Showing papers in "Chaos in 2001"
TL;DR: It is suggested that bistability may be a common means of producing all-or-none responses and a type of biochemical memory in Xenopus oocyte maturation.
Abstract: Xenopus oocyte maturation is an example of an all-or-none, irreversible cell fate induction process. In response to a submaximal concentration of the steroid hormone progesterone, a given oocyte may either mature or not mature, but it can exist in intermediate states only transiently. Moreover, once an oocyte has matured, it will remain arrested in the mature state even after the progesterone is removed. It has been hypothesized that the all-or-none character of oocyte maturation, and some aspects of the irreversibility of maturation, arise out of the bistability of the signal transduction system that triggers maturation. The bistability, in turn, is hypothesized to arise from the way the signal transducers are organized into a signaling circuit that includes positive feedback (which makes it so that the system cannot rest in intermediate states) and ultrasensitivity (which filters small stimuli out of the feedback loop, allowing the system to have a stable off-state). Here we review two simple graphical methods that are commonly used to analyze bistable systems, discuss the experimental evidence for bistability in oocyte maturation, and suggest that bistability may be a common means of producing all-or-none responses and a type of biochemical memory.
381 citations
TL;DR: The degree to which concepts developed in statistical physics can be usefully applied to physiological signals is explored, and very recent work that quantifies multifractal features in these cascades is described, and the discovery that the multifractional structure of healthy dynamics is lost with congestive heart failure is described.
Abstract: We explore the degree to which concepts developed in statistical physics can be usefully applied to physiological signals. We illustrate the problems related to physiologic signal analysis with representative examples of human heartbeat dynamics under healthy and pathologic conditions. We first review recent progress based on two analysis methods, power spectrum and detrended fluctuation analysis, used to quantify long-range power-law correlations in noisy heartbeat fluctuations. The finding of power-law correlations indicates presence of scale-invariant, fractal structures in the human heartbeat. These fractal structures are represented by self-affine cascades of beat-to-beat fluctuations revealed by wavelet decomposition at different time scales. We then describe very recent work that quantifies multifractal features in these cascades, and the discovery that the multifractal structure of healthy dynamics is lost with congestive heart failure. The analytic tools we discuss may be used on a wide range of physiologic signals. © 2001 American Institute of Physics. @DOI: 10.1063/1.1395631#
374 citations
TL;DR: The core of the paper is comprised of a formal description of feedback circuits and unions of disjoint circuits, and a normalization of the system versus one of the circuits, which permits an entirely general description in terms of a common diagram in the "circuit space."
Abstract: A biological introduction serves to remind us that differentiation is an epigenetic process, that multistationarity can account for epigenetic differences, including those involved in cell differentiation, and that positive feedback circuits are a necessary condition for multistationarity and, by inference, for differentiation. The core of the paper is comprised of a formal description of feedback circuits and unions of disjoint circuits. We introduce the concepts of full-circuit (a circuit or union of disjoint circuits which involves all the variables of the system), and of ambiguous circuit (a circuit whose sign depends on the location in phase space). We describe the partition of phase space (a) according to the signs of the ambiguous circuits, and (b) according to the signs of the eigenvalues or their real part. We introduce a normalization of the system versus one of the circuits; in two variables, this permits an entirely general description in terms of a common diagram in the “circuit space.” The paper ends with general statements concerning the requirements for multistationarity, stable periodicity, and deterministic chaos.
312 citations
TL;DR: This generalized logical description provides an image whose qualitative fit with the differential description is quite remarkable, and which tells which constraints on the logical parameters must be fulfilled in order for any circuit (or combination of circuits) to be functional.
Abstract: Circuits and their involvement in complex dynamics are described in differential terms in Part I of this work. Here, we first explain why it may be appropriate to use a logical description, either by itself or in symbiosis with the differential description. The major problem of a logical description is to find an adequate way to involve time. The procedure we adopted differs radically from the classical one by its fully asynchronous character. In Sec. II we describe our “naive” logical approach, and use it to illustrate the major laws of circuitry (namely, the involvement of positive circuits in multistationarity and of negative circuits in periodicity) and in a biological example. Already in the naive description, the major steps of the logical description are to: (i) describe a model as a set of logical equations, (ii) derive the state table from the equations, (iii) derive the graph of the sequences of states from the state table, and (iv) determine which of the possible pathways will be actually followed in terms of time delays. In the following sections we consider multivalued variables where required, the introduction of logical parameters and of logical values ascribed to the thresholds, and the concept of characteristic state of a circuit. This generalized logical description provides an image whose qualitative fit with the differential description is quite remarkable. A major interest of the generalized logical description is that it implies a limited and often quite small number of possible combinations of values of the logical parameters. The space of the logical parameters is thus cut into a limited number of boxes, each of which is characterized by a defined qualitative behavior of the system. Our analysis tells which constraints on the logical parameters must be fulfilled in order for any circuit (or combination of circuits) to be functional. Functionality of a circuit will result in multistationarity (in the case of a positive circuit) or in a cycle (in the case of a negative circuit). The last sections deal with “more about time delays” and “reverse logic,” an approach that aims to proceed rationally from facts to models.
283 citations
TL;DR: The engineered control of cellular function through the design of synthetic genetic networks is becoming plausible, and a naturally occurring network can be used as a parts list for artificial network design, and how model formulation leads to computational and analytical approaches relevant to nonlinear dynamics and statistical physics are shown.
Abstract: The engineered control of cellular function through the design of synthetic genetic networks is becoming plausible. Here we show how a naturally occurring network can be used as a parts list for artificial network design, and how model formulation leads to computational and analytical approaches relevant to nonlinear dynamics and statistical physics. We first review the relevant work on synthetic gene networks, highlighting the important experimental findings with regard to genetic switches and oscillators. We then present the derivation of a deterministic model describing the temporal evolution of the concentration of protein in a single-gene network. Bistability in the steady-state protein concentration arises naturally as a consequence of autoregulatory feedback, and we focus on the hysteretic properties of the protein concentration as a function of the degradation rate. We then formulate the effect of an external noise source which interacts with the protein degradation rate. We demonstrate the utility of such a formulation by constructing a protein switch, whereby external noise pulses are used to switch the protein concentration between two values. Following the lead of earlier work, we show how the addition of a second network component can be used to construct a relaxation oscillator, whereby the system is driven around the hysteresis loop. We highlight the frequency dependence on the tunable parameter values, and discuss design plausibility. We emphasize how the model equations can be used to develop design criteria for robust oscillations, and illustrate this point with parameter plots illuminating the oscillatory regions for given parameter values. We then turn to the utilization of an intrinsic cellular process as a means of controlling the oscillations. We consider a network design which exhibits self-sustained oscillations, and discuss the driving of the oscillator in the context of synchronization. Then, as a second design, we consider a synthetic network with parameter values near, but outside, the oscillatory boundary. In this case, we show how resonance can lead to the induction of oscillations and amplification of a cellular signal. Finally, we construct a toggle switch from positive regulatory elements, and compare the switching properties for this network with those of a network constructed using negative regulation. Our results demonstrate the utility of model analysis in the construction of synthetic gene regulatory networks.
280 citations
TL;DR: This three-part review reviews several elements of design that enter into the characterization of elementary gene circuits in prokaryotic organisms and identifies four design principles that make specific predictions regarding the realizability of alternative gene circuits and their response to phased environmental cues.
Abstract: The control of gene expression involves complex circuits that exhibit enormous variation in design. For years the most convenient explanation for these variations was historical accident. According to this view, evolution is a haphazard process in which many different designs are generated by chance; there are many ways to accomplish the same thing, and so no further meaning can be attached to such different but equivalent designs. In recent years a more satisfying explanation based on design principles has been found for at least certain aspects of gene circuitry. By design principle we mean a rule that characterizes some biological feature exhibited by a class of systems such that discovery of the rule allows one not only to understand known instances but also to predict new instances within the class. The central importance of gene regulation in modern molecular biology provides strong motivation to search for more of these underlying design principles. The search is in its infancy and there are undoubtedly many design principles that remain to be discovered. The focus of this three-part review will be the class of elementary gene circuits in bacteria. The first part reviews several elements of design that enter into the characterization of elementary gene circuits in prokaryotic organisms. Each of these elements exhibits a variety of realizations whose meaning is generally unclear. The second part reviews mathematical methods used to represent, analyze, and compare alternative designs. Emphasis is placed on particular methods that have been used successfully to identify design principles for elementary gene circuits. The third part reviews four design principles that make specific predictions regarding (1) two alternative modes of gene control, (2) three patterns of coupling gene expression in elementary circuits, (3) two types of switches in inducible gene circuits, and (4) the realizability of alternative gene circuits and their response to phased environmental cues. In each case, the predictions are supported by experimental evidence. These results are important for understanding the function, design, and evolution of elementary gene circuits.
196 citations
TL;DR: This work proposes a mechanism for the control system, converts it into a set of 14 differential and algebraic equations, studies these equations by numerical simulation and bifurcation theory, and compares the results to the physiology of wild-type and mutant cells.
Abstract: Much is known about the genes and proteins controlling the cell cycle of fission yeast. Can these molecular components be spun together into a consistent mechanism that accounts for the observed behavior of growth and division in fission yeast cells? To answer this question, we propose a mechanism for the control system, convert it into a set of 14 differential and algebraic equations, study these equations by numerical simulation and bifurcation theory, and compare our results to the physiology of wild-type and mutant cells. In wild-type cells, progress through the cell cycle (G1-->S-->G2-->M) is related to cyclic progression around a hysteresis loop, driven by cell growth and chromosome alignment on the metaphase plate. However, the control system operates much differently in double-mutant cells, wee1(-) cdc25Delta, which are defective in progress through the latter half of the cell cycle (G2 and M phases). These cells exhibit "quantized" cycles (interdivision times clustering around 90, 160, and 230 min). We show that these quantized cycles are associated with a supercritical Hopf bifurcation in the mechanism, when the wee1 and cdc25 genes are disabled. (c) 2001 American Institute of Physics.
172 citations
TL;DR: The first natural and visible examples of Hamiltonian systems with divided phase space allowing a rigorous mathematical analysis are presented and the first example of completely understood (rigorously studied) billiards in domains with a fractal boundary is given.
Abstract: We present the first natural and visible examples of Hamiltonian systems with divided phase space allowing a rigorous mathematical analysis. The simplest such family (mushrooms) demonstrates a continuous transition from a completely chaotic system (stadium) to a completely integrable one (circle). In the course of this transition, an integrable island appears, grows and finally occupies the entire phase space. We also give the first examples of billiards with a “chaotic sea” (one ergodic component) and an arbitrary (finite or infinite) number of KAM islands and the examples with arbitrary (finite or infinite) number of chaotic (ergodic) components with positive measure coexisting with an arbitrary number of islands. Among other results is the first example of completely understood (rigorously studied) billiards in domains with a fractal boundary.
149 citations
TL;DR: In this paper, the authors studied the effect of a common source of noise on the trajectories of chaotic systems, addressing some contradictory results present in the literature, and gave numerical evidence that the addition of common noise to different trajectories, which start from different initial conditions, leads eventually to their perfect synchronization.
Abstract: We study the effect that the injection of a common source of noise has on the trajectories of chaotic systems, addressing some contradictory results present in the literature. We present particular examples of one-dimensional maps and the Lorenz system, both in the chaotic region, and give numerical evidence showing that the addition of a common noise to different trajectories, which start from different initial conditions, leads eventually to their perfect synchronization. When synchronization occurs, the largest Lyapunov exponent becomes negative. For a simple map we are able to show this phenomenon analytically. Finally, we analyze the structural stability of the phenomenon.
131 citations
TL;DR: P perturbation methods are used to show how equations for attracting invariant (slow) manifolds can be constructed by a geometric approach based on functional equations derived directly from the differential equations.
Abstract: After the decay of transients, the behavior of a set of differential equations modeling a chemical or biochemical system generally rests on a low-dimensional surface which is an invariant manifold of the flow. If an equation for such a manifold can be obtained, the model has effectively been reduced to a smaller system of differential equations. Using perturbation methods, we show that the distinction between rapidly decaying and long-lived (slow) modes has a rigorous basis. We show how equations for attracting invariant (slow) manifolds can be constructed by a geometric approach based on functional equations derived directly from the differential equations. We apply these methods to two simple metabolic models.
116 citations
TL;DR: The combination of Parrondo's games can be therefore considered as a discrete-time Brownian ratchet, and the parameter space in which the paradoxical effect occurs is found and the winning rate analysis is carried out.
Abstract: Parrondo’s games present an apparently paradoxical situation where individually losing games can be combined to win. In this article we analyze the case of two coin tossing games. Game B is played with two biased coins and has state-dependent rules based on the player’s current capital. Game B can exhibit detailed balance or even negative drift (i.e., loss), depending on the chosen parameters. Game A is played with a single biased coin that produces a loss or negative drift in capital. However, a winning expectation is achieved by randomly mixing A and B. One possible interpretation pictures game A as a source of “noise” that is rectified by game B to produce overall positive drift—as in a Brownian ratchet. Game B has a state-dependent rule that favors a losing coin, but when this state dependence is broken up by the noise introduced by game A, a winning coin is favored. In this article we find the parameter space in which the paradoxical effect occurs and carry out a winning rate analysis. The significance of Parrondo’s games is that they are physically motivated and were originally derived by considering a Brownian ratchet—the combination of the games can be therefore considered as a discrete-time Brownian ratchet. We postulate the use of games of this type as a toy model for a number of physical and biological processes and raise a number of open questions for future research.
TL;DR: A graphical method for mapping bioregulatory networks is presented that is suited for the representation of multimolecular complexes, protein modifications, as well as actions at cell membranes and between protein domains.
Abstract: A graphical method for mapping bioregulatory networks is presented that is suited for the representation of multimolecular complexes, protein modifications, as well as actions at cell membranes and between protein domains. The symbol conventions defined for these molecular interaction maps are designed to accommodate multiprotein assemblies and protein modifications that can generate combinatorially large numbers of molecular species. Diagrams can either be “heuristic,” meaning that detailed knowledge of all possible reaction paths is not required, or “explicit,” meaning that the diagrams are totally unambiguous and suitable for simulation. Interaction maps are linked to annotation lists and indexes that provide ready access to pertinent data and references, and that allow any molecular species to be easily located. Illustrative interaction maps are included on the domain interactions of Src, transcription control of E2F-regulated genes, and signaling from receptor tyrosine kinase through phosphoinositides to Akt/PKB. A simple method of going from an explicit interaction diagram to an input file for a simulation program is outlined, in which the differential equations need not be written out. The role of interaction maps in selecting and defining systems for modeling is discussed.
TL;DR: Finite, scale-free networks, characterized by a wide distribution in the number of inputs per element, may provide a source of order in living cells, and are shown to be more ordered than either the Poisson or delta function networks below the critical point.
Abstract: formulations of the regulation of gene expression as random Boolean switching networks have been studied extensively over the past three decades. These models have been developed to make statistical predictions of the types of dynamics observed in biological networks based on network topology and interaction bias, p. For values of mean connectivity chosen to correspond to real biological networks, these models predict disordered dynamics. However, chaotic dynamics seems to be absent from the functioning of a normal cell. While these models use a fixed number of inputs for each element in the network, recent experimental evidence suggests that several biological networks have distributions in connectivity. We therefore study randomly constructed Boolean networks with distributions in the number of inputs, K, to each element. We study three distributions: delta function, Poisson, and power law (scale free). We analytically show that the critical value of the interaction bias parameter, p, above which steady state behavior is observed, is independent of the distribution in the limit of the number of elements N→∞. We also study these networks numerically. Using three different measures (types of attractors, fraction of elements that are active, and length of period), we show that finite, scale-free networks are more ordered than either the Poisson or delta function networks below the critical point. Thus the topology of scale-free biochemical networks, characterized by a wide distribution in the number of inputs per element, may provide a source of order in living cells.
TL;DR: It is proposed that the kinetic properties of this feedback loop have evolved to support bistability and flexibility in going between bistable and monostable modes, while simultaneously being very refractory to oscillatory states.
Abstract: Biological signaling networks comprised of cellular components including signaling proteins and small molecule messengers control the many cell function in responses to various extracellular and intracellular signals including hormone and neurotransmitter inputs, and genetic events. Many signaling pathways have motifs familiar to electronics and control theory design. Feedback loops are among the most common of these. Using experimentally derived parameters, we modeled a positive feedback loop in signaling pathways used by growth factors to trigger cell proliferation. This feedback loop is bistable under physiological conditions, although the system can move to a monostable state as well. We find that bistability persists under a wide range of regulatory conditions, even when core enzymes in the feedback loop deviate from physiological values. We did not observe any other phenomena in the core feedback loop, but the addition of a delayed inhibitory feedback was able to generate oscillations under rather extreme parameter conditions. Such oscillations may not be of physiological relevance. We propose that the kinetic properties of this feedback loop have evolved to support bistability and flexibility in going between bistable and monostable modes, while simultaneously being very refractory to oscillatory states.
TL;DR: The case of nonautonomous complex oscillations in a model for circadian oscillations subjected to periodic forcing by a light-dark cycle is considered and it is shown that the occurrence of entrainment versus chaos in these conditions markedly depends on the wave form of periodic forcing.
Abstract: We present an overview of mechanisms responsible for simple or complex oscillatory behavior in metabolic and genetic control networks. Besides simple periodic behavior corresponding to the evolution toward a limit cycle we consider complex modes of oscillatory behavior such as complex periodic oscillations of the bursting type and chaos. Multiple attractors are also discussed, e.g., the coexistence between a stable steady state and a stable limit cycle (hard excitation), or the coexistence between two simultaneously stable limit cycles (birhythmicity). We discuss mechanisms responsible for the transition from simple to complex oscillatory behavior by means of a number of models serving as selected examples. The models were originally proposed to account for simple periodic oscillations observed experimentally at the cellular level in a variety of biological systems. In a second stage, these models were modified to allow for complex oscillatory phenomena such as bursting, birhythmicity, or chaos. We consid...
TL;DR: This work analyzes a class of ordinary differential equations representing a simplified model of a genetic network and defines bifurcations of chaotic dynamics of the differential equation that correspond to changes in the associated language.
Abstract: We analyze a class of ordinary differential equations representing a simplified model of a genetic network In this network, the model genes control the production rates of other genes by a logical function The dynamics in these equations are represented by a directed graph on an n-dimensional hypercube (n-cube) in which each edge is directed in a unique orientation The vertices of the n-cube correspond to orthants of state space, and the edges correspond to boundaries between adjacent orthants The dynamics in these equations can be represented symbolically Starting from a point on the boundary between neighboring orthants, the equation is integrated until the boundary is crossed for a second time Each different cycle, corresponding to a different sequence of orthants that are traversed during the integration of the equation always starting on a boundary and ending the first time that same boundary is reached, generates a different letter of the alphabet A word consists of a sequence of letters corresponding to a possible sequence of orthants that arise from integration of the equation starting and ending on the same boundary The union of the words defines the language Letters and words correspond to analytically computable Poincare maps of the equation This formalism allows us to define bifurcations of chaotic dynamics of the differential equation that correspond to changes in the associated language Qualitative knowledge about the dynamics found by integrating the equation can be used to help solve the inverse problem of determining the underlying network generating the dynamics This work places the study of dynamics in genetic networks in a context comprising both nonlinear dynamics and the theory of computation (c) 2001 American Institute of Physics
TL;DR: Two new improved methods for the analysis of time series of concentrations, entropy metric construction (EMC), and entropy reduction method (ERM) are presented and illustrated with calculations on a model reaction system.
Abstract: We discuss the deduction of reaction pathways in complex chemical systems from measurements of time series of chemical concentrations of reacting species. First we review a technique called correlation metric construction (CMC) and show the construction of a reaction pathway from measurements on a part of glycolysis. Then we present two new improved methods for the analysis of time series of concentrations, entropy metric construction (EMC), and entropy reduction method (ERM), and illustrate (EMC) with calculations on a model reaction system.
TL;DR: It is hypothesized that the periodic stimulation modifies a parameter affecting the oscillation in a time dependent way and thereby induces a Hopf bifurcation, so that the stable oscillations are destabilized and HF-DBS acts via the gradual change of a network property.
Abstract: The mechanism by which chronic, high frequency, electrical deep brain stimulation (HF-DBS) suppresses tremor in Parkinson’s disease is unknown. Rest tremor in subjects with Parkinson’s disease receiving HF-DBS was recorded continuously throughout switching the deep brain stimulator on (at an effective frequency) and off. These data suggest that the stimulation induces a qualitative change in the dynamics, called a Hopf bifurcation, so that the stable oscillations are destabilized. We hypothesize that the periodic stimulation modifies a parameter affecting the oscillation in a time dependent way and thereby induces a Hopf bifurcation. We explore this hypothesis using a schematic network model of an oscillator interacting with periodic stimulation. The mechanism of time-dependent change of a control parameter in the model captures two aspects of the dynamics observed in the data: (1) a gradual increase in tremor amplitude when the stimulation is switched off and a gradual decrease in tremor amplitude when the stimulation is switched on and (2) a time delay in the onset and offset of the oscillations. This mechanism is consistent with these rest tremor transition data and with the idea that HF–DBS acts via the gradual change of a network property.
TL;DR: A control algorithm is introduced to direct the scaling factor onto a desired value from the Lyapunov stability theory, which allows us to arbitrarily amplify or reduce the scale of the response of the slave system via a feedback control on the master system.
Abstract: Scaling factor characterizes the synchronized dynamics of projective synchronization in partially linear chaotic systems but it is difficult to be estimated. To manipulate projective synchronization of chaotic systems in a favored way, a control algorithm is introduced to direct the scaling factor onto a desired value. The control approach is derived from the Lyapunov stability theory. It allows us to arbitrarily amplify or reduce the scale of the response of the slave system via a feedback control on the master system. In numerical experiments, we illustrate the application to the Lorenz system.
TL;DR: A mathematical model of the pressure and flow regulation in a pair of adjacent nephrons is developed and it is shown that this model can reproduce the different types of experimentally observed synchronization.
Abstract: Experimental data for tubular pressure oscillations in rat kidneys are analyzed in order to examine the different types of synchronization that can arise between neighboring functional units. For rats with normal blood pressure, the individual unit (the nephron) typically exhibits regular oscillations in its tubular pressure and flow variations. For such rats, both in-phase and antiphase synchronization can be demonstrated in the experimental data. For spontaneously hypertensive rats, where the pressure variations in the individual nephrons are highly irregular, signs of chaotic phase and frequency synchronization can be observed. Accounting for a hemodynamic as well as for a vascular coupling between nephrons that share a common interlobular artery, we develop a mathematical model of the pressure and flow regulation in a pair of adjacent nephrons. We show that this model, for appropriate values of the parameters, can reproduce the different types of experimentally observed synchronization.
TL;DR: The fact that junction-fold points are structurally stable and the limiting structures persist gives a rigorous but dynamical explanation as to why basic food chain dynamics can be chaotic.
Abstract: Consideration is given to a basic food chain model satisfying the trophic time diversification hypothesis which translates the model into a singularly perturbed system of three time scales. It is demonstrated that in some realistic system parameter region, the model has a unimodal or logistic-like Poincare return map when the singular parameter for the fastest variable is at the limiting value 0. It is also demonstrated that the unimodal map goes through a sequence of period-doubling bifurcations to chaos. The mechanism for the creation of the unimodal criticality is due to the existence of a junction-fold point [B. Deng, J. Math. Biol. 38, 21–78 (1999)]. The fact that junction-fold points are structurally stable and the limiting structures persist gives us a rigorous but dynamical explanation as to why basic food chain dynamics can be chaotic.
TL;DR: It is shown that the transport is anomalous and that its properties can be linked to the ergodic properties of continued fractions, and the distribution function of displacements and its truncated moments as a function of time exhibit log-periodic oscillations (modulations) with a universal period T(log)=pi(2)/12 ln 2.
Abstract: We consider chaotic properties of a particle in a square billiard with a horizontal bar in the middle. Such a system can model field-line windings of the merged surfaces. The system has weak-mixing properties with zero Lyapunov exponent and entropy, and it can be also interesting as an example of a system with intermediate chaotic properties, between the integrability and strong mixing. We show that the transport is anomalous and that its properties can be linked to the ergodic properties of continued fractions. The distribution of Poincare recurrences, distribution of the displacements, and the moments of the truncated distribution of the displacements are obtained. Connections between different exponents are found. It is shown that the distribution function of displacements and its truncated moments as a function of time exhibit log-periodic oscillations (modulations) with a universal period Tlog=π2/12 ln 2. We note that similar results are valid for a family of billiard, particularly for billiards with...
TL;DR: Evidence is provided for the existence of chaotic dynamics in a neurophysiologically plausible continuum theory of electrocortical activity and it is shown that the set of parameter values supporting chaos within parameter space has positive measure and exhibits fat fractal scaling.
Abstract: Various techniques designed to extract nonlinear characteristics from experimental time series have provided no clear evidence as to whether the electroencephalogram (EEG) is chaotic. Compounding the lack of firm experimental evidence is the paucity of physiologically plausible theories of EEG that are capable of supporting nonlinear and chaotic dynamics. Here we provide evidence for the existence of chaotic dynamics in a neurophysiologically plausible continuum theory of electrocortical activity and show that the set of parameter values supporting chaos within parameter space has positive measure and exhibits fat fractal scaling.
TL;DR: It is established that these algorithms converge when applied to any quadratic with distinct roots, namely Cauchy's method, the super-Newton method, and Halley's method.
Abstract: We study numerically and dynamically three cubically convergent iterative root-finding algorithms, namely Cauchy’s method, the super-Newton method, and Halley’s method. Using the concept of a universal Julia set (motivated by the results of McMullen), we establish that these algorithms converge when applied to any quadratic with distinct roots. We give examples showing the existence of attracting periodic orbits not associated to a root for the super-Newton method and Halley’s method applied to cubic polynomials. We include computer plots showing the dynamic structure for each algorithm applied to a variety of polynomials.
TL;DR: A reaction-diffusion model that describes in detail the cascade of molecular events during blood coagulation in a reduced form that contains three equations in three variables, two of which are self-accelerated.
Abstract: We propose a reaction-diffusion model that describes in detail the cascade of molecular events during blood coagulation. In a reduced form, this model contains three equations in three variables, two of which are self-accelerated. One of these variables, an activator, behaves in a threshold manner. An inhibitor is also produced autocatalytically, but there is no inhibitor threshold, because it is generated only in the presence of the activator. All model variables are set to have equal diffusion coefficients. The model has a stable stationary trivial state, which is spatially uniform and an excitation threshold. A pulse of excitation runs from the point where the excitation threshold has been exceeded. The regime of its propagation depends on the model parameters. In a one-dimensional problem, the pulse either stops running at a certain distance from the excitation point, or it reaches the boundaries as an autowave. However, there is a parameter range where the pulse does not disappear after stopping and exists stationarily. The resulting steady-state profiles of the model variables are symmetrical relative to the center of the structure formed.
TL;DR: In this article, an explicit expression for the escape probability of an overdamped Brownian particle is presented and shown to be in quantitative agreement with the simulations, and experiments are performed on a particle optically trapped in a double-well potential.
Abstract: We discuss activated escape from a metastable state of a system driven by a time-periodic force. We show that the escape probabilities can be changed very strongly even by a comparatively weak force. In a broad parameter range, the activation energy of escape depends linearly on the force amplitude. This dependence is described by the logarithmic susceptibility, which is analyzed theoretically and through analog and digital simulations. A closed-form explicit expression for the escape rate of an overdamped Brownian particle is presented and shown to be in quantitative agreement with the simulations. We also describe experiments on a Brownian particle optically trapped in a double-well potential. A suitable periodic modulation of the optical intensity breaks the spatio-temporal symmetry of an otherwise spatially symmetric system. This has allowed us to localize a particle in one of the symmetric wells.
TL;DR: In the Beeler-Reuter model, modified to produce stable spiral waves in two dimensions, it is found that anisotropy can destabilize a vortex filament in a parallelepipedal slab of tissue, but in the Luo-Rudy model, the role of an isotropy does not destabilize filaments.
Abstract: The role of cardiac tissue anisotropy in the breakup of vortex filaments is studied using two detailed cardiac models. In the Beeler-Reuter model, modified to produce stable spiral waves in two dimensions, we find that anisotropy can destabilize a vortex filament in a parallelepipedal slab of tissue. The mechanisms of the instability are similar to the ones reported in previous work on a simplified cardiac model by Fenton and Karma [Chaos 8, 20 (1998)]. In the Luo-Rudy model, also modified to produce stable spiral waves in two dimensions, we find that anisotropy does not destabilize filaments. A possible explanation for this model-dependent behavior based on spiral tip trajectories is offered. (c) 2001 American Institute of Physics.
TL;DR: The analogy of Parrondo's games is used to design a second order switched mode circuit which is unstable in either mode but is stable when switched, and suggests that it is possible to implement a control algorithm that actively uses noise, although too much noise eventually makes the system unusable.
Abstract: In this paper we use the analogy of Parrondo’s games to design a second order switched mode circuit which is unstable in either mode but is stable when switched. We do not require any sophisticated control law. The circuit is stable, even if it is switched at random. We use a stochastic form of Lyapunov’s second method to prove that the randomly switched system is stable with probability of one. Simulations show that the solution to the randomly switched system is very similar to the analytic solution for the time-averaged system. This is consistent with the standard techniques for switched state-space systems with periodic switching. We perform state-space simulations of our system, with a randomized discrete-time switching policy. We also examine the case where the control variable, the loop gain, is a continuous Gaussian random variable. This gives rise to a matrix stochastic differential equation (SDE). We know that, for a one-dimensional SDE, the difference between solution for the time averaged system and any given sample path for the SDE will be an appropriately scaled and conditioned version of Brownian motion. The simulations show that this is approximately true for the matrix SDE. We examine some numerical solutions to the matrix SDE in the time and frequency domains, for the case where the noise power is very small. We also perform some simulations, without analysis, for the same system with large amounts of noise. In this case, the solution is significantly shifted away from the solution for the time-averaged system. The Brownian motion terms dominate all other aspects of the solution. This gives rise to very erratic and “bursty” behavior. The stored energy in the system takes the form a logarithmic random walk. The simulations of our curious circuit suggest that it is possible to implement a control algorithm that actively uses noise, although too much noise eventually makes the system unusable.
TL;DR: This paper presents an overview of the capabilities of the Virtual Cell, and, for the first time, the detailed mathematical formulation used as the basis for spatial computations.
Abstract: The Virtual Cell is a modeling tool that allows biologists and theorists alike to specify and simulate cell-biophysical models on arbitrarily complex geometries. The framework combines an intuitive, front-end graphical user interface that runs in a web browser, sophisticated server-side numerical algorithms, a database for storage of models and simulation results, and flexible visualization capabilities. In this paper, we present an overview of the capabilities of the Virtual Cell, and, for the first time, the detailed mathematical formulation used as the basis for spatial computations. We also present summaries of two rather typical modeling projects, in order to illustrate the principal capabilities of the Virtual Cell.
TL;DR: This Focus Issue brings together a collection of articles that highlight some of the emerging hot unsolved noise problems to point the way for future research.
Abstract: Noise and fluctuations are at the seat of all physical phenomena. It is well known that, in linear systems, noise plays a destructive role. However, an emerging paradigm for nonlinear systems is that noise can play a constructive role—in some cases information transfer can be optimized at nonzero noise levels. Another use of noise is that its measured characteristics can tell us useful information about the system itself. Problems associated with fluctuations have been studied since 1826 and this Focus Issue brings together a collection of articles that highlight some of the emerging hot unsolved noise problems to point the way for future research.