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PubMed2006

Fractal rigidity by enhanced sympatho-vagal antagonism in heartbeat interval dynamics elicited by central application of corticotropin-releasing factor in mice.

The dynamics of heartbeat interval fluctuations were studied in awake unrestrained mice following intracerebroventricular application of the neuropeptide corticotropin-releasing factor (CRF). The cardiac time series derived from telemetric ECG monitoring were analyzed by non-parametric techniques of nonlinear signal processing: delay-vector variance (DVV) analysis, higher-order variability (HOV) analysis, empirical mode decomposition (EMD), multiscale embedding-space decomposition (MESD), multiexponent multifractal (MEMF) analysis. The analyses support the conjecture that cardiac dynamics of normal control mice has both deterministic and stochastic elements, is nonstationary, nonlinear, and exerts multifractal properties. Central application of CRF results in bradycardia and increased variability of the beat-to-beat fluctuations. The altered dynamical properties elicited by CRF reflect a significant loss of intrinsic structural complexity of cardiac control which is due to central neuroautonomic hyperexcitation, i.e., enhanced sympatho-vagal antagonism. The change in dynamical complexity is characterized by an effect referred to as fractal rigidity, leading to a significant impairment of adaptability to extrinsic challenges in a fluctuating environment. The impact of dynamical neurocardiopathy as a major precipiting factor for the propensity of cardiac arrhythmias or sudden cardiac death by unchecked central CRF release in significant acute life events in man is critically discussed.

Animals↗
PubMed1999

Biological effects due to weak electric and magnetic fields: the temperature variation threshold.

A large number of epidemiological and experimental studies suggest that prolonged (>100 s) weak 50-60-Hz electric and magnetic field (EMF) exposures may cause biological effects(NIEHS Working Group, NIH, 1998; Bersani, 1999). We show, however, that for typical temperature sensitivities of biochemical processes, realistic temperature variations during long exposures raise the threshold exposure by two to three orders of magnitude over a fundamental value, independent of the biophysical coupling mechanism. Temperature variations have been omitted in previous theoretical analyses of possible weak field effects, particularly stochastic resonance (Bezrukov and Vodyanoy 1997a. Nature. 385:319-321; Astumian et al., 1997 Nature. 338:632-633; Bezrukov and Vodyanoy, 1997b. Nature. 338:663; Dykman and McClintock, 1998. Nature. 391:344; McClintock, 1998;. Gammaitoni et al., 1998. Rev. Mod. Phys. 70:223-287). Although sensory systems usually respond to much shorter (approximately 1 s) exposures and can approach fundamental limits (Bialek, 1987 Annu. Rev. Biophys. Biophys. Chem. 16:455-468; Adair et al, 1998. Chaos. 8:576-587), our results significantly decrease the plausibility of effects for nonsensory biological systems due to prolonged, weak-field exposures.

Animals↗
PubMed1999

Recurrent sampling models for the Helmholtz machine.

Many recent analysis-by-synthesis density estimation models of cortical learning and processing have made the crucial simplifying assumption that units within a single layer are mutually independent given the states of units in the layer below or the layer above. In this article, we suggest using either a Markov random field or an alternative stochastic sampling architecture to capture explicitly particular forms of dependence within each layer. We develop the architectures in the context of real and binary Helmholtz machines. Recurrent sampling can be used to capture correlations within layers in the generative or the recognition models, and we also show how these can be combined.

Animals↗
PubMed2003

A PI-PD controller design for control of unstable and integrating processes.

A model-based PI-PD controller design, where the PD feedback is used to change the poles of the plant transfer function to more desirable locations for control by a PI controller, is proposed. Several procedures for obtaining the parameters of the PI-PD controller are possible but one of the simplest approaches, which is used in this paper, is to employ integral squared time error standard forms as this enables the design to be completed using simple algebra. Also, an exact method for model extraction of some integrating processes which may or may not have a time delay is presented. The method is compared with several existing methods to control integrating processes and it is shown that the proposed method is superior to existing ones.

Computer Simulation↗
PubMed2004

Reliable biological communication with realistic constraints.

Communication in biological systems must deal with noise and metabolic or temporal constraints. We include these constraints into information theory to obtain the distributions of signal usage corresponding to a maximal rate of information transfer given any noise structure and any constraints. Generalized versions of the Boltzmann, Gaussian, or Poisson distributions are obtained for linear, quadratic and temporal constraints, respectively. These distributions are shown to imply that biological transformations must dedicate a larger output range to the more probable inputs and less to the outputs with higher noise and higher participation in the constraint. To show the general theory of reliable communication at work, we apply these results to biochemical and neuronal signaling. Noncooperative enzyme kinetics is shown to be suited for transfer of a high signal quality when the input distribution has a maximum at low concentrations while cooperative kinetics for near-Gaussian input statistics. Neuronal codes based on spike rates, spike times or bursts have to balance signal quality and cost-efficiency and at the network level imply sparseness and uncorrelation within the limits of noise, cost, and processing operations.

Action Potentials↗
PubMed1998

Change-point analysis of neuron spike train data.

In many medical experiments, data are collected across time, over a number of similar trials, or over a number of experimental units. As is the case of neuron spike train studies, these data may be in the form of counts of events per unit of time. These counts may be correlated within each trial. It is often of interest to know if the introduction of an intervention, such as the application of a stimulus, affects the distribution of the counts over the course of the experiment. In such investigations, each trial generates a sequence of data that may or may not contain a change in distribution at some point in time. Each sequence of integer counts can be viewed as arising from a Poisson process and are therefore independently distributed or as an integer-valued time series that allows for correlations between these counts. The main aim of this paper is to show how the ensemble of sample paths may be used to make inference about the distribution of the instantaneous times of change in a given population. This will be accomplished using a Bayesian hierarchical model for these change-points in time. A bonus of these models is they also allow for inference about the probability of a change in each unit and the magnitude of the effects, if any. The use of such change-point models on integer-valued time series is illustrated on neuron spike train data, although the methods can be applied to other situations where integer-valued processes arise.

Action Potentials↗
PubMedDate not supplied

[The nonstability of the EEG: a methodological and experimental analysis].

Theoretical, methodological and methodical aspects on nonstationary EEGs, that is, EEGs whose patterns undergo changes with time are reviewed. The piecewise description of the EEG is confirmed on the base of the own data and results of another investigators. It is analysed the basic approach to statistical evaluation of nonstationary EEGs in the terms of quasi-stationary segments. The special attention is devoted tho the data of segmental organization of the bioelectrical field of the cortex. Taking into consideration new experimental data concerning the time consistency of the segmental descriptions of regional EEGs it is suggested conception of "operational synchrony" as a form of discretial cooperation of cortex process. The theoretical explanations of the phenomenology for piecewise functioning of neuron nets are discussed.

Cerebral Cortex↗
PubMed2003

Processivity of the single-headed kinesin KIF1A through biased binding to tubulin.

Conventional isoforms of the motor protein kinesin behave functionally not as 'single molecules' but as 'two molecules' paired. This dimeric structure poses a barrier to solving its mechanism. To overcome this problem, we used an unconventional kinesin KIF1A (refs 5, 6) as a model molecule. KIF1A moves processively as an independent monomer, and can also work synergistically as a functional dimer. Here we show, by measuring its movement with an optical trapping system, that a single ATP hydrolysis triggers a single stepping movement of a single KIF1A monomer. The step size is distributed stochastically around multiples of 8 nm with a gaussian-like envelope and a standard deviation of 15 nm. On average, the step is directional to the microtubule's plus-end against a load force of up to 0.15 pN. As the source for this directional movement, we show that KIF1A moves to the microtubule's plus-end by approximately 3 nm on average on binding to the microtubule, presumably by preferential binding to tubulin on the plus-end side. We propose a simple physical formulation to explain the movement of KIF1A.

Adenosine Diphosphate↗
PubMed2006

A bottom-up approach to gene regulation.

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.

Arabinose↗
PubMed1995

Effect of a chiral impulse on the weak interaction induced handedness in a prebiotic medium.

Previously we have carried out simulation of the Weak Neutral Current and symmetry breaking transition bifurcation process addressing some of the issues raised by critics of the approach. We now include the effects of a chiral impulse on the transition and show that under certain cases this could greatly alter the transition time. Examples of a chiral impulse could be a nearby Supernova and an impulse of beta-emitters. We briefly discuss the possibility of these processes in the early solar system.

Biophysical Phenomena↗
PubMed2006

Recursive syntactic pattern learning by songbirds.

Humans regularly produce new utterances that are understood by other members of the same language community. Linguistic theories account for this ability through the use of syntactic rules (or generative grammars) that describe the acceptable structure of utterances. The recursive, hierarchical embedding of language units (for example, words or phrases within shorter sentences) that is part of the ability to construct new utterances minimally requires a 'context-free' grammar that is more complex than the 'finite-state' grammars thought sufficient to specify the structure of all non-human communication signals. Recent hypotheses make the central claim that the capacity for syntactic recursion forms the computational core of a uniquely human language faculty. Here we show that European starlings (Sturnus vulgaris) accurately recognize acoustic patterns defined by a recursive, self-embedding, context-free grammar. They are also able to classify new patterns defined by the grammar and reliably exclude agrammatical patterns. Thus, the capacity to classify sequences from recursive, centre-embedded grammars is not uniquely human. This finding opens a new range of complex syntactic processing mechanisms to physiological investigation.

Acoustic Stimulation↗
PubMed1991

Radiobiological fundamentals in radioepidemiology and radiation protection.

Radiation is a convenient tool to study fundamental processes of life. Biological effects of irradiation may result from indirect actions which are mediated by free radicals (e.g. OH-radicals) or from direct actions which involve ionizations in the DNA and other biomolecules. Damage to the DNA is the principal, but not exclusive target for cell death, loss of reproductive integrity, mutation, cancer, developmental anomalies and other radiobiological effects. Repair of damaged DNA and cellular recovery processes play an essential role in affecting the survival of cells. Dose, dose rate, radiation quality, biological and chemical modifiers also have a pronounced effect upon the extent of radiation responses. The biological effects of ionizing radiation are somatic or hereditary and can further be classified into stochastic and deterministic effects. For radiation epidemiology and protection the stochastic action is more relevant because the probability of an effect is a function of dose, without a threshold. Induction of cancer, hereditary diseases and probably also mental retardation are regarded as stochastic effects.

Cell Survival↗
PubMed1983

Algorithm for calculating theoretical probabilities of patterns generated by sequential inequality testing.

Temporal patterns of extracellularly monitored single neuronal impulses or 'spike' trains can be viewed as stochastic point processes that carry information from one neuron to another. There are indications that the dependencies among sequential spike intervals, if treated as sequential inequality patterns, encompass much more than 7 spike intervals. Hence, to fill the gap between the available knowledge and the experimental need, a limited stochastic model of inequality patterns was reviewed and its inherent symmetries were explored. The symmetric attributes of the model, based on three through seven spike intervals, led to an algorithm which allows one to readily compute the theoretical distribution of inequality patterns of considerable complexity and length suitable for studying neuronal responses and other phenomena.

Action Potentials↗
PubMed1992

Bone marrow regeneration under cytotoxic drug regimens: behaviour ranging from homeostasis to unpredictability in a model for hemopoietic differentiation.

In the process of hemopoiesis, bone marrow stem cells differentiate into the various types of mature blood cells. We present a model for bone marrow dynamics, which retrieves its ability to continuously modulate the balance between self-renewal and differentiation, even under periodic cytodestructive perturbations. Yet, a temporally stochastic perturbation results in chaotic-like behaviour which has no deterministic source.

Animals↗
PubMed2001

A power law for cells.

Darwin observed that multiple, lowly organized, rudimentary, or exaggerated structures show increased relative variability. However, the cellular basis for these laws has never been investigated. Some animals, such as the nematode Caenorhabditis elegans, are famous for having organs that possess the same number of cells in all individuals, a property known as eutely. But for most multicellular creatures, the extent of cell number variability is unknown. Here we estimate variability in organ cell number for a variety of animals, plants, slime moulds, and volvocine algae. We find that the mean and variance in cell number obey a power law with an exponent of 2, comparable to Taylor's law in ecological processes. Relative cell number variability, as measured by the coefficient of variation, differs widely across taxa and tissues, but is generally independent of mean cell number among homologous tissues of closely related species. We show that the power law for cell number variability can be explained by stochastic branching process models based on the properties of cell lineages. We also identify taxa in which the precision of developmental control appears to have evolved. We propose that the scale independence of relative cell number variability is maintained by natural selection.

Animals↗
PubMed1999

Dynamic paradigms for human mortality and aging.

Hazard models are often applied to mortality data of humans and other species so that the parameter estimates made for those models can be used to make inferences about the biology, and comparative biology, of aging processes. Enough longitudinal data on physiological and functional changes in humans now exist to know that the age trajectory of the physiological state of individuals is multidimensional, stochastic, and plastic. Thus, to fully assess the biological significance of existing longitudinal data on human aging and mortality processes, multivariate stochastic process models must be developed that are biologically detailed and valid. This requires assessing genetic mechanisms controlling human longevity and rates of aging, developing models of how those traits may have evolved, and developing statistical methods for identifying gene environment interactions. This article examines the theoretical basis for such models and the biological rationale of their parametric structure. Several examples are given.

Aged↗
PubMed2002

An intelligent system for multivariate statistical process monitoring and diagnosis.

A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. Multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present.

Algorithms↗