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Enzymes: An integrated view of structure, dynamics and function.

Microbes utilize enzymes to perform a variety of functions. Enzymes are biocatalysts working as highly efficient machines at the molecular level. In the past, enzymes have been viewed as static entities and their function has been explained on the basis of direct structural interactions between the enzyme and the substrate. A variety of experimental and computational techniques, however, continue to reveal that proteins are dynamically active machines, with various parts exhibiting internal motions at a wide range of time-scales. Increasing evidence also indicates that these internal protein motions play a role in promoting protein function such as enzyme catalysis. Moreover, the thermodynamical fluctuations of the solvent, surrounding the protein, have an impact on internal protein motions and, therefore, on enzyme function. In this review, we describe recent biochemical and theoretical investigations of internal protein dynamics linked to enzyme catalysis. In the enzyme cyclophilin A, investigations have lead to the discovery of a network of protein vibrations promoting catalysis. Cyclophilin A catalyzes peptidyl-prolyl cis/trans isomerization in a variety of peptide and protein substrates. Recent studies of cyclophilin A are discussed in detail and other enzymes (dihydrofolate reductase and liver alcohol dehydrogenase) where similar discoveries have been reported are also briefly discussed. The detailed characterization of the discovered networks indicates that protein dynamics plays a role in rate-enhancement achieved by enzymes. An integrated view of enzyme structure, dynamics and function have wide implications in understanding allosteric and co-operative effects, as well as protein engineering of more efficient enzymes and novel drug design.

Journal Article↗

A neuro-mimetic dynamic scheduling algorithm for control: analysis and applications.

A simple neuronal network model of the baroreceptor reflex is analyzed. From a control perspective, the analysis suggests a dynamic scheduled control mechanisms by which the baroreflex may perform regulation of the blood pressure. The main objectives of this work are to investigate the static and dynamic response characteristics of the single neurons and the network, to analyze the neuromimetic dynamic scheduled control function of the model, and to apply the algorithm to nonlinear process control problems. The dynamic scheduling activity of the network is exploited in two control architectures. Control structure I is drawn directly from the present model of the baroreceptor reflex. An application of this structure for level control in a conical tank is described. Control structure II employs an explicit set point to determine the feedback error. The performance of this control structure is illustrated on a nonlinear continuous stirred tank reactor with van de Vusse kinetics. The two case studies validate the dynamic scheduled control approach for nonlinear process control applications.

Algorithms↗

Neuromuscular modulation in Aplysia. I. Dynamic model.

Many physiological systems are regulated by complex networks of modulatory actions. Here we use mathematical modeling and complementary experiments to study the dynamic behavior of such a network in the accessory radula closer (ARC) neuromuscular system of Aplysia. The ARC muscle participates in several types of rhythmic consummatory feeding behavior. The muscle's motor neurons release acetylcholine to produce basal contractions, but also modulatory peptide cotransmitters that, through multiple cellular effects, shape the contractions to meet behavioral demands. We construct a dynamic model of the modulatory network and examine its operation as the motor neurons fire in realistic patterns that change gradually over an hour-long meal and abruptly with switches between the different feeding behaviors. The modulatory effects have very disparate dynamical time scales. Some react to the motor neuron firing only over many cycles of the behavior, but one key effect is fast enough to respond to each individual cycle. Switches between the behaviors are therefore followed by rapid relaxations along some modulatory dimensions but not others. The trajectory of the modulatory state is a transient throughout the meal, ranging widely over regions of the modulatory space not accessible in the steady state. There is a pronounced history-dependency: the modulatory state associated with a cycle of a particular behavior depends on when that cycle occurs and what behaviors preceded it. On average, nevertheless, each behavior is associated with a different modulatory state. In the following companion study, we add a model of the neuromuscular transform to reconstruct and evaluate the actual modulated contraction shapes.

Animals↗

Intermolecular dynamical charge fluctuations in water: a signature of the H-bond network.

We report a simulation of deuterated water using a Car-Parrinello approach based on maximally localized Wannier functions. This provides local information on the dynamics of the hydrogen-bond network and on the origin of the low-frequency infrared activity. The oscillator strength of the translational modes, peaked around approximately 200 cm-1, is anisotropic and originates from intermolecular--not intramolecular--charge fluctuations. These fluctuations are a signature of a tetrahedral hydrogen-bonding environment.

Journal Article↗

Information coding on the basis of synchronization of neuronal activity.

Three types of oscillatory neural networks are considered and their dynamics are analyzed. The regime of chaotic coherent oscillations was found in a network of excitatory and inhibitory integrate-and-fire neurons with all-to-all connections. The regime of envelope oscillations was found in a chain of neural oscillators with local connections. The regime of partial synchronization of the central element with a group of peripheral oscillators under perturbation of the rest of the peripheral oscillators was found in the oscillatory neural network of phase oscillators. A possible use of the dynamical regimes for the information coding and processing is briefly discussed.

Models, Biological↗

Global exponential stability of hybrid bidirectional associative memory neural networks with discrete delays.

In this paper, the dynamical characteristics of hybrid bidirectional associative memory neural networks with constant transmission delays are investigated. Without assuming symmetry of synaptic connection weights and monotonicity and differentiability of activation functions, Halanay-type inequalities (which are different from the approach of constructing Lyapunov functionals) are employed to derive the delay-independent sufficient conditions under which the networks converge exponentially to the equilibria associated with temporally uniform external inputs. Our results are less conservative and restrictive than previously known results.

Algorithms↗

Neural network modeling for on-line estimation of nutrient dynamics in a sequentially-operated batch reactor.

In monitoring and controlling wastewater treatment processes, on-line information of nutrient dynamics is very important. However, these variables are determined with a significant time delay. Although the final effluent quality can be analyzed after this delay, it is often too late to make proper adjustments. In this paper, a neural network approach, a software sensor, was proposed to overcome this problem. Software sensor refers to a modeling approach inferring hard-to-measure process variables from other on-line measurable process variables. A bench-scale sequentially-operated batch reactor (SBR) used for advanced wastewater treatment (BOD plus nutrient removal) was employed to develop the neural network model. In order to improve the network performance, the structure of neural network was arranged in such a way of reflecting the change of operational conditions within a cycle. Real-time estimation of PO3-(4), NO-3, and NH+4 concentrations was successfully carried out with the on-line information of the SBR system only.

Anaerobiosis↗

Modeling change-pattern-value dynamics on land use: an integrated GIS and artificial neural networks approach.

The use of spatial methods to detect and characterize changes in land use has been attracting increasing attention from researchers. The objectives of this article were to formulate the dynamics of land use on the temporal and spatial dimensions from the perspectives of the Change-Pattern-Value (CPV) and driving mechanism, based on multitemporal remote sensing data and socioeconomic data. The Artificial Neural Networks were used to identify the factors driving changes in land use. The Pearl River Delta Region of southeast China, which was experiencing rapid economic growth and widespread land conversion, has been selected as the study region. The results show that from 1985 to 2000 in the study region (1) the most prominent characteristics of change in land use were the expansion of the urban land at the expense of farmland, forests, and grasslands, (2) the land-use pattern was being optimized during this period, (3) in an analysis of value, built-up land can yield a return of more than 30 times that of farmland, water area, and forests lands, and (4) rapid economic development, growth in population, and the development of an infrastructure were major driving factors behind ecological land loss and the nonecological land expansion.

Agriculture↗

Noise, regularizers, and unrealizable scenarios in online learning from restricted training sets.

We study the dynamics of online learning in multilayer neural networks where training examples are sampled with repetition and where the number of examples scales with the number of network weights. The analysis is carried out using the dynamical replica method aimed at obtaining a closed set of coupled equations for a set of macroscopic variables from which both training and generalization errors can be calculated. We focus on scenarios whereby training examples are corrupted by additive Gaussian output noise and regularizers are introduced to improve the network performance. The dependence of the dynamics on the noise level, with and without regularizers, is examined, as well as that of the asymptotic values obtained for both training and generalization errors. We also demonstrate the ability of the method to approximate the learning dynamics in structurally unrealizable scenarios. The theoretical results show good agreement with those obtained from computer simulations.

Algorithms↗

Non-linear analysis of the electroencephalogram in Creutzfeldt-Jakob disease.

Creutzfeldt-Jakob disease is a rare, neurological, dementing disorder characterised by periodic sharp waves in the electroencephalogram (EEG). Non-linear analysis of these EEG changes may provide insight into the abnormal dynamics of cortical neural networks in this disorder. Babloyantz et al. have suggested that the periodic sharp waves reflect low-dimensional chaotic dynamics in the brain. In the present study this hypothesis was re-examined using newly developed techniques for non-linear time series analysis. We analysed the EEG of a patient with autopsy-proven Creutzfeldt-Jakob disease using the method of non-linear forecasting as introduced by Sugihara and May, and we tested for non-linearity with amplitude-adjusted, phase-randomised surrogate data. Two epochs with generalised periodic sharp waves showed clear evidence for non-linearity. These epochs could be predicted better and further ahead in time than most of the irregular background activity. Testing against cycle-randomised surrogate data and close inspection of the periodograms showed that the non-linearity of the periodic sharp waves may be better explained by quasi-periodicity than by low-dimensional chaos. The EEG further displayed at least one example of a sudden, large qualitative change in the dynamics, highly suggestive of a bifurcation. The presence of quasi-periodicity and bifurcations strongly argues for the use of a non-linear model to describe the EEG in Creutzfeldt-Jakob disease.

Creutzfeldt-Jakob Syndrome↗

Unified description of temperature-dependent hydrogen-bond rearrangements in liquid water.

The unique chemical and physical properties of liquid water are a direct result of its highly directional hydrogen-bond (HB) network structure and associated dynamics. However, despite intense experimental and theoretical scrutiny spanning more than four decades, a coherent description of this HB network remains elusive. The essential question of whether continuum or multicomponent ("intact," "broken bond," etc.) models best describe the HB interactions in liquid water has engendered particularly intense discussion. Most notably, the temperature dependence of water's Raman spectrum has long been considered to be among the strongest evidence for a multicomponent distribution. Using a combined experimental and theoretical approach, we show here that many of the features of the Raman spectrum that are considered to be hallmarks of a multistate system, including the asymmetric band profile, the isosbestic (temperature invariant) point, and van't Hoff behavior, actually result from a continuous distribution. Furthermore, the excellent agreement between our newly remeasured Raman spectra and our model system further supports the locally tetrahedral description of liquid water, which has recently been called into question.

Computer Simulation↗

Simulation study on dynamics transition in neuronal activity during sleep cycle by using asynchronous and symmetry neural network model.

We have found that single neuronal activities in different regions in the brain commonly exhibit the distinct dynamics transition during sleep-waking cycle in cats. Especially, power spectral densities of single neuronal activities change their profiles from the white to the 1/f along with sleep cycle from slow wave sleep (SWS) to paradoxical sleep (PS). Each region has different neural network structure and physiological function. This suggests a globally working mechanism may be underlying the dynamics transition we concern. Pharmacological studies have shown that a change in a wide-spread serotonergic input to these regions possibly causes the neuronal dynamics transition during sleep cycle. In this paper, based on these experimental results, an asynchronous and symmetry neural network model including inhibitory input, which represents the role of the serotonergic system, is utilized to examine the reality of our idea that the inhibitory input level varying during sleep cycle induce that transition. Simulation results show that the globally applied inhibitory input can control the dynamics of single neuronal state evolution in the artificial neural network: 1/f-like power spectral density profiles result under weak inhibition, which possibly corresponds to PS, and white profiles under strong inhibition, which possibly corresponds to SWS. An asynchronous neural network is known to change its state according to its energy function. The geometrical structure of network energy function is thought to vary along with the change in inhibitory level, which is expected to cause the dynamics transition of neuronal state evolution in the network model. These simulation results support the possibility that the serotonergic system is essential for the dynamics transition of single neuronal activities during sleep cycle.

Animals↗

Mean-driven and fluctuation-driven persistent activity in recurrent networks.

Spike trains from cortical neurons show a high degree of irregularity, with coefficients of variation (CV) of their interspike interval (ISI) distribution close to or higher than one. It has been suggested that this irregularity might be a reflection of a particular dynamical state of the local cortical circuit in which excitation and inhibition balance each other. In this "balanced" state, the mean current to the neurons is below threshold, and firing is driven by current fluctuations, resulting in irregular Poisson-like spike trains. Recent data show that the degree of irregularity in neuronal spike trains recorded during the delay period of working memory experiments is the same for both low-activity states of a few Hz and for elevated, persistent activity states of a few tens of Hz. Since the difference between these persistent activity states cannot be due to external factors coming from sensory inputs, this suggests that the underlying network dynamics might support coexisting balanced states at different firing rates. We use mean field techniques to study the possible existence of multiple balanced steady states in recurrent networks of current-based leaky integrate-and-fire (LIF) neurons. To assess the degree of balance of a steady state, we extend existing mean-field theories so that not only the firing rate, but also the coefficient of variation of the interspike interval distribution of the neurons, are determined self-consistently. Depending on the connectivity parameters of the network, we find bistable solutions of different types. If the local recurrent connectivity is mainly excitatory, the two stable steady states differ mainly in the mean current to the neurons. In this case, the mean drive in the elevated persistent activity state is suprathreshold and typically characterized by low spiking irregularity. If the local recurrent excitatory and inhibitory drives are both large and nearly balanced, or even dominated by inhibition, two stable states coexist, both with subthreshold current drive. In this case, the spiking variability in both the resting state and the mnemonic persistent state is large, but the balance condition implies parameter fine-tuning. Since the degree of required fine-tuning increases with network size and, on the other hand, the size of the fluctuations in the afferent current to the cells increases for small networks, overall we find that fluctuation-driven persistent activity in the very simplified type of models we analyze is not a robust phenomenon. Possible implications of considering more realistic models are discussed.

Action Potentials↗

Synchrony in binary-oscillator networks with local couplings.

Since synchronous oscillations in the visual cortex may be responsible for some features of visual scene, many works have been done to study the dynamics of oscillatory neural networks. Most of the oscillator network models rely on a global connection to reach synchronization. Here we propose a class of neural network models based on a simplified binary-oscillator, and find that the neurons of networks with local couplings will be in global synchrony under some criteria.

Models, Neurological↗

Network reset: a simplified overarching theory of locus coeruleus noradrenaline function.

Unraveling the functional role of neuromodulatory systems has been a major challenge for cognitive neuroscience, giving rise to theories ranging from a simple role in vigilance to complex models concerning decision making, prediction errors or unexpected uncertainty. A new, simplified and overarching theory of noradrenaline function is inspired by an invertebrate model: neuromodulators in crustacea abruptly interrupt activity in neural networks and reorganize the elements into new functional networks determining the behavioral output. Analogously in mammals, phasic activation of noradrenergic neurons of the locus coeruleus in time with cognitive shifts could provoke or facilitate dynamic reorganization of target neural networks, permitting rapid behavioral adaptation to changing environmental imperatives. Detailed analysis and discussion of extensive electrophysiological data from the locus coeruleus of rats and monkeys in controlled behavioral situations is provided here to support this view. This simplified 'new look' at locus coeruleus noradrenaline function redirects the challenge of understanding neuromodulatory systems towards their target networks, particularly to the dynamics of their interactions and how they organize adaptive behavior.

Adaptation, Physiological↗

Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons.

We have developed a two-compartment, eight-variable model of a CA3 pyramidal cell as a reduction of a complex 19-compartment cable model [Traub et al, 1991]. Our reduced model segregates the fast currents for sodium spiking into a proximal, soma-like, compartment and the slower calcium and calcium-mediated currents into a dendrite-like compartment. In each model periodic bursting gives way to repetitive soma spiking as somatic injected current increases. Steady dendritic stimulation can produce periodic bursting of significantly higher frequency (8-20 Hz) than can steady somatic input (< 8 Hz). Bursting in our model occurs only for an intermediate range of electronic coupling conductance. It depends on the segregation of channel types and on the coupling current that flows back-and-forth between compartments. When the soma and dendrite are tightly coupled electrically, our model reduces to a single compartment and does not burst. Network simulations with our model using excitatory AMPA and NMDA synapses (without inhibition) give results similar to those obtained with the complex cable model [Traub et al, 1991; Traub et al, 1992]. Brief stimulation of a single cell in a resting network produces multiple synchronized population bursts, with fast AMPA synapses providing the dominant synchronizing mechanism. The number of bursts increases with the level of maximal NMDA conductance. For high enough maximal NMDA conductance synchronized bursting repeats indefinitely. We find that two factors can cause the cells to desynchronize when AMPA synapses are blocked: heterogeneity of properties amongst cells and intrinsically chaotic burst dynamics. But even when cells are identical, they may synchronize only approximately rather than exactly. Since our model has a limited number of parameters and variables, we have studied its cellular and network dynamics computationally with relative ease and over wide parameter ranges. Thereby, we identify some qualitative features that parallel or are distinguished from those of other neuronal systems; e.g., we discuss how bursting here differs from that in some classical models.

Animals↗

Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data.

Biological signaling networks process extracellular cues to control important cell decisions such as death-survival, growth-quiescence, and proliferation-differentiation. After receptor activation, intracellular signaling proteins change in abundance, modification state, and enzymatic activity. Many of the proteins in signaling networks have been identified, but it is not known how signaling molecules work together to control cell decisions. To begin to address this issue, we report the use of partial least squares regression as an analytical method to glean signal-response relationships from heterogeneous multivariate signaling data collected from HT-29 human colon carcinoma cells stimulated to undergo programmed cell death. By partial least squares modeling, we relate dynamic and quantitative measurements of 20-30 intracellular signals to cell survival after treatment with tumor necrosis factor alpha (a death factor) and insulin (a survival factor). We find that partial least squares models can distinguish highly informative signals from redundant uninformative signals to generate a reduced model that retains key signaling features and signal-response relationships. In these models, measurements of biochemical characteristics, based on very different techniques (Western blots, kinase assays, etc.), are grouped together as covariates, showing that heterogenous data have been effectively fused. Importantly, informative protein predictors of cell responses are always multivariate, demonstrating the multicomponent nature of the decision process.

Apoptosis↗

De novo formation of cytokeratin filament networks originates from the cell cortex in A-431 cells.

Of the three major cytoskeletal filament systems, the intermediate filaments are the least understood. Since they differ fundamentally from the actin- and microtubule-based networks by their lack of polarity, it has remained a mystery how and where these principally endless filaments are formed. Using a recently established epithelial cell system in which fluorescently labeled intermediate filaments of the cytokeratin type can be monitored in living cells, we address these issues. By multidimensional time-lapse fluorescence microscopy, we examine de novo intermediate filament network formation from non-filamentous material at the end of mitosis and show that it mirrors disassembly. It is demonstrated that filament formation is initiated from the cell cortex without focal preference after cytokinesis. Furthermore, it is shown that this process is dependent on energy, on the integrity of the actin filament network and the microtubule system, and that it can be inhibited by the tyrosine phosphatase inhibitor pervanadate. Based on these observations, a two-step working model is proposed involving (1) interactions within the planar cortical layer acting as an organizing center forming a two-dimensional network and (2) subsequent radial dynamics facilitating the formation of a mature three-dimensional network.

Cell Division↗