. The mitochondria formed a dynamic fibrous network or reticulum, which co-localised with microtubules and vimentin intermediate filaments. Cell deformation induced distortion of the mitochondria, which collapsed in the axis of compression with a resulting loss of volume. Compression generated heterogeneous intracellular strain fields indicating mechanical heterogeneity within the cytoplasm. The study provides evidence supporting the potential involvement of mitochondrial deformation in chondrocyte mechanotransduction, possibly involving strain-mediated release of reactive oxygen species. Furthermore the heterogeneous strain fields, which appear to be influenced by intracellular structure and organisation, may generate significant heterogeneity in mechanotransduction behaviour for cells subjected to identical levels of deformation.

Animals↗
PubMed2004

Solution of voter model dynamics on annealed small-world networks.

An analytical study of the behavior of the voter model on the small-world topology is performed. In order to solve the equations for the dynamics, we consider an annealed version of the Watts-Strogatz (WS) network, where long-range connections are randomly chosen at each time step. The resulting dynamics is as rich as on the original WS network. A temporal scale tau separates a quasistationary disordered state with coexisting domains from a fully ordered frozen configuration. tau is proportional to the number of nodes in the network, so that the system remains asymptotically disordered in the thermodynamic limit.

Journal Article↗
PubMed1996

Measures of concurrency in networks and the spread of infectious disease.

An investigation is made into the impact of concurrent partnerships on epidemic spread. Starting from a definition of concurrency on the level of individuals, the authors define ways to quantify concurrency on the population level. An index of concurrency based on graph theoretical considerations is introduced, and the way in which it is related to the degree distribution of the contact graph is demonstrated. Then the spread of an infectious disease on a dynamic partnership network is investigated. The model is based on a stochastic process of pair formation and separation and a process of disease transmission within partnerships of susceptible and infected individuals. Using Monte Carlo simulation, the spread of the epidemic is compared for contact patterns ranging from serial monogamy to situations where individuals can have many partners simultaneously. It is found that for a fixed mean number of partners per individual the distribution of these partnerships over the population has a major influence on the speed of the epidemic in its initial phase and consequently in the number of individuals who are infected after a certain time period.

Acquired Immunodeficiency Syndrome↗
PubMed2005

Structure formation in electromagnetically driven granular media.

We report structure formation in submonolayers of magnetic microparticles subjected to periodic electrostatic and magnetic excitations. Depending on the excitation parameters, we observe the formation of a rich variety of structures: clusters, rings, chains, and networks. The dynamics and shapes of the structures are strongly dependent on the amplitude and frequency of the external magnetic field. We find that for pure ac magnetic driving the low-frequency magnetic excitation favors compact clusters, whereas high frequency driving favors chains and netlike structures. An abrupt phase transition from chains to a network phase was observed for a high density of particles.

Journal Article↗
PubMed1975

A topological theorem for nonlinear dynamics in chemical and ecological networks.

A topological theorem is given which places restrictions on the numbers and types of critical points in nonlinear chemical and ecological systems, provided certain stringent conditions on the flows on the boundary of the concentration space are satisfied. The sets of critical points that are found for stable and unstable oscillations, and for competitive exclusion, represent the simplest sets of critical points that satisfy the topological theorem. The restrictions that are imposed on bifurcations of the dynamics are discussed.

Journal Article↗
PubMed2004

Scaling of F-actin network rheology to probe single filament elasticity and dynamics.

The linear and nonlinear viscoelastic response of networks of cross-linked and bundled cytoskeletal filaments demonstrates remarkable scaling with both frequency and applied prestress, which helps elucidate the origins of the viscoelasticity. The frequency dependence of the shear modulus reflects the underlying single-filament relaxation dynamics for 0.1-10 rad/sec. Moreover, the nonlinear strain stiffening of such networks exhibits a universal form as a function of prestress; this is quantitatively explained by the full force-extension relation of single semiflexible filaments.

Actins↗
PubMed2006

Inhibitory transmission, activity-dependent ionic changes and neuronal network oscillations.

Oscillatory network activity arises from interactions between synaptic and intrinsic membrane properties of neurons. In this review, we summarize general mechanisms of synchronous neuronal oscillations. In addition, we focus on recent experimental and computational studies which suggest that activity-dependent changes of ionic environment can affect both the synaptic and intrinsic neuronal properties and influence the network behavior. GABA(A) receptor (GABA(A)R)-mediated signaling, that is based on Cl(-) and HCO(3)(-) permeability, is thought to be important for the oscillogenesis and synchronization in cortical networks. A remarkable feature of GABAergic synapses is that prolonged GABA(A)R activation may lead to switching from a hyperpolarizing to a depolarizing response. This is partly due to a positive shift of the GABA(A) R reversal potential (E(GABA)) that is generated by GABA-induced Cl(-) accumulation in neurons. Recent studies suggest that activity-dependent E(GABA) changes may have important implications for the mechanisms of gamma oscillations and seizure-like discharges. Thus, a better understanding of the impact of intracellular Cl(-) dynamics on network behavior may provide insights into the mechanisms of physiological and pathological brain rhythms. Combination of experiments and simulations is a promising approach for elucidating which properties of the time-varying ionic environment can shape the dynamics of a given circuit.

Biological Clocks↗
PubMed2006

Neuronal computations with stochastic network states.

Neuronal networks in vivo are characterized by considerable spontaneous activity, which is highly complex and intrinsically generated by a combination of single-cell electrophysiological properties and recurrent circuits. As seen, for example, during waking compared with being asleep or under anesthesia, neuronal responsiveness differs, concomitant with the pattern of spontaneous brain activity. This pattern, which defines the state of the network, has a dramatic influence on how local networks are engaged by inputs and, therefore, on how information is represented. We review here experimental and theoretical evidence of the decisive role played by stochastic network states in sensory responsiveness with emphasis on activated states such as waking. From single cells to networks, experiments and computational models have addressed the relation between neuronal responsiveness and the complex spatiotemporal patterns of network activity. The understanding of the relation between network state dynamics and information representation is a major challenge that will require developing, in conjunction, specific experimental paradigms and theoretical frameworks.

Anesthesia↗
PubMed2002

The use of polynomial neural networks for mortality prediction in uncontrolled venous and arterial hemorrhage.

BACKGROUND: The ability to rapidly and accurately triage, evacuate, and utilize appropriate interventions can be problematic in the early decision-making process of trauma care. With current methods of prehospital data collection and analysis, decisions are often based upon single data points. This information may be insufficient for reliable decision-making. To date, no studies have attempted to utilize data at multiple time points for purposes of enhancing prediction, nor have studies attempted to synthesize prediction models with data reflecting both large-vessel venous and arterial injuries. Therefore, we performed a retrospective study to examine the potential utility of dynamic neural networks in predicting mortality using highly discretized uncontrolled hemorrhagic shock data. METHODS: One hundred forty-three swine with either grade V liver injuries or 2.8-mm aortotomies had hemodynamic data collected every minute throughout injury and resuscitation. The independent variables used as inputs to the polynomial neural networks (PNNs) included systolic blood pressure and mean arterial pressure (MAP). These inputs were used to predict mortality in individual swine 1 hour after injury using data up to 20 minutes after injury. Survival models were compared based on discrimination power (DP), i.e., where specificity equals sensitivity, and area under the receiver operating characteristic (ROC) curve (c-statistic). The Hosmer-Lemeshow (H-L) statistic was used to measure model calibration. RESULTS: The best PNN model predicted mortality at 60 minutes utilizing data from injury to 20 minutes after injury. This model produced a ROC area of 0.919, a DP of 0.857, and a H-L value of 16.47. A DP of 0.857 means that 85.7% of the survivors are correctly predicted to survive, and 85.7% of the nonsurvivors are predicted to die. MAP of survivors and nonsurvivors were graphed for comparative purposes. As this graph illustrates, the use of MAP alone cannot discriminate survivors from nonsurvivors. CONCLUSION: This study demonstrates that PNN models can effectively harness the dynamic nature of uncontrolled hemorrhagic shock data, despite utilizing data from large-vessel arterial and venous injuries. Utilizing the dynamic nature of hemorrhagic shock data in PNNs may ultimately allow the development of novel decision assist devices.

Animals↗
PubMed2004

Advances to Bayesian network inference for generating causal networks from observational biological data.

MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/

Algorithms↗
PubMed2005

"Shape activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection.

The aim is to model "activity" performed by a group of moving and interacting objects (which can be people, cars, or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include co-occurrence statistics (individual and joint histograms) and dynamic Bayesian networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as "landmarks") and propose to model their changing configuration as a moving and deforming "shape" (using Kendall's shape theory for discrete landmarks). A continuous-state hidden Markov model is defined for landmark shape dynamics in an activity. The configuration of landmarks at a given time forms the observation vector, and the corresponding shape and the scaled Euclidean motion parameters form the hidden-state vector. An abnormal activity is then defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Results are shown on a real abnormal activity-detection problem involving multiple moving objects.

Algorithms↗
PubMed2004

Extinction dynamics of Lotka-Volterra ecosystems on evolving networks.

We study a model of a multispecies ecosystem described by Lotka-Volterra-like equations. Interactions among species form a network whose evolution is determined by the dynamics of the model. Numerical simulations show power-law distribution of intervals between extinctions, but only for ecosystems with sufficient variability of species and with networks of connectivity above certain threshold that is very close to the percolation threshold of the network. The effect of slow environmental changes on extinction dynamics, degree distribution of the network of interspecies interactions, and some emergent properties of our model are also examined.

Adaptation, Physiological↗