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PubMed1999

Visualising the actin cytoskeleton.

The actin cytoskeleton is a dynamic filamentous network whose formation and remodeling underlies the fundamental processes of cell motility and shape determination. To serve these roles, different compartments of the actin cytoskeleton engage in forming specific coupling sites between neighbouring cells and with the underlying matrix, which themselves serve signal transducing functions. In this review, we focus on methods used to visualise the actin cytoskeleton and its dynamics, embracing the use of proteins tagged with conventional fluorophores and green fluorescent protein. Included also is a comparison of cooled CCD technology, confocal and 2-photon fluorescence microscopy of living and fixed cells, as well as a critique of current procedures for electron microscopy.

Actins↗
PubMedDate not supplied

Formal modeling with multistate neurones and multidimensional synapses.

Multistate neurones, a generalization of the popular McCulloch-Pitts binary neurones, are described; they are intended to model the fact that neurones may be in several different states of activity, while McCulloch-Pitts neurones model two states only: active or inactive. We show that as a consequence, multidimensional synapses are necessary to describe the dynamics of the model. As an illustration, we show how to derive the parameters of formal multistate neurones and their associated multidimensional synapses from simulations involving Hodgkin-Huxley neurones. Our approach opens the way to solve in a more biologically plausible way, two problems that were addressed previously: (1) the resolution of 'inverse problems', i.e. the construction of formal networks, whose dynamics follows a pre-defined spatio-temporal binary sequence, (2) the generation of spatio-temporal patterns that reproduce exactly the 'code' extracted from experimental recordings (olfactory codes at the glomerular level).

Models, Neurological↗
PubMed2005

Cooperation, social networks, and the emergence of leadership in a prisoner's dilemma with adaptive local interactions.

Cooperative behavior among a group of agents is studied assuming adaptive interactions. Each agent plays a Prisoner's Dilemma game with its local neighbors, collects an aggregate payoff, and imitates the strategy of its best neighbor. Agents may punish or reward their neighbors by removing or sustaining the interactions, according to their satisfaction level and strategy played. An agent may dismiss an interaction, and the corresponding neighbor is replaced by another randomly chosen agent, introducing diversity and evolution to the network structure. We perform an extensive numerical and analytical study, extending results in M. G. Zimmermann, V. M. Eguíluz, and M. San Miguel, Phys. Rev. E 69, 065102(R) (2004). We show that the system typically reaches either a full-defective state or a highly cooperative steady state. The latter equilibrium solution is composed mostly by cooperative agents, with a minor population of defectors that exploit the cooperators. It is shown how the network adaptation dynamics favors the emergence of cooperators with the highest payoff. These "leaders" are shown to sustain the global cooperative steady state. Also we find that the average payoff of defectors is larger than the average payoff of cooperators. Whenever "leaders" are perturbed (e.g., by addition of noise), an unstable situation arises and global cascades with oscillations between the nearly full defection network and the fully cooperative outcome are observed.

Journal Article↗
PubMed2001

Organization and functional roles of the cytoskeleton in oligodendrocytes.

Mature oligodendrocytes are characterized by their numerous cytoplasmic extensions and flat membranous sheets. These sheets contain an extensive cytoskeletal network of microtubules (MTs) that maintain the cellular morphology, are specifically important for cellular sorting, and provide the rails for organelle trafficking. Mitochondria are localized in the primary and secondary processes and follow the tracks of the MTs in the cytoplasmic extensions. Oligodendrocytes express microtubule associated proteins (MAPs), specifically MAP2 and tau, which might be involved in the regulation and stabilization of the dynamic MT network in the myelin-containing cellular processes. Tau and MAP2 heterogeneity increases during oligodendroglia maturation, and in mature oligodendrocytes tau mRNA with four MT binding domains are more prominent than in progenitor cells. Filamentous cell inclusions are a unifying mechanism underlying a variety of late-onset neurodegenerative disorders and have mainly been viewed as neuron-specific. Recent evidence indicated that glial changes occur in CNS degenerative diseases and seem to be a more common feature than previously thought. Glial fibrillary tangles (GFTs) in oligodendrocytes were observed in familial multiple system tauopathy, and glial cytoplasmic inclusions (GCIs) and oligodendroglia degeneration are the histological hallmark of multiple system atrophy (MSA). GCIs are associated with MTs and contain stress proteins and MAPs. Thus, neurons and glial cells share common cytoskeletal pathologies. During health and disease, MAPs might be important regulators of the structural stability and plasticity of the oligodendroglia cytoskeleton.

Animals↗
PubMed2004

A neural network approach to movement pattern analysis.

Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, where the sequence of clusters to which the time-depending configurations belong, characterizes the process as a 2-dimensional trajectory. The advantages of this method are that: the high dimensionality of the original processes is reduced to two dimensional trajectories, the clusters are automatically determined by the network, and all data for further analyses can automatically be transferred into a data base. Thus, the processes can either be visualized and analysed by an expert or again processed by further automatic analysing tools, as has been done with similarity matrices. The disadvantage is that a KFM-training needs a huge amount of information, which normally is not available from experiments. However, the Dynamically Controlled Network DyCoN (a special type of KFM) makes it possible to reduce the amount of original training data substantially--e.g., by adding stochastically generated ones. Currently, DyCoN is used in several projects in order to generally support analyses of processes in sport. It should be emphasized that the presented approach is not meant to improve the understanding or to develop models of human movement but to give a survey of the advantages and methodological aspects of net-based movement analysis.

Algorithms↗
PubMed2006

Synchronization reveals topological scales in complex networks.

We study the relationship between topological scales and dynamic time scales in complex networks. The analysis is based on the full dynamics towards synchronization of a system of coupled oscillators. In the synchronization process, modular structures corresponding to well-defined communities of nodes emerge in different time scales, ordered in a hierarchical way. The analysis also provides a useful connection between synchronization dynamics, complex networks topology, and spectral graph analysis.

Journal Article↗
PubMed1999

Attractor neural network models of spatial maps in hippocampus.

Hippocampal pyramidal neurons in rats are selectively activated at specific locations in an environment (O'Keefe and Dostrovsky, Brain Res 1971;34:171-175). Different cells are active in different places, therefore providing a faithful representation of the environment in which every spatial location is mapped to a particular population state of activity of place cells (Wilson and McNaughton, Science 1993;261:1055-1058; Zhang et al., J Neurosci 1998;79:1017-1044). We describe a theory of the hippocampus, according to which the map results from the cooperative dynamics of network, in which the strength of synaptic interaction between the neurons depends on the distance between their place fields. This synaptic structure guarantees that the network possesses a quasi-continuous set of stable states (attractors) that are localized in the space of neuronal variables reflecting their synaptic interactions, rather than their physical location in the hippocampus. As a consequence of the stable states, the network can exhibit place selective activity even without relying on input from external sensory cues.

Animals↗
PubMed1984

Mathematical modelling of dynamics and control in metabolic networks. I. On Michaelis-Menten kinetics.

As a starting point for modeling of metabolic networks this paper considers the simple Michaelis-Menten reaction mechanism. After the elimination of diffusional effects a mathematically intractable mass action kinetic model is obtained. The properties of this model are explored via scaling and linearization. The scaling is carried out such that kinetic properties, concentration parameters and external influences are clearly separated. We then try to obtain reasonable estimates for values of the dimensionless groups and examine the dynamic properties of the model over this part of the parameter space. Linear analysis is found to give excellent insight into reaction dynamics and it also gives a forum for understanding and justifying the two commonly used quasi-stationary and quasi-equilibrium analyses. The first finding is that there are two separate time scales inherent in the model existing over most of the parameter space, and in particular over the regions of importance here. Full modal analysis gives a new interpretation of quasi-stationary analysis, and its extension via singular perturbation theory, and a rationalization of the quasi-equilibrium approximation. The new interpretation of the quasi-steady state assumption is that the applicability is intimately related to dynamic interactions between the concentration variables rather than the traditional notion that a quasi-stationary state is reached, after a short transient period, where the rates of formation and decomposition of the enzyme intermediate are approximately equal. The modal analysis reveals that the generally used criterion for the applicability of quasi-stationary analysis that total enzyme concentration must be much less than total substrate concentration, et much less than St, is incomplete and that the criterion et much less than Km much less than St (Km is the well known Michaelis constant) is the appropriate one. The first inequality (et much less than Km) guarantees agreement over the longer time scale leading to quasi-stationary behavior or the applicability of the zeroth order outer singular perturbation solution but the second half of the criterion (Km much less than St) justifies zeroth order inner singular perturbation solution where the substrate concentration is assumed to be invariant. Furthermore linear analysis shows that when a fast mode representing the binding of substrate to the enzyme is fast it can be relaxed leading to the quasi-equilibrium assumption. The influence of the dimensionless groups is ascertained by integrating the equations numerically, and the predictions made by the linear analysis are found to be accurate.(ABSTRACT TRUNCATED AT 400 WORDS)

Diffusion↗
PubMed2004

A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles.

MOTIVATION: The increasing use of microarray technologies is generating large amounts of data that must be processed in order to extract useful and rational fundamental patterns of gene expression. Hierarchical clustering technology is one method used to analyze gene expression data, but traditional hierarchical clustering algorithms suffer from several drawbacks (e.g. fixed topology structure; mis-clustered data which cannot be reevaluated). In this paper, we introduce a new hierarchical clustering algorithm that overcomes some of these drawbacks. RESULT: We propose a new tree-structure self-organizing neural network, called dynamically growing self-organizing tree (DGSOT) algorithm for hierarchical clustering. The DGSOT constructs a hierarchy from top to bottom by division. At each hierarchical level, the DGSOT optimizes the number of clusters, from which the proper hierarchical structure of the underlying dataset can be found. In addition, we propose a new cluster validation criterion based on the geometric property of the Voronoi partition of the dataset in order to find the proper number of clusters at each hierarchical level. This criterion uses the Minimum Spanning Tree (MST) concept of graph theory and is computationally inexpensive for large datasets. A K-level up distribution (KLD) mechanism, which increases the scope of data distribution in the hierarchy construction, was used to improve the clustering accuracy. The KLD mechanism allows the data misclustered in the early stages to be reevaluated at a later stage and increases the accuracy of the final clustering result. The clustering result of the DGSOT is easily displayed as a dendrogram for visualization. Based on a yeast cell cycle microarray expression dataset, we found that our algorithm extracts gene expression patterns at different levels. Furthermore, the biological functionality enrichment in the clusters is considerably high and the hierarchical structure of the clusters is more reasonable. AVAILABILITY: DGSOT is available upon request from the authors.

Algorithms↗
PubMed2005

Ultrafast memory loss and energy redistribution in the hydrogen bond network of liquid H2O.

Many of the unusual properties of liquid water are attributed to its unique structure, comprised of a random and fluctuating three-dimensional network of hydrogen bonds that link the highly polar water molecules. One of the most direct probes of the dynamics of this network is the infrared spectrum of the OH stretching vibration, which reflects the distribution of hydrogen-bonded structures and the intermolecular forces controlling the structural dynamics of the liquid. Indeed, water dynamics has been studied in detail, most recently using multi-dimensional nonlinear infrared spectroscopy for acquiring structural and dynamical information on femtosecond timescales. But owing to technical difficulties, only OH stretching vibrations in D2O or OD vibrations in H2O could be monitored. Here we show that using a specially designed, ultrathin sample cell allows us to observe OH stretching vibrations in H2O. Under these fully resonant conditions, we observe hydrogen bond network dynamics more than one order of magnitude faster than seen in earlier studies that include an extremely fast sweep in the OH frequencies on a 50-fs timescale and an equally fast disappearance of the initial inhomogeneous distribution of sites. Our results highlight the efficiency of energy redistribution within the hydrogen-bonded network, and that liquid water essentially loses the memory of persistent correlations in its structure within 50 fs.

Absorption↗
PubMed2002

Modelling regulatory pathways in E. coli from time series expression profiles.

MOTIVATION: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data. RESULTS: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.

Adaptation, Physiological↗
PubMed2004

Water molecules in hydroxy/acid networks as a competition between dynamics and bonding. Synthesis of a wet hydrophobic pore.

In a model formed by hydroxy acids with a general structure (+/-)-1, we found that solid-state structures depend on steric interactions. Thus, with the exception of molecules 1b and 1e, compounds (+/-)-1a-(+/-)-1m, which possess bulky and conformationally rigid substituents, aggregate by forming tapes and sheets by alternating (+) and (-) subunits held together via carboxylic acid to alcohol hydrogen bonds. Homologue (+/-)-1n with conformationally flexible substituents, which allow conformational deformation gives, by way of the incorporation of water molecules, an efficient hexagonal assembly, which extends to the third-dimension to form tubular H-bonding networks. Each puckered channel can be described as being interconnected by closely packed hexagons in chair-like conformations. The ethyl groups presented in (+/-)-1n provided the volume required to lock the inner hexagonal wall into a rigid structure.

Carboxylic Acids↗
PubMed2006

Dynamic fluctuations and spatial inhomogeneities in poly(N-isopropylacrylamide)/clay nanocomposite hydrogels studied by dynamic light scattering.

The contributions of the dynamic fluctuations and the frozen-in inhomogeneities to the total light scattering intensity observed in poly(N-isopropylacrylamide)/clay nanocomposite hydrogels were analyzed by applying the nonergodic method proposed by Pusey and van Megen. Approximately 90% of the total scattering intensity corresponds to the frozen-in component. The scattering intensity of the fluctuating component is smaller by far than that of a pure clay suspension, indicating that the thermal fluctuations of the clay particles are largely suppressed upon network formation. Accordingly, the fluctuating component consists of two contributions: one due to the polymer chains and the other, smaller one representing the residual mobility of the clay particles. The latter depends on how tightly the clay particles are fixed in the network. The dynamic features of the nanocomposite hydrogels are described by two relaxation modes. The fast one is purely diffusive and can be related to a dynamic correlation length of 6-8 nm, which is similar to that of a corresponding polymer solution. The relaxation time of the slow mode varies appreciably with sample position even though the data had been treated with the nonergodic method.

Journal Article↗
PubMed2006

Dynamics of asynchronous random Boolean networks with asynchrony generated by stochastic processes.

An asynchronous Boolean network with N nodes whose states at each time point are determined by certain parent nodes is considered. We make use of the models developed by Matache and Heidel [Matache, M.T., Heidel, J., 2005. Asynchronous random Boolean network model based on elementary cellular automata rule 126. Phys. Rev. E 71, 026232] for a constant number of parents, and Matache [Matache, M.T., 2006. Asynchronous random Boolean network model with variable number of parents based on elementary cellular automata rule 126. IJMPB 20 (8), 897-923] for a varying number of parents. In both these papers the authors consider an asynchronous updating of all nodes, with asynchrony generated by various random distributions. We supplement those results by using various stochastic processes as generators for the number of nodes to be updated at each time point. In this paper we use the following stochastic processes: Poisson process, random walk, birth and death process, Brownian motion, and fractional Brownian motion. We study the dynamics of the model through sensitivity of the orbits to initial values, bifurcation diagrams, and fixed-point analysis. The dynamics of the system show that the number of nodes to be updated at each time point is of great importance, especially for the random walk, the birth and death, and the Brownian motion processes. Small or moderate values for the number of updated nodes generate order, while large values may generate chaos depending on the underlying parameters. The Poisson process generates order. With fractional Brownian motion, as the values of the Hurst parameter increase, the system exhibits order for a wider range of combinations of the underlying parameters.

Logistic Models↗
PubMed2005

Mitochondrial morphology and dynamics in yeast and multicellular eukaryotes.

Mitochondria form dynamic tubular networks that continually change their shape and move throughout the cell. In eukaryotes, these organellar gymnastics are controlled by numerous pathways that preserve proper mitochondrial morphology and function. The best understood of these are the fusion and fission pathways, which rely on conserved GTPases and their binding partners to regulate organelle connectivity and copy number in healthy cells and during apoptosis. In budding yeast, mitochondrial shape is also maintained by proteins acting in the tubulation pathway. Novel proteins and pathways that control mitochondrial dynamics continue to be discovered, indicating that the mechanisms governing this organelle's behavior are more sophisticated than previously appreciated. Here we review recent advances in the field of mitochondrial dynamics and highlight the importance of these pathways to human health.

Apoptosis↗
PubMed2004

Inferring models of gene expression dynamics.

We study the problem of identifying genetic networks in which expression dynamics are modeled by a differential equation that uses logical rules to specify time derivatives. We make three main contributions. First, we describe computationally efficient procedures for identifying the structure and dynamics of such networks from expression time series. Second, we derive predictions for the expected amount of data needed to identify randomly generated networks. Third, if expression values are available for only some of the genes, we show that the structure of the network for these "visible" genes can be identified and that the size and overall complexity of the network can be estimated. We validate these procedures and predictions using simulation experiments based on randomly generated networks with up to 30,000 genes and 17 distinct regulators per gene and on a network that models floral morphogenesis in Arabidopsis thaliana.

Arabidopsis↗
PubMed2005

Network based models of infectious disease spread.

It has recently become possible to simulate directly dynamics on very large networks. This paper describes a model of epidemiology on a social network, the Epidemiological Simulation System (EpiSims), and offers general speculation on analyzing disease dynamics on networks. We describe the process of building a realistic social network, describe several different definitions of the network, each useful for certain purposes. Finally, we raise some important questions about structural properties of networks and how they influence dynamics.

Communicable Diseases↗
PubMed2026

Macrophage regulation of extracellular matrix remodeling in aging skeletal muscle.

The extracellular matrix (ECM) is a dynamic structural network that supports tissue architecture and regulates cell function. It is primarily composed of collagens, elastin, proteoglycans, and glycoproteins, which are synthesized by canonical and non-canonical ECM-producing cells. During aging, the ECM undergoes progressive changes in structure and composition, a process recently recognized as the 13th hallmark of aging. In skeletal muscle (SKM), age-associated ECM remodeling, largely regulated by immune system-ECM crosstalk, contributes to sarcopenia and impaired regeneration. Macrophages (MΦs), as key innate immune cells, regulate ECM dynamics both indirectly by activating canonical ECM-producing cells and directly by synthesizing ECM components. Notably, a distinct subset of ECM-producing MΦs that express collagen (COL+ MΦs) has been identified across multiple tissues, although their function in SKM homeostasis and aging remains poorly understood. Here, we review current knowledge of ECM production and remodeling, with special emphasis on MΦ involvement, including COL+ MΦs, as critical regulators of fibrogenesis, especially during SKM aging and regeneration.

Extracellular Matrix↗